





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_=13   ,				A_=10   ,				A_=3   ,				A_=2   ,				A_=2   ,				A_=True   ,				A_=True   ,				A_=32   ,				A_=5   ,				A_=4   ,				A_=37   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=10   ,				A_=0.02   ,				A_="divided_space_time"   ,				A_=None   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			image_size
        SCREAMING_SNAKE_CASE__											=			num_channels
        SCREAMING_SNAKE_CASE__											=			patch_size
        SCREAMING_SNAKE_CASE__											=			num_frames
        SCREAMING_SNAKE_CASE__											=			is_training
        SCREAMING_SNAKE_CASE__											=			use_labels
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_type
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			scope
        SCREAMING_SNAKE_CASE__											=			num_labels

        # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
        SCREAMING_SNAKE_CASE__											=			(image_size // patch_size) ** 2
        SCREAMING_SNAKE_CASE__											=			(num_frames) * self.num_patches_per_frame + 1



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

        SCREAMING_SNAKE_CASE__											=			None
        if self.use_labels:
            SCREAMING_SNAKE_CASE__											=			ids_tensor([self.batch_size]   ,				self.num_labels     )

        SCREAMING_SNAKE_CASE__											=			self.get_config()

        return config, pixel_values, labels



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimesformerConfig(
            image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				num_frames=self.num_frames   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				initializer_range=self.initializer_range   ,				attention_type=self.attention_type   ,				)
        SCREAMING_SNAKE_CASE__											=			self.num_labels
        return config



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimesformerModel(config=A_     )
        model.to(A_     )
        model.eval()
        SCREAMING_SNAKE_CASE__											=			model(A_     )
        self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimesformerForVideoClassification(A_     )
        model.to(A_     )
        model.eval()

        SCREAMING_SNAKE_CASE__											=			model(A_     )

        # verify the logits shape
        SCREAMING_SNAKE_CASE__											=			torch.Size((self.batch_size, self.num_labels)     )
        self.parent.assertEqual(result.logits.shape   ,				A_     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			config_and_inputs
        SCREAMING_SNAKE_CASE__											=			{'''pixel_values''': pixel_values}
        return config, inputs_dict





@require_torch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[Any]				=	(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
    lowerCamelCase__    :		Optional[int]				=	(
        {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
        if is_torch_available()
        else {}
    )

    lowerCamelCase__    :		List[str]				=	False
    lowerCamelCase__    :		Any				=	False
    lowerCamelCase__    :		Union[str, Any]				=	False
    lowerCamelCase__    :		Dict				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimesformerModelTester(self     )
        SCREAMING_SNAKE_CASE__											=			ConfigTester(
            self   ,				config_class=A_   ,				has_text_modality=A_   ,				hidden_size=37     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_=False     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			copy.deepcopy(A_     )

        if return_labels:
            if model_class in get_values(A_     ):
                SCREAMING_SNAKE_CASE__											=			torch.zeros(
                    self.model_tester.batch_size   ,				dtype=torch.long   ,				device=A_     )

        return inputs_dict



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self.config_tester.run_common_tests()



    @unittest.skip(reason='''TimeSformer does not use inputs_embeds'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			model_class(A_     )
            self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)     )
            SCREAMING_SNAKE_CASE__											=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(A_   ,				nn.Linear     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__											=			['''pixel_values''']
            self.assertListEqual(arg_names[:1]   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_video_classification(*A_     )



    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__											=			TimesformerModel.from_pretrained(A_     )
            self.assertIsNotNone(A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not self.has_attentions:
            pass

        else:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()
            SCREAMING_SNAKE_CASE__											=			True

            for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE__											=			self.model_tester.seq_length
                SCREAMING_SNAKE_CASE__											=			self.model_tester.num_frames

                SCREAMING_SNAKE_CASE__											=			True
                SCREAMING_SNAKE_CASE__											=			False
                SCREAMING_SNAKE_CASE__											=			True
                SCREAMING_SNAKE_CASE__											=			model_class(A_     )
                model.to(A_     )
                model.eval()
                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			model(**self._prepare_for_class(A_   ,				A_     )     )
                SCREAMING_SNAKE_CASE__											=			outputs.attentions
                self.assertEqual(len(A_     )   ,				self.model_tester.num_hidden_layers     )

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

                # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                self.assertListEqual(
                    list(attentions[0].shape[-3:]     )   ,				[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]   ,				)
                SCREAMING_SNAKE_CASE__											=			len(A_     )

                # Check attention is always last and order is fine
                SCREAMING_SNAKE_CASE__											=			True
                SCREAMING_SNAKE_CASE__											=			True
                SCREAMING_SNAKE_CASE__											=			model_class(A_     )
                model.to(A_     )
                model.eval()
                with torch.no_grad():
                    SCREAMING_SNAKE_CASE__											=			model(**self._prepare_for_class(A_   ,				A_     )     )

                self.assertEqual(out_len + 1   ,				len(A_     )     )

