# coding=utf-8
# Copyright 2022 Apple Inc. 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.
""" PyTorch MobileNetV1 model."""


from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_v1 import MobileNetV1Config


logger = logging.get_logger(__name__)


# General docstring
_CONFIG_FOR_DOC = "MobileNetV1Config"

# Base docstring
_CHECKPOINT_FOR_DOC = "google/mobilenet_v1_1.0_224"
_EXPECTED_OUTPUT_SHAPE = [1, 1024, 7, 7]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "google/mobilenet_v1_1.0_224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


from ..deprecated._archive_maps import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST  # noqa: F401, E402


def _build_tf_to_pytorch_map(model, config, tf_weights=None):
    """
    A map of modules from TF to PyTorch.
    """

    tf_to_pt_map = {}

    if isinstance(model, MobileNetV1ForImageClassification):
        backbone = model.mobilenet_v1
    else:
        backbone = model

    prefix = "MobilenetV1/Conv2d_0/"
    tf_to_pt_map[prefix + "weights"] = backbone.conv_stem.convolution.weight
    tf_to_pt_map[prefix + "BatchNorm/beta"] = backbone.conv_stem.normalization.bias
    tf_to_pt_map[prefix + "BatchNorm/gamma"] = backbone.conv_stem.normalization.weight
    tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.normalization.running_mean
    tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.normalization.running_var

    for i in range(13):
        tf_index = i + 1
        pt_index = i * 2

        pointer = backbone.layer[pt_index]
        prefix = f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
        tf_to_pt_map[prefix + "depthwise_weights"] = pointer.convolution.weight
        tf_to_pt_map[prefix + "BatchNorm/beta"] = pointer.normalization.bias
        tf_to_pt_map[prefix + "BatchNorm/gamma"] = pointer.normalization.weight
        tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.normalization.running_mean
        tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.normalization.running_var

        pointer = backbone.layer[pt_index + 1]
        prefix = f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
        tf_to_pt_map[prefix + "weights"] = pointer.convolution.weight
        tf_to_pt_map[prefix + "BatchNorm/beta"] = pointer.normalization.bias
        tf_to_pt_map[prefix + "BatchNorm/gamma"] = pointer.normalization.weight
        tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.normalization.running_mean
        tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.normalization.running_var

    if isinstance(model, MobileNetV1ForImageClassification):
        prefix = "MobilenetV1/Logits/Conv2d_1c_1x1/"
        tf_to_pt_map[prefix + "weights"] = model.classifier.weight
        tf_to_pt_map[prefix + "biases"] = model.classifier.bias

    return tf_to_pt_map


def load_tf_weights_in_mobilenet_v1(model, config, tf_checkpoint_path):
    """Load TensorFlow checkpoints in a PyTorch model."""
    try:
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise

    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_checkpoint_path)
    tf_weights = {}
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_checkpoint_path, name)
        tf_weights[name] = array

    # Build TF to PyTorch weights loading map
    tf_to_pt_map = _build_tf_to_pytorch_map(model, config, tf_weights)

    for name, pointer in tf_to_pt_map.items():
        logger.info(f"Importing {name}")
        if name not in tf_weights:
            logger.info(f"{name} not in tf pre-trained weights, skipping")
            continue

        array = tf_weights[name]

        if "depthwise_weights" in name:
            logger.info("Transposing depthwise")
            array = np.transpose(array, (2, 3, 0, 1))
        elif "weights" in name:
            logger.info("Transposing")
            if len(pointer.shape) == 2:  # copying into linear layer
                array = array.squeeze().transpose()
            else:
                array = np.transpose(array, (3, 2, 0, 1))

        if pointer.shape != array.shape:
            raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")

        logger.info(f"Initialize PyTorch weight {name} {array.shape}")
        pointer.data = torch.from_numpy(array)

        tf_weights.pop(name, None)
        tf_weights.pop(name + "/RMSProp", None)
        tf_weights.pop(name + "/RMSProp_1", None)
        tf_weights.pop(name + "/ExponentialMovingAverage", None)

    logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}")
    return model


def apply_tf_padding(features: torch.Tensor, conv_layer: nn.Conv2d) -> torch.Tensor:
    """
    Apply TensorFlow-style "SAME" padding to a convolution layer. See the notes at:
    https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2
    """
    in_height, in_width = features.shape[-2:]
    stride_height, stride_width = conv_layer.stride
    kernel_height, kernel_width = conv_layer.kernel_size

    if in_height % stride_height == 0:
        pad_along_height = max(kernel_height - stride_height, 0)
    else:
        pad_along_height = max(kernel_height - (in_height % stride_height), 0)

    if in_width % stride_width == 0:
        pad_along_width = max(kernel_width - stride_width, 0)
    else:
        pad_along_width = max(kernel_width - (in_width % stride_width), 0)

