# coding=utf-8
# Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax Wav2Vec2 model. """

from functools import partial
from typing import Optional, Tuple, Union

import numpy as np

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from flax.linen.attention import dot_product_attention_weights
from jax import lax

from ...file_utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel
from ...utils import logging
from .configuration_wav2vec2 import Wav2Vec2Config


logger = logging.get_logger(__name__)


@flax.struct.dataclass
class FlaxWav2Vec2BaseModelOutput(ModelOutput):
    """
    Output type of :class:`~transformers.FlaxWav2Vec2BaseModelOutput`, with potential hidden states and attentions.

    Args:
        last_hidden_state (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        extract_features (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, last_conv_dim)`):
            Sequence of extracted feature vectors of the last convolutional layer of the model with ``last_conv_dim``
            being the dimension of the last convolutional layer.
        hidden_states (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of
            shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`jnp.ndarray` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    last_hidden_state: jnp.ndarray = None
    extract_features: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


@flax.struct.dataclass
class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput):
    """
    Output type of :class:`~transformers.FlaxWav2Vec2ForPreTrainingOutput`, with potential hidden states and
    attentions.

    Args:
        loss (`optional`, returned when model is in train mode, ``jnp.ndarray`` of shape :obj:`(1,)`):
            Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the `official
            paper <https://arxiv.org/pdf/2006.11477.pdf>`__ . (classification) loss.
        projected_states (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, config.proj_codevector_dim)`):
            Hidden-states of the model projected to `config.proj_codevector_dim` that can be used to predict the masked
            projected quantized states.
        projected_quantized_states (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length, config.proj_codevector_dim)`):
            Quantized extracted feature vectors projected to `config.proj_codevector_dim` representing the positive
            target vectors for contrastive loss.
        hidden_states (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
            Tuple of :obj:`jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of
            shape :obj:`(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (:obj:`tuple(jnp.ndarray)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
            Tuple of :obj:`jnp.ndarray` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    projected_states: jnp.ndarray = None
    projected_quantized_states: jnp.ndarray = None
    codevector_perplexity: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


def _compute_mask_indices(
    shape: Tuple[int, int],
    mask_prob: float,
    mask_length: int,
    min_masks: int = 0,
) -> np.ndarray:
    """
    Computes random mask spans for a given shape. Used to implement `SpecAugment: A Simple Data Augmentation Method for
    ASR <https://arxiv.org/abs/1904.08779>`__. Note that this method is not optimized to run on TPU and should be run
    on CPU as part of the preprocessing during training.

    Args:
        shape: the the shape for which to compute masks.
            should be of size 2 where first element is batch size and 2nd is timesteps
        mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
            number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
            however due to overlaps, the actual number will be smaller (unless no_overlap is True)
        mask_length: size of the mask
        min_masks: minimum number of masked spans

    """
    batch_size, sequence_length = shape

    if mask_length < 1:
        raise ValueError("`mask_length` has to be bigger than 0.")

    if mask_length > sequence_length:
        raise ValueError(
            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
        )

    # compute number of masked spans in batch
    num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item())
    num_masked_spans = max(num_masked_spans, min_masks)

    # make sure num masked indices <= sequence_length
    if num_masked_spans * mask_length > sequence_length:
        num_masked_spans = sequence_length // mask_length

    # SpecAugment mask to fill
    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=np.bool)

    # get random indices to mask
    spec_aug_mask_idxs = np.array(
        [
            np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False)
            for _ in range(batch_size)
        ]
    )

    # expand masked indices to masked spans
    spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length))
    spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length)

    offsets = np.arange(mask_length)[None, None, :]
    offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape(
        batch_size, num_masked_spans * mask_length
    )
    spec_aug_mask_idxs = spec_aug_mask_idxs + offsets

    # scatter indices to mask
    np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1)

    return spec_aug_mask


def _sample_negative_indices(features_shape: Tuple, num_negatives: int):
    """
    Sample `num_negatives` vectors from feature vectors.
    """
    batch_size, sequence_length, hidden_size = features_shape
    if sequence_length <= 1:
        raise ValueError(
            f"`features should have `sequence_length` > 1, but are of shape "
            f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})."
        )

