# coding=utf-8
# Copyright 2021 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.
""" Testing suite for the PyTorch Speech2Text model. """

import copy
import inspect
import os
import pickle
import tempfile
import unittest

from transformers import Speech2TextConfig
from transformers.testing_utils import (
    is_torch_available,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    require_torchaudio,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_fx_available

from ...generation.test_generation_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor


if is_torch_available():
    import torch

    from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
    from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder

if is_torch_fx_available():
    from transformers.utils.fx import symbolic_trace


def prepare_speech_to_text_inputs_dict(
    config,
    input_features,
    decoder_input_ids,
    attention_mask=None,
    decoder_attention_mask=None,
    head_mask=None,
    decoder_head_mask=None,
    cross_attn_head_mask=None,
):
    if attention_mask is None:
        attention_mask = input_features.ne(0)
    if decoder_attention_mask is None:
        decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
    if head_mask is None:
        head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
    if decoder_head_mask is None:
        decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
    if cross_attn_head_mask is None:
        cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
    return {
        # "input_ids": input_features,
        "input_features": input_features,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }


@require_torch
class Speech2TextModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=True,
        use_labels=False,
        vocab_size=99,
        hidden_size=16,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=4,
        num_conv_layers=2,
        conv_kernel_sizes=(5, 5),
        conv_channels=32,
        input_feat_per_channel=24,
        input_channels=1,
        hidden_act="relu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=20,
        max_source_positions=20,
        max_target_positions=20,
        eos_token_id=2,
        pad_token_id=1,
        bos_token_id=0,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.num_conv_layers = num_conv_layers
        self.conv_kernel_sizes = conv_kernel_sizes
        self.conv_channels = conv_channels
        self.input_feat_per_channel = input_feat_per_channel
        self.input_channels = input_channels
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.max_source_positions = max_source_positions
        self.max_target_positions = max_target_positions
        self.eos_token_id = eos_token_id
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id

    def prepare_config_and_inputs(self):
        input_features = floats_tensor(
            [self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
        )
        attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
        decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)

        config = self.get_config()
        inputs_dict = prepare_speech_to_text_inputs_dict(
            config,
            input_features=input_features,
            decoder_input_ids=decoder_input_ids,
            attention_mask=attention_mask,
        )
        return config, inputs_dict

    def get_config(self):
        return Speech2TextConfig(
            vocab_size=self.vocab_size,
            d_model=self.hidden_size,
            encoder_layers=self.num_hidden_layers,
            decoder_layers=self.num_hidden_layers,
            encoder_attention_heads=self.num_attention_heads,
            decoder_attention_heads=self.num_attention_heads,
            encoder_ffn_dim=self.intermediate_size,
            decoder_ffn_dim=self.intermediate_size,
            num_conv_layers=self.num_conv_layers,
            conv_kernel_sizes=self.conv_kernel_sizes,
            conv_channels=self.conv_channels,
            input_feat_per_channel=self.input_feat_per_channel,
            input_channels=self.input_channels,
            dropout=self.hidden_dropout_prob,
            attention_dropout=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            max_source_positions=self.max_source_positions,
            max_target_positions=self.max_target_positions,
            eos_token_id=self.eos_token_id,
            bos_token_id=self.bos_token_id,
            pad_token_id=self.pad_token_id,
        )

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict

    def get_subsampled_output_lengths(self, input_lengths):
        """
        Computes the output length of the convolutional layers
        """

        for i in range(self.num_conv_layers):
            input_lengths = (input_lengths - 1) // 2 + 1

        return input_lengths

    def create_and_check_model_forward(self, config, inputs_dict):
        model = Speech2TextModel(config=config).to(torch_device).eval()

        input_features = inputs_dict["input_features"]
        decoder_input_ids = inputs_dict["decoder_input_ids"]

        # first forward pass
        last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state

        self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))

    def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
        model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval()
        input_ids = inputs_dict["decoder_input_ids"]
        attention_mask = inputs_dict["decoder_attention_mask"]

        # first forward pass
        outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)

        output, past_key_values = outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
        next_attn_mask = ids_tensor((self.batch_size, 3), 2)

        # append to next input_ids and
        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
        next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)

        output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
        output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
            "last_hidden_state"
        ]