                SCREAMING_SNAKE_CASE__											=			outputs.attentions

                self.assertEqual(len(A_     )   ,				self.model_tester.num_hidden_layers     )

                # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]     )   ,				[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]   ,				)




    def  lowercase_					(       self     ):





        '''simple docstring'''

        def check_hidden_states_output(A_   ,				A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			model_class(A_     )
            model.to(A_     )
            model.eval()

            with torch.no_grad():
                SCREAMING_SNAKE_CASE__											=			model(**self._prepare_for_class(A_   ,				A_     )     )

            SCREAMING_SNAKE_CASE__											=			outputs.hidden_states
            SCREAMING_SNAKE_CASE__											=			self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(A_     )   ,				A_     )

            SCREAMING_SNAKE_CASE__											=			self.model_tester.seq_length

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

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__											=			True
            check_hidden_states_output(A_   ,				A_   ,				A_     )

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

            check_hidden_states_output(A_   ,				A_   ,				A_     )





def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,							filename='''eating_spaghetti.npy''' ,							repo_type='''dataset'''	)
    SCREAMING_SNAKE_CASE__											=			np.load(lowerCAmelCase_	)
    return list(lowerCAmelCase_	)



@require_torch
@require_vision
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    @cached_property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,				image_std=[0.5, 0.5, 0.5]     )
            if is_vision_available()
            else None
        )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''     ).to(
            A_     )

        SCREAMING_SNAKE_CASE__											=			self.default_image_processor
        SCREAMING_SNAKE_CASE__											=			prepare_video()
        SCREAMING_SNAKE_CASE__											=			image_processor(video[:8]   ,				return_tensors='''pt'''     ).to(A_     )

        # forward pass
        with torch.no_grad():
            SCREAMING_SNAKE_CASE__											=			model(**A_     )

        # verify the logits
        SCREAMING_SNAKE_CASE__											=			torch.Size((1, 4_00)     )
        self.assertEqual(outputs.logits.shape   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			torch.tensor([-0.3016, -0.7713, -0.4205]     ).to(A_     )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				A_   ,				atol=1E-4     )     )







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_A					:				List[str]				=			logging.get_logger(__name__)

_A					:				List[str]				=			{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
_A					:				Optional[int]				=			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			{}
    with open(lowerCAmelCase_ ,							'''r'''	) as file:
        for line_number, line in enumerate(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			line.strip()
            if line:
                SCREAMING_SNAKE_CASE__											=			line.split()
                SCREAMING_SNAKE_CASE__											=			line_number
                SCREAMING_SNAKE_CASE__											=			words[0]
                SCREAMING_SNAKE_CASE__											=			value
    return result


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    for attribute in key.split('''.'''	):
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			PARAM_MAPPING[full_name.split('''.'''	)[-1]]
            SCREAMING_SNAKE_CASE__											=			'''param'''

    if weight_type is not None and weight_type != "param":
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	).shape
    elif weight_type is not None and weight_type == "param":
        SCREAMING_SNAKE_CASE__											=			hf_pointer
        for attribute in hf_param_name.split('''.'''	):
            SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			shape_pointer.shape

        # let's reduce dimension
        SCREAMING_SNAKE_CASE__											=			value[0]
    else:
        SCREAMING_SNAKE_CASE__											=			hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
            f''' {value.shape} for {full_name}'''	)

    if weight_type == "weight":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "weight_g":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "weight_v":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "bias":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "param":
        for attribute in hf_param_name.split('''.'''	):
            SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			value
    else:
        SCREAMING_SNAKE_CASE__											=			value

    logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			PARAM_MAPPING[full_name.split('''.'''	)[-1]]
            SCREAMING_SNAKE_CASE__											=			'''param'''

    if weight_type is not None and weight_type != "param":
        SCREAMING_SNAKE_CASE__											=			'''.'''.join([key, weight_type]	)
    elif weight_type is not None and weight_type == "param":
        SCREAMING_SNAKE_CASE__											=			'''.'''.join([key, hf_param_name]	)
    else:
        SCREAMING_SNAKE_CASE__											=			key

    SCREAMING_SNAKE_CASE__											=			value if '''lm_head''' in full_key else value[0]


_A					:				Union[str, Any]				=			{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			False
    for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE__											=			'''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
            SCREAMING_SNAKE_CASE__											=			True
            if "*" in mapped_key:
                SCREAMING_SNAKE_CASE__											=			name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
                SCREAMING_SNAKE_CASE__											=			mapped_key.replace('''*''' ,							lowerCAmelCase_	)
            if "weight_g" in name:
                SCREAMING_SNAKE_CASE__											=			'''weight_g'''
            elif "weight_v" in name:
                SCREAMING_SNAKE_CASE__											=			'''weight_v'''
            elif "bias" in name:
                SCREAMING_SNAKE_CASE__											=			'''bias'''
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                SCREAMING_SNAKE_CASE__											=			'''weight'''
            else:
                SCREAMING_SNAKE_CASE__											=			None
            if hf_dict is not None:
                rename_dict(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
            else:
                set_recursively(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
            return is_used
    return is_used