    # get `num_negatives` random vector indices from the same utterance
    sampled_negative_indices = np.random.randint(
        low=0,
        high=sequence_length - 1,
        size=(batch_size, num_negatives * sequence_length),
    )

    # generate indices of the positive vectors themselves, repeat them `num_negatives` times
    feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten()

    # avoid sampling the same positive vector, but keep the distribution uniform
    sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1

    # correct for batch size
    for batch_idx in range(1, batch_size):
        sampled_negative_indices[batch_idx] += batch_idx * sequence_length

    return sampled_negative_indices


WAV_2_VEC_2_START_DOCSTRING = r"""
    Wav2Vec2 was proposed in `wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
    <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.

    This model inherits from :class:`~transformers.FlaxPreTrainedModel`. Check the superclass documentation for the
    generic methods the library implements for all its model (such as downloading or saving, resizing the input
    embeddings, pruning heads etc.)

    This model is also a Flax Linen `flax.nn.Module
    <https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html>`__ subclass. Use it as a regular Flax
    Module and refer to the Flax documentation for all matter related to general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - `Just-In-Time (JIT) compilation <https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit>`__
    - `Automatic Differentiation <https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation>`__
    - `Vectorization <https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap>`__
    - `Parallelization <https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap>`__

    Parameters:
        config (:class:`~transformers.Wav2Vec2Config`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the :meth:`~transformers.FlaxPreTrainedModel.from_pretrained` method to load the
            model weights.
"""


WAV_2_VEC_2_INPUTS_DOCSTRING = r"""
    Args:
        input_values (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`):
            Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file
            into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via the soundfile library (`pip install
            soundfile`). To prepare the array into `input_values`, the :class:`~transformers.Wav2Vec2Processor` should
            be used for padding and conversion into a tensor of type `jnp.ndarray`. See
            :meth:`transformers.Wav2Vec2Processor.__call__` for details.
        attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Mask to avoid performing convolution and attention on padding token indices. Mask values selected in ``[0,
            1]``:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            `What are attention masks? <../glossary.html#attention-mask>`__ .. warning:: :obj:`attention_mask` should
            only be passed if the corresponding processor has ``config.return_attention_mask == True``. For all models
            whose processor has ``config.return_attention_mask == False``, such as `wav2vec2-base
            <https://huggingface.co/facebook/wav2vec2-base-960h>`__, :obj:`attention_mask` should **not** be passed to
            avoid degraded performance when doing batched inference. For such models :obj:`input_values` should simply
            be padded with 0 and passed without :obj:`attention_mask`. Be aware that these models also yield slightly
            different results depending on whether :obj:`input_values` is padded or not.
        mask_time_indices (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict
            masked extracted features in `config.proj_codevector_dim` space.
        output_attentions (:obj:`bool`, `optional`):
            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
            tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""


class FlaxWav2Vec2LayerNormConvLayer(nn.Module):
    config: Wav2Vec2Config
    layer_id: int = 0
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1
        self.out_conv_dim = self.config.conv_dim[self.layer_id]

        self.conv = nn.Conv(
            features=self.config.conv_dim[self.layer_id],
            kernel_size=self.config.conv_kernel[self.layer_id],
            strides=(self.config.conv_stride[self.layer_id],),
            use_bias=self.config.conv_bias,
            kernel_init=jax.nn.initializers.he_normal(dtype=self.dtype),
            padding="VALID",
            dtype=self.dtype,
        )
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.activation = ACT2FN[self.config.feat_extract_activation]

    def __call__(self, hidden_states):
        hidden_states = self.conv(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


class FlaxConvWithWeightNorm(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = nn.Conv(
            features=self.config.hidden_size,
            kernel_size=self.config.num_conv_pos_embeddings,
            kernel_init=jax.nn.initializers.he_normal(dtype=self.dtype),
            padding="VALID",
            feature_group_count=self.config.num_conv_pos_embedding_groups,
            dtype=self.dtype,
        )
        weight_shape = (
            self.conv.features,
            self.conv.features // self.conv.feature_group_count,
            self.conv.kernel_size,
        )
        self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(dtype=self.dtype), weight_shape)
        self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :])
        self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,))
        self.prev_padding = self.conv.kernel_size // 2