    pad_left = pad_along_width // 2
    pad_right = pad_along_width - pad_left
    pad_top = pad_along_height // 2
    pad_bottom = pad_along_height - pad_top

    padding = (pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(features, padding, "constant", 0.0)


class MobileNetV1ConvLayer(nn.Module):
    def __init__(
        self,
        config: MobileNetV1Config,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: Optional[int] = 1,
        groups: Optional[int] = 1,
        bias: bool = False,
        use_normalization: Optional[bool] = True,
        use_activation: Optional[bool or str] = True,
    ) -> None:
        super().__init__()
        self.config = config

        if in_channels % groups != 0:
            raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.")
        if out_channels % groups != 0:
            raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.")

        padding = 0 if config.tf_padding else int((kernel_size - 1) / 2)

        self.convolution = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            groups=groups,
            bias=bias,
            padding_mode="zeros",
        )

        if use_normalization:
            self.normalization = nn.BatchNorm2d(
                num_features=out_channels,
                eps=config.layer_norm_eps,
                momentum=0.9997,
                affine=True,
                track_running_stats=True,
            )
        else:
            self.normalization = None

        if use_activation:
            if isinstance(use_activation, str):
                self.activation = ACT2FN[use_activation]
            elif isinstance(config.hidden_act, str):
                self.activation = ACT2FN[config.hidden_act]
            else:
                self.activation = config.hidden_act
        else:
            self.activation = None

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        if self.config.tf_padding:
            features = apply_tf_padding(features, self.convolution)
        features = self.convolution(features)
        if self.normalization is not None:
            features = self.normalization(features)
        if self.activation is not None:
            features = self.activation(features)
        return features


class MobileNetV1PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = MobileNetV1Config
    load_tf_weights = load_tf_weights_in_mobilenet_v1
    base_model_prefix = "mobilenet_v1"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = False
    _no_split_modules = []

    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None:
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.BatchNorm2d):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


MOBILENET_V1_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

MOBILENET_V1_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.",
    MOBILENET_V1_START_DOCSTRING,
)
class MobileNetV1Model(MobileNetV1PreTrainedModel):
    def __init__(self, config: MobileNetV1Config, add_pooling_layer: bool = True):
        super().__init__(config)
        self.config = config

        depth = 32
        out_channels = max(int(depth * config.depth_multiplier), config.min_depth)

        self.conv_stem = MobileNetV1ConvLayer(
            config,
            in_channels=config.num_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=2,
        )

        strides = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

        self.layer = nn.ModuleList()
        for i in range(13):
            in_channels = out_channels

            if strides[i] == 2 or i == 0:
                depth *= 2
                out_channels = max(int(depth * config.depth_multiplier), config.min_depth)

            self.layer.append(
                MobileNetV1ConvLayer(
                    config,
                    in_channels=in_channels,
                    out_channels=in_channels,
                    kernel_size=3,
                    stride=strides[i],
                    groups=in_channels,
                )
            )

            self.layer.append(
                MobileNetV1ConvLayer(
                    config,
                    in_channels=in_channels,
                    out_channels=out_channels,
                    kernel_size=1,
                )
            )

        self.pooler = nn.AdaptiveAvgPool2d((1, 1)) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()

    def _prune_heads(self, heads_to_prune):
        raise NotImplementedError

    @add_start_docstrings_to_model_forward(MOBILENET_V1_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPoolingAndNoAttention,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
        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.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        hidden_states = self.conv_stem(pixel_values)

        all_hidden_states = () if output_hidden_states else None

        for i, layer_module in enumerate(self.layer):
            hidden_states = layer_module(hidden_states)

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        last_hidden_state = hidden_states

        if self.pooler is not None:
            pooled_output = torch.flatten(self.pooler(last_hidden_state), start_dim=1)
        else:
            pooled_output = None

        if not return_dict:
            return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=all_hidden_states,
        )


@add_start_docstrings(
    """
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """,
    MOBILENET_V1_START_DOCSTRING,
)
class MobileNetV1ForImageClassification(MobileNetV1PreTrainedModel):
    def __init__(self, config: MobileNetV1Config) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.mobilenet_v1 = MobileNetV1Model(config)

        last_hidden_size = self.mobilenet_v1.layer[-1].convolution.out_channels

        # Classifier head
        self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True)
        self.classifier = nn.Linear(last_hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()

        # Initialize weights and apply final processing
        self.post_init()

    @add_start_docstrings_to_model_forward(MOBILENET_V1_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=ImageClassifierOutputWithNoAttention,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def forward(
        self,
        pixel_values: Optional[torch.Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.mobilenet_v1(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)

        pooled_output = outputs.pooler_output if return_dict else outputs[1]

        logits = self.classifier(self.dropout(pooled_output))

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
        )
