# What does this PR do?
### _Automatically converts input pixels to the correct type when running image_to_text pipelines_

---

Currently, when using image_to_text pipelines with half precision, I encounter an error in the forward function when passing in the pixel data. I found that casting to the target_dtype within the forward function fixes this issue in my case.

This is my first time working with the transformers library, so I'm not sure if this is the "correct" place to fix this kind of issue, or if perhaps there's another step in the pipeline code that I've missed that should be responsible for casting the input data to the correct type.

I assume there are other similar models that may benefit from the same type of fix. However, I've constrained my fix to the models that I've been working with already, as I didn't want to contribute unvalidated code. I'm happy to make similar changes to further models if required, or if someone with more experience with this library wants to rework these changes and approach the fix differently, I'd be happy with that, and can test with the subset of models I've been using if required.

- _Possibly_ Fixes #24834

_(This issue looked similar to the issue I was encountering, however I had different additional issues using 8bit optimizations, so I've only tested my fix under float16. But I think given that issue seems to relate to the same root cause of missing casting for input data within pipelines, I think there's a good chance it may be fixed by this issue.)_

PR Checklist Notes
- I haven't been able to run tests as I ran into dependency issues on my local environment, and the CI workflows appear to use self hosted runners, which I assume won't be easy for me to set up. Given the small scope of my changes, perhaps someone can approve running my PR against CircleCI if necessary?
- I haven't added new tests for this change, I had a read through of the existing tests and I wasn't sure if this sort of low level change would usually warrant explicit test coverage. Since I don't currently have a good way of running the tests myself, I figured it was best to omit new tests for now.

Tagging @younesbelkada and @NielsRogge as the last authors of the lines I edited.
fix: cast input pixels to appropriate dtype for image_to_text pipelines. # What does this PR do?
### _Automatically converts input pixels to the correct type when running image_to_text pipelines_

---

Currently, when using image_to_text pipelines with half precision, I encounter an error in the forward function when passing in the pixel data. I found that casting to the target_dtype within the forward function fixes this issue in my case.

This is my first time working with the transformers library, so I'm not sure if this is the "correct" place to fix this kind of issue, or if perhaps there's another step in the pipeline code that I've missed that should be responsible for casting the input data to the correct type.

I assume there are other similar models that may benefit from the same type of fix. However, I've constrained my fix to the models that I've been working with already, as I didn't want to contribute unvalidated code. I'm happy to make similar changes to further models if required, or if someone with more experience with this library wants to rework these changes and approach the fix differently, I'd be happy with that, and can test with the subset of models I've been using if required.

- _Possibly_ Fixes #24834

_(This issue looked similar to the issue I was encountering, however I had different additional issues using 8bit optimizations, so I've only tested my fix under float16. But I think given that issue seems to relate to the same root cause of missing casting for input data within pipelines, I think there's a good chance it may be fixed by this issue.)_

PR Checklist Notes
- I haven't been able to run tests as I ran into dependency issues on my local environment, and the CI workflows appear to use self hosted runners, which I assume won't be easy for me to set up. Given the small scope of my changes, perhaps someone can approve running my PR against CircleCI if necessary?
- I haven't added new tests for this change, I had a read through of the existing tests and I wasn't sure if this sort of low level change would usually warrant explicit test coverage. Since I don't currently have a good way of running the tests myself, I figured it was best to omit new tests for now.

Tagging @younesbelkada and @NielsRogge as the last authors of the lines I edited.
# 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 Hubert model. """


import math
import unittest

import pytest

from transformers import SEWConfig, is_torch_available
from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        Wav2Vec2FeatureExtractor,
        Wav2Vec2Processor,
    )
    from transformers.models.hubert.modeling_hubert import _compute_mask_indices


class SEWModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=1024,  # speech is longer
        is_training=False,
        hidden_size=32,
        feat_extract_norm="group",
        feat_extract_dropout=0.0,
        feat_extract_activation="gelu",
        conv_dim=(64, 32, 32),
        conv_stride=(5, 2, 1),
        conv_kernel=(10, 3, 1),
        conv_bias=False,
        num_conv_pos_embeddings=31,
        num_conv_pos_embedding_groups=2,
        squeeze_factor=2,
        num_hidden_layers=4,
        num_attention_heads=2,
        hidden_dropout=0.1,
        intermediate_size=20,
        layer_norm_eps=1e-5,
        hidden_act="gelu",
        initializer_range=0.02,
        vocab_size=32,
        do_stable_layer_norm=False,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.feat_extract_norm = feat_extract_norm
        self.feat_extract_dropout = feat_extract_dropout
        self.feat_extract_activation = feat_extract_activation
        self.conv_dim = conv_dim
        self.conv_stride = conv_stride
        self.conv_kernel = conv_kernel
        self.conv_bias = conv_bias
        self.num_conv_pos_embeddings = num_conv_pos_embeddings
        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
        self.squeeze_factor = squeeze_factor
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_dropout = hidden_dropout
        self.intermediate_size = intermediate_size
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.vocab_size = vocab_size
        self.do_stable_layer_norm = do_stable_layer_norm
        self.scope = scope

        output_seq_length = self.seq_length
        for kernel, stride in zip(self.conv_kernel, self.conv_stride):
            output_seq_length = (output_seq_length - (kernel - 1)) / stride
        self.output_seq_length = int(math.ceil(output_seq_length))
        self.encoder_seq_length = self.output_seq_length // self.squeeze_factor

    def prepare_config_and_inputs(self):
        input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
        attention_mask = random_attention_mask([self.batch_size, self.seq_length])

        config = self.get_config()

        return config, input_values, attention_mask

    def get_config(self):
        return SEWConfig(
            hidden_size=self.hidden_size,
            feat_extract_norm=self.feat_extract_norm,
            feat_extract_dropout=self.feat_extract_dropout,
            feat_extract_activation=self.feat_extract_activation,
            conv_dim=self.conv_dim,
            conv_stride=self.conv_stride,
            conv_kernel=self.conv_kernel,
            conv_bias=self.conv_bias,
            num_conv_pos_embeddings=self.num_conv_pos_embeddings,
            num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
            squeeze_factor=self.squeeze_factor,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            hidden_dropout=self.hidden_dropout,
            intermediate_size=self.intermediate_size,
            layer_norm_eps=self.layer_norm_eps,
            hidden_act=self.hidden_act,
            initializer_range=self.initializer_range,
            vocab_size=self.vocab_size,
        )

    def create_and_check_model(self, config, input_values, attention_mask):
        model = SEWModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_values, attention_mask=attention_mask)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
        )

    def create_and_check_batch_inference(self, config, input_values, *args):
        # test does not pass for models making use of `group_norm`
        # check: https://github.com/pytorch/fairseq/issues/3227
        model = SEWModel(config=config)
        model.to(torch_device)
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0.0

        batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state

        for i in range(input_values.shape[0]):
            input_slice = input_values[i : i + 1, : input_lengths[i]]
            output = model(input_slice).last_hidden_state

            batch_output = batch_outputs[i : i + 1, : output.shape[1]]
            self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))

    def check_ctc_loss(self, config, input_values, *args):
        model = SEWForCTC(config=config)
        model.to(torch_device)

        # make sure that dropout is disabled
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0

        model.config.ctc_loss_reduction = "sum"
        sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()

        model.config.ctc_loss_reduction = "mean"
        mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()

        self.parent.assertTrue(isinstance(sum_loss, float))
        self.parent.assertTrue(isinstance(mean_loss, float))

    def check_ctc_training(self, config, input_values, *args):
        config.ctc_zero_infinity = True
        model = SEWForCTC(config=config)
        model.to(torch_device)
        model.train()

        # freeze feature encoder
        model.freeze_feature_encoder()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0

            if max_length_labels[i] < labels.shape[-1]:
                # it's important that we make sure that target lenghts are at least
                # one shorter than logit lenghts to prevent -inf
                labels[i, max_length_labels[i] - 1 :] = -100

        loss = model(input_values, labels=labels).loss
        self.parent.assertFalse(torch.isinf(loss).item())

        loss.backward()

    def check_seq_classifier_loss(self, config, input_values, *args):
        model = SEWForSequenceClassification(config=config)
        model.to(torch_device)