    def _get_normed_weights(self):
        weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]
        normed_weight_v = jnp.divide(self.weight_v, weight_v_norm)
        normed_kernel = jnp.multiply(normed_weight_v, self.weight_g)
        return normed_kernel

    def __call__(self, hidden_states):
        kernel = self._get_normed_weights()
        hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0)))
        hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states)
        return hidden_states


class FlaxWav2Vec2PositionalConvEmbedding(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype)
        self.activation = ACT2FN[self.config.feat_extract_activation]
        self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0

    def __call__(self, hidden_states):
        hidden_states = hidden_states.transpose((0, 1, 2))

        hidden_states = self.conv(hidden_states)

        if self.num_pad_remove > 0:
            hidden_states = hidden_states[:, : -self.num_pad_remove, :]
        hidden_states = self.activation(hidden_states)

        hidden_states = hidden_states.transpose((0, 1, 2))
        return hidden_states


class FlaxConvLayersCollection(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        if self.config.feat_extract_norm == "layer":
            self.layers = [
                FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_feat_extract_layers)
            ]
        elif self.config.feat_extract_norm == "group":
            raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported")
        else:
            raise ValueError(
                f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group', 'layer']"
            )

    def __call__(self, hidden_states):
        for i, conv_layer in enumerate(self.layers):
            hidden_states = conv_layer(hidden_states)
        return hidden_states


class FlaxWav2Vec2FeatureExtractor(nn.Module):
    """Construct the featurs from raw audio waveform"""

    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype)

    def __call__(self, input_values):
        hidden_states = input_values[:, :, None]
        hidden_states = self.conv_layers(hidden_states)
        return hidden_states


class FlaxWav2Vec2FeatureProjection(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.projection = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout)

    def __call__(self, hidden_states, deterministic=True):
        norm_hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.projection(norm_hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        return hidden_states, norm_hidden_states


class FlaxWav2Vec2Attention(nn.Module):
    config: Wav2Vec2Config
    embed_dim: int
    num_heads: int
    dropout: float = 0.0
    bias: bool = True
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self) -> None:
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
            )

        dense = partial(
            nn.Dense,
            self.embed_dim,
            use_bias=self.bias,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
        )

        self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
        self.out_proj = dense()

        self.dropout_layer = nn.Dropout(rate=self.dropout)

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))

    def __call__(
        self,
        hidden_states: jnp.ndarray,
        key_value_states: Optional[jnp.ndarray] = None,
        attention_mask: Optional[jnp.ndarray] = None,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        """Input shape: Batch x Time x Channel"""

        # get query proj
        query_states = self.q_proj(hidden_states)

        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        if attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.dropout > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.dropout,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights


class FlaxWav2Vec2FeedForward(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout)

        self.intermediate_dense = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
            dtype=self.dtype,
        )
        if isinstance(self.config.hidden_act, str):
            self.intermediate_act_fn = ACT2FN[self.config.hidden_act]
        else:
            self.intermediate_act_fn = self.config.hidden_act

        self.output_dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
            dtype=self.dtype,
        )
        self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout)

    def __call__(self, hidden_states, deterministic=True):
        hidden_states = self.intermediate_dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic)

        hidden_states = self.output_dense(hidden_states)
        hidden_states = self.output_dropout(hidden_states, deterministic=deterministic)
        return hidden_states


class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.attention = FlaxWav2Vec2Attention(
            config=self.config,
            embed_dim=self.config.hidden_size,
            num_heads=self.config.num_attention_heads,
            dropout=self.config.attention_dropout,
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout)
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype)
        self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False):
        attn_residual = hidden_states
        hidden_states = self.layer_norm(hidden_states)
        hidden_states, attn_weights = self.attention(
            hidden_states, attention_mask=attention_mask, deterministic=deterministic
        )
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = attn_residual + hidden_states
        hidden_states = hidden_states + self.feed_forward(
            self.final_layer_norm(hidden_states), deterministic=deterministic
        )