        # select random slice
        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))

    def check_encoder_decoder_model_standalone(self, config, inputs_dict):
        model = Speech2TextModel(config=config).to(torch_device).eval()
        outputs = model(**inputs_dict)

        encoder_last_hidden_state = outputs.encoder_last_hidden_state
        last_hidden_state = outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
            encoder = model.get_encoder()
            encoder.save_pretrained(tmpdirname)
            encoder = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device)

        encoder_last_hidden_state_2 = encoder(
            inputs_dict["input_features"], attention_mask=inputs_dict["attention_mask"]
        )[0]

        self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)

        with tempfile.TemporaryDirectory() as tmpdirname:
            decoder = model.get_decoder()
            decoder.save_pretrained(tmpdirname)
            decoder = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device)

        encoder_attention_mask = encoder._get_feature_vector_attention_mask(
            encoder_last_hidden_state.shape[1], inputs_dict["attention_mask"]
        )

        last_hidden_state_2 = decoder(
            input_ids=inputs_dict["decoder_input_ids"],
            attention_mask=inputs_dict["decoder_attention_mask"],
            encoder_hidden_states=encoder_last_hidden_state,
            encoder_attention_mask=encoder_attention_mask,
        )[0]

        self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)


@require_torch
class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
    all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else ()
    all_generative_model_classes = (Speech2TextForConditionalGeneration,) if is_torch_available() else ()
    is_encoder_decoder = True
    fx_compatible = True
    test_pruning = False
    test_missing_keys = False

    input_name = "input_features"

    def setUp(self):
        self.model_tester = Speech2TextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
        self.maxDiff = 3000

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_save_load_strict(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
            self.assertEqual(info["missing_keys"], [])

    def test_model_forward(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_forward(*config_and_inputs)

    def test_decoder_model_past_with_large_inputs(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)

    def test_encoder_decoder_model_standalone(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)

    # not implemented currently
    def test_inputs_embeds(self):
        pass

    # training is not supported yet
    def test_training(self):
        pass

    def test_training_gradient_checkpointing(self):
        pass

    def test_generate_fp16(self):
        config, input_dict = self.model_tester.prepare_config_and_inputs()
        input_features = input_dict["input_features"]
        attention_mask = input_dict["attention_mask"]
        model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
        if torch_device == "cuda":
            input_features = input_features.half()
            model.half()
        model.generate(input_features, attention_mask=attention_mask)
        model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            expected_arg_names = [
                "input_features",
                "attention_mask",
                "decoder_input_ids",
                "decoder_attention_mask",
            ]
            expected_arg_names.extend(
                ["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
                if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
                else ["encoder_outputs"]
            )
            self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

    def test_hidden_states_output(self):
        def check_hidden_states_output(inputs_dict, config, model_class):
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )
            self.assertEqual(len(hidden_states), expected_num_layers)

            if hasattr(self.model_tester, "encoder_seq_length"):
                seq_length = self.model_tester.encoder_seq_length
            else:
                seq_length = self.model_tester.seq_length

            subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]),
                [subsampled_seq_length, self.model_tester.hidden_size],
            )

            if config.is_encoder_decoder:
                hidden_states = outputs.decoder_hidden_states

                self.assertIsInstance(hidden_states, (list, tuple))
                self.assertEqual(len(hidden_states), expected_num_layers)
                seq_len = getattr(self.model_tester, "seq_length", None)
                decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)

                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [decoder_seq_length, self.model_tester.hidden_size],
                )

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(inputs_dict, config, model_class)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True

            check_hidden_states_output(inputs_dict, config, model_class)

    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True

        seq_len = getattr(self.model_tester, "seq_length", None)
        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
        decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = False
            config.return_dict = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()

            subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
            subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)

            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)

            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
            )
            out_len = len(outputs)

            correct_outlen = 5

            # loss is at first position
            if "labels" in inputs_dict:
                correct_outlen += 1  # loss is added to beginning
            if "past_key_values" in outputs:
                correct_outlen += 1  # past_key_values have been returned

            self.assertEqual(out_len, correct_outlen)

            # decoder attentions
            decoder_attentions = outputs.decoder_attentions
            self.assertIsInstance(decoder_attentions, (list, tuple))
            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
            )

            # cross attentions
            cross_attentions = outputs.cross_attentions
            self.assertIsInstance(cross_attentions, (list, tuple))
            self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(cross_attentions[0].shape[-3:]),
                [
                    self.model_tester.num_attention_heads,
                    decoder_seq_length,
                    subsampled_encoder_key_length,
                ],
            )