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE__											=			hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        SCREAMING_SNAKE_CASE__											=			False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							hf_model.config.feat_extract_norm == '''group''' ,							)
            SCREAMING_SNAKE_CASE__											=			True
        else:
            SCREAMING_SNAKE_CASE__											=			load_wavaveca_layer(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
        if not is_used:
            unused_weights.append(lowerCAmelCase_	)

    logger.warning(f'''Unused weights: {unused_weights}'''	)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			full_name.split('''conv_layers.'''	)[-1]
    SCREAMING_SNAKE_CASE__											=			name.split('''.'''	)
    SCREAMING_SNAKE_CASE__											=			int(items[0]	)
    SCREAMING_SNAKE_CASE__											=			int(items[1]	)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f'''{full_name} has size {value.shape}, but'''
                    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
            SCREAMING_SNAKE_CASE__											=			value
            logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f'''{full_name} has size {value.shape}, but'''
                    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
            SCREAMING_SNAKE_CASE__											=			value
            logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f'''{full_name} has size {value.shape}, but'''
                    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''	)
            SCREAMING_SNAKE_CASE__											=			value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f'''{full_name} has size {value.shape}, but'''
                    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''	)
            SCREAMING_SNAKE_CASE__											=			value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
    else:
        unused_weights.append(lowerCAmelCase_	)


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=True ,							lowerCAmelCase_=False	)				->						int:
    if config_path is not None:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig()

    if is_seq_class:
        SCREAMING_SNAKE_CASE__											=			read_txt_into_dict(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			idalabel
        SCREAMING_SNAKE_CASE__											=			WavaVecaForSequenceClassification(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor(
            feature_size=1 ,							sampling_rate=1_6_0_0_0 ,							padding_value=0 ,							do_normalize=lowerCAmelCase_ ,							return_attention_mask=lowerCAmelCase_ ,							)
        feature_extractor.save_pretrained(lowerCAmelCase_	)

    elif is_finetuned:
        if dict_path:
            SCREAMING_SNAKE_CASE__											=			Dictionary.load(lowerCAmelCase_	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            SCREAMING_SNAKE_CASE__											=			target_dict.pad_index
            SCREAMING_SNAKE_CASE__											=			target_dict.bos_index
            SCREAMING_SNAKE_CASE__											=			target_dict.eos_index
            SCREAMING_SNAKE_CASE__											=			len(target_dict.symbols	)
            SCREAMING_SNAKE_CASE__											=			os.path.join(lowerCAmelCase_ ,							'''vocab.json'''	)
            if not os.path.isdir(lowerCAmelCase_	):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_	)	)
                return
            os.makedirs(lowerCAmelCase_ ,							exist_ok=lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			target_dict.indices

            # fairseq has the <pad> and <s> switched
            SCREAMING_SNAKE_CASE__											=			0
            SCREAMING_SNAKE_CASE__											=			1
            with open(lowerCAmelCase_ ,							'''w''' ,							encoding='''utf-8'''	) as vocab_handle:
                json.dump(lowerCAmelCase_ ,							lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			WavaVecaCTCTokenizer(
                lowerCAmelCase_ ,							unk_token=target_dict.unk_word ,							pad_token=target_dict.pad_word ,							bos_token=target_dict.bos_word ,							eos_token=target_dict.eos_word ,							word_delimiter_token='''|''' ,							do_lower_case=lowerCAmelCase_ ,							)
            SCREAMING_SNAKE_CASE__											=			True if config.feat_extract_norm == '''layer''' else False
            SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor(
                feature_size=1 ,							sampling_rate=1_6_0_0_0 ,							padding_value=0 ,							do_normalize=lowerCAmelCase_ ,							return_attention_mask=lowerCAmelCase_ ,							)
            SCREAMING_SNAKE_CASE__											=			WavaVecaProcessor(feature_extractor=lowerCAmelCase_ ,							tokenizer=lowerCAmelCase_	)
            processor.save_pretrained(lowerCAmelCase_	)

        SCREAMING_SNAKE_CASE__											=			WavaVecaForCTC(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaForPreTraining(lowerCAmelCase_	)

    if is_finetuned or is_seq_class:
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path] ,							arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
    else:
        SCREAMING_SNAKE_CASE__											=			argparse.Namespace(task='''audio_pretraining'''	)
        SCREAMING_SNAKE_CASE__											=			fairseq.tasks.setup_task(lowerCAmelCase_	)

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,							task=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			model[0].eval()

    recursively_load_weights(lowerCAmelCase_ ,							lowerCAmelCase_ ,							not is_finetuned	)

    hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				int				=			argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  _A					:				List[str]				=			parser.parse_args()

  _A					:				List[str]				=			not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )