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.layers = [
            FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype)
            for i in range(self.config.num_hidden_layers)
        ]

    def __call__(
        self,
        hidden_states,
        attention_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = layer(
                hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions += (layer_outputs[1],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states,)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


class FlaxWav2Vec2StableLayerNormEncoder(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype)
        self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout)
        self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask=None,
        deterministic=True,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=True,
    ):

        if attention_mask is not None:
            # make sure padded tokens are not attended to
            hidden_states = jnp.where(
                jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0
            )

        position_embeddings = self.pos_conv_embed(hidden_states)

        hidden_states = hidden_states + position_embeddings
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)

        outputs = self.layers(
            hidden_states,
            attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = self.layer_norm(outputs[0])

        if not return_dict:
            return (hidden_states,) + outputs[1:]

        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )


class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module):
    """
    Vector quantization using gumbel softmax. See `CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX
    <https://arxiv.org/pdf/1611.01144.pdf>`__ for more information.
    """

    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.num_groups = self.config.num_codevector_groups
        self.num_vars = self.config.num_codevectors_per_group

        if self.config.codevector_dim % self.num_groups != 0:
            raise ValueError(
                f"`config.codevector_dim {self.config.codevector_dim} must be divisible by"
                f" `config.num_codevector_groups` {self.num_groups} for concatenation"
            )

        # storage for codebook variables (codewords)
        self.codevectors = self.param(
            "codevectors",
            jax.nn.initializers.uniform(),
            (1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups),
        )
        self.weight_proj = nn.Dense(
            self.num_groups * self.num_vars,
            kernel_init=jax.nn.initializers.normal(1.0, self.dtype),
            dtype=self.dtype,
        )

    @staticmethod
    def _compute_perplexity(probs, mask=None):
        if mask is not None:
            mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape)
            probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs))
            marginal_probs = probs.sum(axis=0) / mask.sum()
        else:
            marginal_probs = probs.mean(axis=0)

        perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum()
        return perplexity

    def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1):
        batch_size, sequence_length, hidden_size = hidden_states.shape

        # project to codevector dim
        hidden_states = self.weight_proj(hidden_states)
        hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1)

        if not deterministic:
            # sample code vector probs via gumbel in differentiateable way
            gumbel_rng = self.make_rng("gumbel")
            gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape)
            codevector_probs = nn.softmax((hidden_states + gumbels) / temperature)

            # compute perplexity
            codevector_soft_dist = nn.softmax(
                hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1
            )
            perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices)
        else:
            # take argmax in non-differentiable way
            # comptute hard codevector distribution (one hot)
            codevector_idx = hidden_states.argmax(axis=-1)
            codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0
            codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1)
            perplexity = self._compute_perplexity(codevector_probs, mask_time_indices)

        codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1)
        # use probs to retrieve codevectors
        codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors
        codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1)
        codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1)

        return codevectors, perplexity


class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = Wav2Vec2Config
    base_model_prefix: str = "wav2vec2"
    module_class: nn.Module = None

    def __init__(
        self,
        config: Wav2Vec2Config,
        input_shape: Tuple = (1, 1024),
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
        # init input tensors
        input_values = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_values)
        params_rng, dropout_rng = jax.random.split(rng, 2)
        rngs = {"params": params_rng, "dropout": dropout_rng}

        return self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"]

    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
    def __call__(
        self,
        input_values,
        attention_mask=None,
        mask_time_indices=None,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        batch_size, sequence_length = input_values.shape

        if attention_mask is None:
            attention_mask = jnp.ones((batch_size, sequence_length))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        return self.module.apply(
            inputs,
            jnp.array(input_values, dtype="f4"),
            jnp.array(attention_mask, dtype="i4"),
            mask_time_indices,
            not train,
            output_attentions,
            output_hidden_states,
            return_dict,
            rngs=rngs,
        )

    def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]):
        return self.module._get_feat_extract_output_lengths(input_lengths)


class FlaxWav2Vec2Module(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.feature_extractor = FlaxWav2Vec2FeatureExtractor(self.config, dtype=self.dtype)
        self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype)
        self.masked_spec_embed = self.param(
            "masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,)
        )

        if self.config.do_stable_layer_norm:
            self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype)
        else:
            raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.")

    def __call__(
        self,
        input_values,
        attention_mask=None,
        mask_time_indices=None,
        deterministic=True,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        """

        Returns:

        Example::

            >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2Model
            >>> from datasets import load_dataset
            >>> import soundfile as sf

            >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
            >>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")

            >>> def map_to_array(batch):
            >>>     speech, _ = sf.read(batch["file"])
            >>>     batch["speech"] = speech
            >>>     return batch

            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
            >>> ds = ds.map(map_to_array)

            >>> input_values = processor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1
            >>> hidden_states = model(input_values).last_hidden_state

        """
        extract_features = self.feature_extractor(input_values)

        if attention_mask is not None:
            # compute real output lengths according to convolution formula
            output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1).astype("i4"))

            attention_mask = jnp.zeros(extract_features.shape[:2], dtype=self.dtype)

            # these two operations makes sure that all values
            # before the output lengths indices are attended to
            attention_mask = jax.ops.index_update(
                attention_mask, jax.ops.index[jnp.arange(attention_mask.shape[0]), output_lengths - 1], 1
            )
            attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool")

        hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic)
        if mask_time_indices is not None:  # apply SpecAugment along time axis with given indices
            hidden_states = jnp.where(
                jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape),
                jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape),
                hidden_states,
            )

        encoder_outputs = self.encoder(
            hidden_states,
            attention_mask=attention_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = encoder_outputs[0]

        if not return_dict:
            return (hidden_states, extract_features) + encoder_outputs[1:]

        return FlaxWav2Vec2BaseModelOutput(
            last_hidden_state=hidden_states,
            extract_features=extract_features,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

    def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]):
        """
        Computes the output length of the convolutional layers
        """

        def _conv_out_length(input_length, kernel_size, stride):
            # 1D convolutional layer output length formula taken
            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
            return (input_length - kernel_size) // stride + 1

        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

        return input_lengths


@add_start_docstrings(
    "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.",
    WAV_2_VEC_2_START_DOCSTRING,
)
class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel):
    module_class = FlaxWav2Vec2Module


class FlaxWav2Vec2ForCTCModule(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.final_dropout)
        self.lm_head = nn.Dense(
            self.config.vocab_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
            dtype=self.dtype,
        )

    def __call__(
        self,
        input_values,
        attention_mask=None,
        mask_time_indices=None,
        deterministic=True,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Returns:

        Example::

            >>> import jax.numpy as jnp
            >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2ForCTC
            >>> from datasets import load_dataset
            >>> import soundfile as sf

            >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
            >>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

            >>> def map_to_array(batch):
            >>>     speech, _ = sf.read(batch["file"])
            >>>     batch["speech"] = speech
            >>>     return batch

            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
            >>> ds = ds.map(map_to_array)

            >>> input_values = processor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1
            >>> logits = model(input_values).logits
            >>> predicted_ids = jnp.argmax(logits, axis=-1)

            >>> transcription = processor.decode(predicted_ids[0])
            >>> # should give:  "A MAN SAID TO THE UNIVERSE SIR I EXIST"

        """

        outputs = self.wav2vec2(
            input_values,
            attention_mask=attention_mask,
            mask_time_indices=mask_time_indices,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)

        logits = self.lm_head(hidden_states)

        if not return_dict:
            return (logits,) + outputs[2:]

        return FlaxCausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)

    def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]):
        """
        Computes the output length of the convolutional layers
        """

        def _conv_out_length(input_length, kernel_size, stride):
            # 1D convolutional layer output length formula taken
            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
            return (input_length - kernel_size) // stride + 1