        # make sure that dropout is disabled
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0

        masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
        unmasked_loss = model(input_values, labels=labels).loss.item()

        self.parent.assertTrue(isinstance(masked_loss, float))
        self.parent.assertTrue(isinstance(unmasked_loss, float))
        self.parent.assertTrue(masked_loss != unmasked_loss)

    def check_seq_classifier_training(self, config, input_values, *args):
        config.ctc_zero_infinity = True
        model = SEWForSequenceClassification(config=config)
        model.to(torch_device)
        model.train()

        # freeze everything but the classification head
        model.freeze_base_model()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0

        loss = model(input_values, labels=labels).loss
        self.parent.assertFalse(torch.isinf(loss).item())

        loss.backward()

    def check_labels_out_of_vocab(self, config, input_values, *args):
        model = SEWForCTC(config)
        model.to(torch_device)
        model.train()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)

        with pytest.raises(ValueError):
            model(input_values, labels=labels)

    def prepare_config_and_inputs_for_common(self):
        config, input_values, attention_mask = self.prepare_config_and_inputs()
        inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
        return config, inputs_dict


@require_torch
class SEWModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (SEWForCTC, SEWModel, SEWForSequenceClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "audio-classification": SEWForSequenceClassification,
            "automatic-speech-recognition": SEWForCTC,
            "feature-extraction": SEWModel,
        }
        if is_torch_available()
        else {}
    )
    test_pruning = False
    test_headmasking = False

    def setUp(self):
        self.model_tester = SEWModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SEWConfig, hidden_size=37)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_ctc_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_loss(*config_and_inputs)

    def test_ctc_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_training(*config_and_inputs)

    def test_labels_out_of_vocab(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_labels_out_of_vocab(*config_and_inputs)

    # Hubert has no inputs_embeds
    def test_inputs_embeds(self):
        pass

    # `input_ids` is renamed to `input_values`
    def test_forward_signature(self):
        pass

    # SEW cannot resize token embeddings
    # since it has no tokens embeddings
    def test_resize_tokens_embeddings(self):
        pass

    # SEW has no inputs_embeds
    # and thus the `get_input_embeddings` fn
    # is not implemented
    def test_model_common_attributes(self):
        pass

    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        # set layer drop to 0
        model.config.layerdrop = 0.0

        input_values = inputs_dict["input_values"]

        input_lengths = torch.tensor(
            [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
        )
        output_lengths = model._get_feat_extract_output_lengths(input_lengths)

        labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
        inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
        inputs_dict["labels"] = labels

        outputs = model(**inputs_dict)

        output = outputs[0]

        # Encoder-/Decoder-only models
        hidden_states = outputs.hidden_states[0]
        attentions = outputs.attentions[0]

        hidden_states.retain_grad()
        attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(hidden_states.grad)
        self.assertIsNotNone(attentions.grad)

    def test_seq_classifier_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_loss(*config_and_inputs)

    def test_seq_classifier_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_training(*config_and_inputs)

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                uniform_init_parms = [
                    "conv.weight",
                    "masked_spec_embed",
                    "quantizer.weight_proj.weight",
                ]
                if param.requires_grad:
                    if any(x in name for x in uniform_init_parms):
                        self.assertTrue(
                            -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "weight_g") and module.weight_g is not None:
            module.weight_g.data.fill_(3)
        if hasattr(module, "weight_v") and module.weight_v is not None:
            module.weight_v.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)
        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
            module.masked_spec_embed.data.fill_(3)

    @unittest.skip(reason="Feed forward chunking is not implemented")
    def test_feed_forward_chunking(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model = SEWModel.from_pretrained("asapp/sew-tiny-100k")
        self.assertIsNotNone(model)


@require_torch
class SEWUtilsTest(unittest.TestCase):
    def test_compute_mask_indices(self):
        batch_size = 4
        sequence_length = 60
        mask_prob = 0.5
        mask_length = 1