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            inputs_dict["output_hidden_states"] = True
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            with torch.no_grad():
                outputs = model(**self._prepare_for_class(inputs_dict, model_class))

            added_hidden_states = 2
            self.assertEqual(out_len + added_hidden_states, len(outputs))

            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(self_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
            )

    def test_resize_tokens_embeddings(self):
        (
            original_config,
            inputs_dict,
        ) = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.test_resize_embeddings:
            return

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config)
            model.to(torch_device)

            if self.model_tester.is_training is False:
                model.eval()

            model_vocab_size = config.vocab_size
            # Retrieve the embeddings and clone theme
            model_embed = model.resize_token_embeddings(model_vocab_size)
            cloned_embeddings = model_embed.weight.clone()

            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
            model_embed = model.resize_token_embeddings(model_vocab_size + 10)
            self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
            # Check that it actually resizes the embeddings matrix
            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
            model_embed = model.resize_token_embeddings(model_vocab_size - 15)
            self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
            # Check that it actually resizes the embeddings matrix
            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)

            # make sure that decoder_input_ids are resized
            if "decoder_input_ids" in inputs_dict:
                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
            model(**self._prepare_for_class(inputs_dict, model_class))

            # Check that adding and removing tokens has not modified the first part of the embedding matrix.
            models_equal = True
            for p1, p2 in zip(cloned_embeddings, model_embed.weight):
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

    def test_resize_embeddings_untied(self):
        (
            original_config,
            inputs_dict,
        ) = self.model_tester.prepare_config_and_inputs_for_common()
        if not self.test_resize_embeddings:
            return

        original_config.tie_word_embeddings = False

        # if model cannot untied embeddings -> leave test
        if original_config.tie_word_embeddings:
            return

        for model_class in self.all_model_classes:
            config = copy.deepcopy(original_config)
            model = model_class(config).to(torch_device)

            # if no output embeddings -> leave test
            if model.get_output_embeddings() is None:
                continue

            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
            model_vocab_size = config.vocab_size
            model.resize_token_embeddings(model_vocab_size + 10)
            self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
            output_embeds = model.get_output_embeddings()
            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
            # Check bias if present
            if output_embeds.bias is not None:
                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
            model.resize_token_embeddings(model_vocab_size - 15)
            self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
            # Check that it actually resizes the embeddings matrix
            output_embeds = model.get_output_embeddings()
            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
            # Check bias if present
            if output_embeds.bias is not None:
                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            if "decoder_input_ids" in inputs_dict:
                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
            # Check that the model can still do a forward pass successfully (every parameter should be resized)
            model(**self._prepare_for_class(inputs_dict, model_class))

    def test_generate_without_input_ids(self):
        pass

    @staticmethod
    def _get_encoder_outputs(
        model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
    ):
        encoder = model.get_encoder()
        encoder_outputs = encoder(
            input_ids,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )
        encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
            num_interleave, dim=0
        )
        input_ids = input_ids[:, :, 0]
        input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + model._get_decoder_start_token_id()
        attention_mask = None
        return encoder_outputs, input_ids, attention_mask

    def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
        batch_size, seq_length = input_ids.shape[:2]
        subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
        num_sequences_in_output = batch_size * num_return_sequences
        gen_len = (
            output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
        )

        # scores
        self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)

        # Attentions
        # encoder
        self._check_encoder_attention_for_generate(
            output.encoder_attentions, batch_size, config, subsampled_seq_length
        )
        # decoder
        self._check_attentions_for_generate(
            num_sequences_in_output,
            output.decoder_attentions,
            min_length=1,
            max_length=output.sequences.shape[-1],
            config=config,
            use_cache=use_cache,
        )

        # Hidden States
        # encoder
        self._check_encoder_hidden_states_for_generate(
            output.encoder_hidden_states, batch_size, config, subsampled_seq_length
        )

        # decoder
        self._check_hidden_states_for_generate(
            num_sequences_in_output,
            output.decoder_hidden_states,
            min_length=1,
            max_length=output.sequences.shape[-1],
            config=config,
            use_cache=use_cache,
        )

    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            return

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class)

            try:
                model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
                input_features = inputs["input_features"]
                attention_mask = inputs["attention_mask"]
                decoder_input_ids = inputs["decoder_input_ids"]
                decoder_attention_mask = inputs["decoder_attention_mask"]
                traced_model = torch.jit.trace(
                    model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
                )
            except RuntimeError:
                self.fail("Couldn't trace module.")