        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

        return input_lengths


@add_start_docstrings(
    "Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).",
    WAV_2_VEC_2_START_DOCSTRING,
)
class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel):
    module_class = FlaxWav2Vec2ForCTCModule


class FlaxWav2Vec2ForPreTrainingModule(nn.Module):
    config: Wav2Vec2Config
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype)
        self.dropout_features = nn.Dropout(self.config.feat_quantizer_dropout)

        self.quantizer = FlaxWav2Vec2GumbelVectorQuantizer(self.config, dtype=self.dtype)
        self.project_q = nn.Dense(
            self.config.proj_codevector_dim,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
            dtype=self.dtype,
        )
        self.project_hid = nn.Dense(
            self.config.proj_codevector_dim,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range, self.dtype),
            dtype=self.dtype,
        )

    def __call__(
        self,
        input_values,
        attention_mask=None,
        mask_time_indices=None,
        gumbel_temperature: int = 1,
        deterministic: bool = True,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Returns:

        Example::

            >>> import optax
            >>> import numpy as np
            >>> import jax.numpy as jnp
            >>> from transformers import Wav2Vec2FeatureExtractor, FlaxWav2Vec2ForPreTraining
            >>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
            >>> from datasets import load_dataset
            >>> import soundfile as sf

            >>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("patrickvonplaten/wav2vec2-base")
            >>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("patrickvonplaten/wav2vec2-base")


            >>> def map_to_array(batch):
            ...     speech, _ = sf.read(batch["file"])
            ...     batch["speech"] = speech
            ...     return batch


            >>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
            >>> ds = ds.map(map_to_array)

            >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1

            >>> # compute masked indices
            >>> batch_size, raw_sequence_length = input_values.shape
            >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
            >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)

            >>> outputs = model(input_values, mask_time_indices=mask_time_indices)

            >>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states)
            >>> cosine_sim = optax.cosine_similarity(
            ...     outputs.projected_states, outputs.projected_quantized_states, axis=-1
            ... )

            >>> # show that cosine similarity is much higher than random
            >>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5

        """

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.wav2vec2(
            input_values,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            mask_time_indices=mask_time_indices,
            deterministic=deterministic,
            return_dict=return_dict,
        )

        # project all transformed features (including masked) to final vq dim
        transformer_features = self.project_hid(outputs[0])

        # quantize all (unmasked) extracted features and project to final vq dim
        extract_features = self.dropout_features(outputs[1], deterministic=deterministic)
        quantized_features, codevector_perplexity = self.quantizer(
            extract_features, mask_time_indices, deterministic=deterministic, temperature=gumbel_temperature
        )
        quantized_features = self.project_q(quantized_features)

        if not return_dict:
            return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:]

        return FlaxWav2Vec2ForPreTrainingOutput(
            projected_states=transformer_features,
            projected_quantized_states=quantized_features,
            codevector_perplexity=codevector_perplexity,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def _get_feat_extract_output_lengths(self, input_lengths: Union[jnp.ndarray, int]):
        """
        Computes the output length of the convolutional layers
        """

        def _conv_out_length(input_length, kernel_size, stride):
            # 1D convolutional layer output length formula taken
            # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html
            return (input_length - kernel_size) // stride + 1

        for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride):
            input_lengths = _conv_out_length(input_lengths, kernel_size, stride)

        return input_lengths


@add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top. """, WAV_2_VEC_2_START_DOCSTRING)
class FlaxWav2Vec2ForPreTraining(FlaxWav2Vec2PreTrainedModel):
    module_class = FlaxWav2Vec2ForPreTrainingModule

    @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING)
    # overwrite since has `gumbel_temperature` input
    def __call__(
        self,
        input_values,
        attention_mask=None,
        mask_time_indices=None,
        gumbel_temperature: int = 1,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        gumbel_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        batch_size, sequence_length = input_values.shape

        if attention_mask is None:
            attention_mask = jnp.ones((batch_size, sequence_length))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        if gumbel_rng is not None:
            rngs["gumbel"] = gumbel_rng

        inputs = {"params": params or self.params}

        return self.module.apply(
            inputs,
            jnp.array(input_values, dtype="f4"),
            jnp.array(attention_mask, dtype="i4"),
            mask_time_indices,
            gumbel_temperature,
            not train,
            output_attentions,
            output_hidden_states,
            return_dict,
            rngs=rngs,
        )