        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
        mask = torch.from_numpy(mask).to(torch_device)

        self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])

    def test_compute_mask_indices_overlap(self):
        batch_size = 4
        sequence_length = 80
        mask_prob = 0.5
        mask_length = 4

        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
        mask = torch.from_numpy(mask).to(torch_device)

        # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
        for batch_sum in mask.sum(axis=-1):
            self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)


@require_torch
@require_soundfile
@slow
class SEWModelIntegrationTest(unittest.TestCase):
    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").filter(
            lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
        )[:num_samples]["audio"]

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

    def test_inference_pretrained_batched(self):
        model = SEWModel.from_pretrained("asapp/sew-tiny-100k").to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("asapp/sew-tiny-100k")

        input_speech = self._load_datasamples(2)

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

        input_values = inputs.input_values.to(torch_device)

        with torch.no_grad():
            outputs = model(input_values).last_hidden_state

        # expected outputs taken from the original SEW implementation
        expected_outputs_first = torch.tensor(
            [
                [
                    [0.1509, 0.5372, 0.3061, -0.1694],
                    [-0.1700, 0.5764, 0.2753, -0.1299],
                    [0.1281, 0.7949, 0.2342, -0.1624],
                    [-0.1627, 0.6710, 0.2215, -0.1317],
                ],
                [
                    [0.0408, 1.4355, 0.8605, -0.0968],
                    [0.0393, 1.2368, 0.6826, 0.0364],
                    [-0.1269, 1.9215, 1.1677, -0.1297],
                    [-0.1654, 1.6524, 0.6877, -0.0196],
                ],
            ],
            device=torch_device,
        )
        expected_outputs_last = torch.tensor(
            [
                [
                    [1.3379, -0.1450, -0.1500, -0.0515],
                    [0.8364, -0.1680, -0.1248, -0.0689],
                    [1.2791, -0.1507, -0.1523, -0.0564],
                    [0.8208, -0.1690, -0.1199, -0.0751],
                ],
                [
                    [0.6959, -0.0861, -0.1235, -0.0861],
                    [0.4700, -0.1686, -0.1141, -0.1199],
                    [1.0776, -0.1137, -0.0124, -0.0472],
                    [0.5774, -0.1675, -0.0376, -0.0823],
                ],
            ],
            device=torch_device,
        )
        expected_output_sum = 62146.7422

        self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3))
        self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3))
        self.assertTrue(abs(outputs.sum() - expected_output_sum) < 5)

    def test_inference_ctc_batched(self):
        model = SEWForCTC.from_pretrained("asapp/sew-tiny-100k-ft-ls100h").to(torch_device)
        processor = Wav2Vec2Processor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h", do_lower_case=True)

        input_speech = self._load_datasamples(2)

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

        input_values = inputs.input_values.to(torch_device)

        with torch.no_grad():
            logits = model(input_values).logits

        predicted_ids = torch.argmax(logits, dim=-1)
        predicted_trans = processor.batch_decode(predicted_ids)

        EXPECTED_TRANSCRIPTIONS = [
            "a man said to the universe sir i exist",
            "swet covered brian's body trickling into the tightloine closs hat was the only garment he wore",
        ]
        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)

# 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 Hubert model. """


import math
import unittest

import pytest

from transformers import SEWConfig, is_torch_available
from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        Wav2Vec2FeatureExtractor,
        Wav2Vec2Processor,
    )
    from transformers.models.hubert.modeling_hubert import _compute_mask_indices


class SEWModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=1024,  # speech is longer
        is_training=False,
        hidden_size=32,
        feat_extract_norm="group",
        feat_extract_dropout=0.0,
        feat_extract_activation="gelu",
        conv_dim=(64, 32, 32),
        conv_stride=(5, 2, 1),
        conv_kernel=(10, 3, 1),
        conv_bias=False,
        num_conv_pos_embeddings=31,
        num_conv_pos_embedding_groups=2,
        squeeze_factor=2,
        num_hidden_layers=4,
        num_attention_heads=2,
        hidden_dropout=0.1,
        intermediate_size=20,
        layer_norm_eps=1e-5,
        hidden_act="gelu",
        initializer_range=0.02,
        vocab_size=32,
        do_stable_layer_norm=False,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.feat_extract_norm = feat_extract_norm
        self.feat_extract_dropout = feat_extract_dropout
        self.feat_extract_activation = feat_extract_activation
        self.conv_dim = conv_dim
        self.conv_stride = conv_stride
        self.conv_kernel = conv_kernel
        self.conv_bias = conv_bias
        self.num_conv_pos_embeddings = num_conv_pos_embeddings
        self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
        self.squeeze_factor = squeeze_factor
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_dropout = hidden_dropout
        self.intermediate_size = intermediate_size
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.vocab_size = vocab_size
        self.do_stable_layer_norm = do_stable_layer_norm
        self.scope = scope

        output_seq_length = self.seq_length
        for kernel, stride in zip(self.conv_kernel, self.conv_stride):
            output_seq_length = (output_seq_length - (kernel - 1)) / stride
        self.output_seq_length = int(math.ceil(output_seq_length))
        self.encoder_seq_length = self.output_seq_length // self.squeeze_factor

    def prepare_config_and_inputs(self):
        input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
        attention_mask = random_attention_mask([self.batch_size, self.seq_length])

        config = self.get_config()

        return config, input_values, attention_mask

    def get_config(self):
        return SEWConfig(
            hidden_size=self.hidden_size,
            feat_extract_norm=self.feat_extract_norm,
            feat_extract_dropout=self.feat_extract_dropout,
            feat_extract_activation=self.feat_extract_activation,
            conv_dim=self.conv_dim,
            conv_stride=self.conv_stride,
            conv_kernel=self.conv_kernel,
            conv_bias=self.conv_bias,
            num_conv_pos_embeddings=self.num_conv_pos_embeddings,
            num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
            squeeze_factor=self.squeeze_factor,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            hidden_dropout=self.hidden_dropout,
            intermediate_size=self.intermediate_size,
            layer_norm_eps=self.layer_norm_eps,
            hidden_act=self.hidden_act,
            initializer_range=self.initializer_range,
            vocab_size=self.vocab_size,
        )

    def create_and_check_model(self, config, input_values, attention_mask):
        model = SEWModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_values, attention_mask=attention_mask)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
        )

    def create_and_check_batch_inference(self, config, input_values, *args):
        # test does not pass for models making use of `group_norm`
        # check: https://github.com/pytorch/fairseq/issues/3227
        model = SEWModel(config=config)
        model.to(torch_device)
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0.0

        batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state

        for i in range(input_values.shape[0]):
            input_slice = input_values[i : i + 1, : input_lengths[i]]
            output = model(input_slice).last_hidden_state

            batch_output = batch_outputs[i : i + 1, : output.shape[1]]
            self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))

    def check_ctc_loss(self, config, input_values, *args):
        model = SEWForCTC(config=config)
        model.to(torch_device)

        # make sure that dropout is disabled
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0

        model.config.ctc_loss_reduction = "sum"
        sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()

        model.config.ctc_loss_reduction = "mean"
        mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()

        self.parent.assertTrue(isinstance(sum_loss, float))
        self.parent.assertTrue(isinstance(mean_loss, float))

    def check_ctc_training(self, config, input_values, *args):
        config.ctc_zero_infinity = True
        model = SEWForCTC(config=config)
        model.to(torch_device)
        model.train()

        # freeze feature encoder
        model.freeze_feature_encoder()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0

            if max_length_labels[i] < labels.shape[-1]:
                # it's important that we make sure that target lenghts are at least
                # one shorter than logit lenghts to prevent -inf
                labels[i, max_length_labels[i] - 1 :] = -100

        loss = model(input_values, labels=labels).loss
        self.parent.assertFalse(torch.isinf(loss).item())

        loss.backward()

    def check_seq_classifier_loss(self, config, input_values, *args):
        model = SEWForSequenceClassification(config=config)
        model.to(torch_device)

        # make sure that dropout is disabled
        model.eval()

        input_values = input_values[:3]
        attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0
            attention_mask[i, input_lengths[i] :] = 0

        masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
        unmasked_loss = model(input_values, labels=labels).loss.item()

        self.parent.assertTrue(isinstance(masked_loss, float))
        self.parent.assertTrue(isinstance(unmasked_loss, float))
        self.parent.assertTrue(masked_loss != unmasked_loss)

    def check_seq_classifier_training(self, config, input_values, *args):
        config.ctc_zero_infinity = True
        model = SEWForSequenceClassification(config=config)
        model.to(torch_device)
        model.train()

        # freeze everything but the classification head
        model.freeze_base_model()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

        # pad input
        for i in range(len(input_lengths)):
            input_values[i, input_lengths[i] :] = 0.0

        loss = model(input_values, labels=labels).loss
        self.parent.assertFalse(torch.isinf(loss).item())

        loss.backward()

    def check_labels_out_of_vocab(self, config, input_values, *args):
        model = SEWForCTC(config)
        model.to(torch_device)
        model.train()

        input_values = input_values[:3]

        input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
        max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
        labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)

        with pytest.raises(ValueError):
            model(input_values, labels=labels)

    def prepare_config_and_inputs_for_common(self):
        config, input_values, attention_mask = self.prepare_config_and_inputs()
        inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
        return config, inputs_dict


@require_torch
class SEWModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (SEWForCTC, SEWModel, SEWForSequenceClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {
            "audio-classification": SEWForSequenceClassification,
            "automatic-speech-recognition": SEWForCTC,
            "feature-extraction": SEWModel,
        }
        if is_torch_available()
        else {}
    )
    test_pruning = False
    test_headmasking = False

    def setUp(self):
        self.model_tester = SEWModelTester(self)
        self.config_tester = ConfigTester(self, config_class=SEWConfig, hidden_size=37)

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

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_ctc_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_loss(*config_and_inputs)

    def test_ctc_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_ctc_training(*config_and_inputs)

    def test_labels_out_of_vocab(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_labels_out_of_vocab(*config_and_inputs)

    # Hubert has no inputs_embeds
    def test_inputs_embeds(self):
        pass

    # `input_ids` is renamed to `input_values`
    def test_forward_signature(self):
        pass

    # SEW cannot resize token embeddings
    # since it has no tokens embeddings
    def test_resize_tokens_embeddings(self):
        pass

    # SEW has no inputs_embeds
    # and thus the `get_input_embeddings` fn
    # is not implemented
    def test_model_common_attributes(self):
        pass

    def test_retain_grad_hidden_states_attentions(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.output_hidden_states = True
        config.output_attentions = True

        # no need to test all models as different heads yield the same functionality
        model_class = self.all_model_classes[0]
        model = model_class(config)
        model.to(torch_device)

        # set layer drop to 0
        model.config.layerdrop = 0.0

        input_values = inputs_dict["input_values"]

        input_lengths = torch.tensor(
            [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
        )
        output_lengths = model._get_feat_extract_output_lengths(input_lengths)

        labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
        inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
        inputs_dict["labels"] = labels

        outputs = model(**inputs_dict)

        output = outputs[0]

        # Encoder-/Decoder-only models
        hidden_states = outputs.hidden_states[0]
        attentions = outputs.attentions[0]

        hidden_states.retain_grad()
        attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=True)

        self.assertIsNotNone(hidden_states.grad)
        self.assertIsNotNone(attentions.grad)

    def test_seq_classifier_loss_inference(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_loss(*config_and_inputs)

    def test_seq_classifier_train(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.check_seq_classifier_training(*config_and_inputs)

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                uniform_init_parms = [
                    "conv.weight",
                    "masked_spec_embed",
                    "quantizer.weight_proj.weight",
                ]
                if param.requires_grad:
                    if any(x in name for x in uniform_init_parms):
                        self.assertTrue(
                            -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )

    # overwrite from test_modeling_common
    def _mock_init_weights(self, module):
        if hasattr(module, "weight") and module.weight is not None:
            module.weight.data.fill_(3)
        if hasattr(module, "weight_g") and module.weight_g is not None:
            module.weight_g.data.fill_(3)
        if hasattr(module, "weight_v") and module.weight_v is not None:
            module.weight_v.data.fill_(3)
        if hasattr(module, "bias") and module.bias is not None:
            module.bias.data.fill_(3)
        if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
            module.masked_spec_embed.data.fill_(3)

    @unittest.skip(reason="Feed forward chunking is not implemented")
    def test_feed_forward_chunking(self):
        pass

    @slow
    def test_model_from_pretrained(self):
        model = SEWModel.from_pretrained("asapp/sew-tiny-100k")
        self.assertIsNotNone(model)


@require_torch
class SEWUtilsTest(unittest.TestCase):
    def test_compute_mask_indices(self):
        batch_size = 4
        sequence_length = 60
        mask_prob = 0.5
        mask_length = 1

        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
        mask = torch.from_numpy(mask).to(torch_device)

        self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])

    def test_compute_mask_indices_overlap(self):
        batch_size = 4
        sequence_length = 80
        mask_prob = 0.5
        mask_length = 4

        mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
        mask = torch.from_numpy(mask).to(torch_device)

        # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
        for batch_sum in mask.sum(axis=-1):
            self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)


@require_torch
@require_soundfile
@slow
class SEWModelIntegrationTest(unittest.TestCase):
    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").filter(
            lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
        )[:num_samples]["audio"]

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

    def test_inference_pretrained_batched(self):
        model = SEWModel.from_pretrained("asapp/sew-tiny-100k").to(torch_device)
        processor = Wav2Vec2FeatureExtractor.from_pretrained("asapp/sew-tiny-100k")

        input_speech = self._load_datasamples(2)

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

        input_values = inputs.input_values.to(torch_device)

        with torch.no_grad():
            outputs = model(input_values).last_hidden_state

        # expected outputs taken from the original SEW implementation
        expected_outputs_first = torch.tensor(
            [
                [
                    [0.1509, 0.5372, 0.3061, -0.1694],
                    [-0.1700, 0.5764, 0.2753, -0.1299],
                    [0.1281, 0.7949, 0.2342, -0.1624],
                    [-0.1627, 0.6710, 0.2215, -0.1317],
                ],
                [
                    [0.0408, 1.4355, 0.8605, -0.0968],
                    [0.0393, 1.2368, 0.6826, 0.0364],
                    [-0.1269, 1.9215, 1.1677, -0.1297],
                    [-0.1654, 1.6524, 0.6877, -0.0196],
                ],
            ],
            device=torch_device,
        )
        expected_outputs_last = torch.tensor(
            [
                [
                    [1.3379, -0.1450, -0.1500, -0.0515],
                    [0.8364, -0.1680, -0.1248, -0.0689],
                    [1.2791, -0.1507, -0.1523, -0.0564],
                    [0.8208, -0.1690, -0.1199, -0.0751],
                ],
                [
                    [0.6959, -0.0861, -0.1235, -0.0861],
                    [0.4700, -0.1686, -0.1141, -0.1199],
                    [1.0776, -0.1137, -0.0124, -0.0472],
                    [0.5774, -0.1675, -0.0376, -0.0823],
                ],
            ],
            device=torch_device,
        )
        expected_output_sum = 62146.7422

        self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3))
        self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3))
        self.assertTrue(abs(outputs.sum() - expected_output_sum) < 5)

    def test_inference_ctc_batched(self):
        model = SEWForCTC.from_pretrained("asapp/sew-tiny-100k-ft-ls100h").to(torch_device)
        processor = Wav2Vec2Processor.from_pretrained("asapp/sew-tiny-100k-ft-ls100h", do_lower_case=True)

        input_speech = self._load_datasamples(2)

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

        input_values = inputs.input_values.to(torch_device)

        with torch.no_grad():
            logits = model(input_values).logits

        predicted_ids = torch.argmax(logits, dim=-1)
        predicted_trans = processor.batch_decode(predicted_ids)

        EXPECTED_TRANSCRIPTIONS = [
            "a man said to the universe sir i exist",
            "swet covered brian's body trickling into the tightloine closs hat was the only garment he wore",
        ]
        self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