            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")

                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")

                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")

            model.to(torch_device)
            model.eval()

            loaded_model.to(torch_device)
            loaded_model.eval()

            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()

            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))

            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False

            self.assertTrue(models_equal)

    def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
        if not is_torch_fx_available() or not self.fx_compatible:
            return

        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.return_dict = False

        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)

            try:
                if model.config.is_encoder_decoder:
                    model.config.use_cache = False  # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
                    labels = inputs.get("labels", None)
                    input_names = [
                        "input_ids",
                        "attention_mask",
                        "decoder_input_ids",
                        "decoder_attention_mask",
                        "input_features",
                    ]
                    if labels is not None:
                        input_names.append("labels")

                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = list(filtered_inputs.keys())

                    model_output = model(**filtered_inputs)

                    traced_model = symbolic_trace(model, input_names)
                    traced_output = traced_model(**filtered_inputs)
                else:
                    input_names = ["input_ids", "attention_mask", "token_type_ids", "pixel_values", "input_features"]

                    labels = inputs.get("labels", None)
                    start_positions = inputs.get("start_positions", None)
                    end_positions = inputs.get("end_positions", None)
                    if labels is not None:
                        input_names.append("labels")
                    if start_positions is not None:
                        input_names.append("start_positions")
                    if end_positions is not None:
                        input_names.append("end_positions")

                    filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                    input_names = list(filtered_inputs.keys())

                    model_output = model(**filtered_inputs)

                    traced_model = symbolic_trace(model, input_names)
                    traced_output = traced_model(**filtered_inputs)

            except RuntimeError as e:
                self.fail(f"Couldn't trace module: {e}")

            def flatten_output(output):
                flatten = []
                for x in output:
                    if isinstance(x, (tuple, list)):
                        flatten += flatten_output(x)
                    elif not isinstance(x, torch.Tensor):
                        continue
                    else:
                        flatten.append(x)
                return flatten

            model_output = flatten_output(model_output)
            traced_output = flatten_output(traced_output)
            num_outputs = len(model_output)

            for i in range(num_outputs):
                self.assertTrue(
                    torch.allclose(model_output[i], traced_output[i]),
                    f"traced {i}th output doesn't match model {i}th output for {model_class}",
                )

            # Test that the model can be serialized and restored properly
            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
                try:
                    with open(pkl_file_name, "wb") as f:
                        pickle.dump(traced_model, f)
                    with open(pkl_file_name, "rb") as f:
                        loaded = pickle.load(f)
                except Exception as e:
                    self.fail(f"Couldn't serialize / deserialize the traced model: {e}")

                loaded_output = loaded(**filtered_inputs)
                loaded_output = flatten_output(loaded_output)

                for i in range(num_outputs):
                    self.assertTrue(
                        torch.allclose(model_output[i], loaded_output[i]),
                        f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
                    )


@require_torch
@require_torchaudio
@require_sentencepiece
@require_tokenizers
@slow
class Speech2TextModelIntegrationTests(unittest.TestCase):
    @cached_property
    def default_processor(self):
        return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")

    def _load_datasamples(self, num_samples):
        from datasets import load_dataset

        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        # automatic decoding with librispeech
        speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]

    def test_generation_librispeech(self):
        model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
        model.to(torch_device)
        processor = self.default_processor

        input_speech = self._load_datasamples(1)

        input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)

        generated_ids = model.generate(input_features)
        generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)

        EXPECTED_TRANSCRIPTIONS = [
            "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
        ]
        self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)

    def test_generation_librispeech_batched(self):
        model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
        model.to(torch_device)
        processor = self.default_processor

        input_speech = self._load_datasamples(4)

        inputs = processor(input_speech, return_tensors="pt", padding=True)

        input_features = inputs.input_features.to(torch_device)
        attention_mask = inputs.attention_mask.to(torch_device)

        generated_ids = model.generate(input_features, attention_mask=attention_mask)
        generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)

        EXPECTED_TRANSCRIPTIONS = [
            "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel",
            "nor is mister cultar's manner less interesting than his matter",
            "he tells us that at this festive season of the year with christmas and roast beef looming before us"
            " similes drawn from eating and its results occur most readily to the mind",
            "he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it"
            " but little of rocky ithaca",
        ]

        self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
