





import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_A					:				List[str]				=			pytest.mark.integration



@require_faiss
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(A_     ) for x in np.arange(30     ).tolist()]}     )
        return dset



    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			self._create_dummy_dataset()
        SCREAMING_SNAKE_CASE__											=			dset.map(
            lambda A_   ,				A_     : {"vecs": i * np.ones(5   ,				dtype=np.floataa     )}   ,				with_indices=A_   ,				keep_in_memory=A_     )
        SCREAMING_SNAKE_CASE__											=			dset.add_faiss_index('''vecs'''   ,				batch_size=1_00   ,				metric_type=faiss.METRIC_INNER_PRODUCT     )
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			dset.get_nearest_examples('''vecs'''   ,				np.ones(5   ,				dtype=np.floataa     )     )
        self.assertEqual(examples['''filename'''][0]   ,				'''my_name-train_29'''     )
        dset.drop_index('''vecs'''     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)     ) * np.arange(30     ).reshape(-1   ,				1     )   ,				index_name='''vecs'''   ,				batch_size=1_00   ,				metric_type=faiss.METRIC_INNER_PRODUCT   ,				)
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			dset.get_nearest_examples('''vecs'''   ,				np.ones(5   ,				dtype=np.floataa     )     )
        self.assertEqual(examples['''filename'''][0]   ,				'''my_name-train_29'''     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)     ) * np.arange(30     ).reshape(-1   ,				1     )   ,				index_name='''vecs'''   ,				metric_type=faiss.METRIC_INNER_PRODUCT   ,				)

        # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
        # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
        # see https://bugs.python.org/issue14243 and
        # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
        with tempfile.NamedTemporaryFile(delete=A_     ) as tmp_file:
            dset.save_faiss_index('''vecs'''   ,				tmp_file.name     )
            dset.load_faiss_index('''vecs2'''   ,				tmp_file.name     )
        os.unlink(tmp_file.name     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			dset.get_nearest_examples('''vecs2'''   ,				np.ones(5   ,				dtype=np.floataa     )     )
        self.assertEqual(examples['''filename'''][0]   ,				'''my_name-train_29'''     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)     ) * np.arange(30     ).reshape(-1   ,				1     )   ,				index_name='''vecs'''     )
        dset.drop_index('''vecs'''     )
        self.assertRaises(A_   ,				partial(dset.get_nearest_examples   ,				'''vecs2'''   ,				np.ones(5   ,				dtype=np.floataa     )     )     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        from elasticsearch import Elasticsearch

        SCREAMING_SNAKE_CASE__											=			self._create_dummy_dataset()
        with patch('''elasticsearch.Elasticsearch.search'''     ) as mocked_search, patch(
            '''elasticsearch.client.IndicesClient.create'''     ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''     ) as mocked_bulk:
            SCREAMING_SNAKE_CASE__											=			{'''acknowledged''': True}
            mocked_bulk.return_value([(True, None)] * 30     )
            SCREAMING_SNAKE_CASE__											=			{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
            SCREAMING_SNAKE_CASE__											=			Elasticsearch()

            dset.add_elasticsearch_index('''filename'''   ,				es_client=A_     )
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			dset.get_nearest_examples('''filename'''   ,				'''my_name-train_29'''     )
            self.assertEqual(examples['''filename'''][0]   ,				'''my_name-train_29'''     )





@require_faiss
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )

        # add vectors
        index.add_vectors(np.eye(5   ,				dtype=np.floataa     )     )
        self.assertIsNotNone(index.faiss_index     )
        self.assertEqual(index.faiss_index.ntotal   ,				5     )
        index.add_vectors(np.zeros((5, 5)   ,				dtype=np.floataa     )     )
        self.assertEqual(index.faiss_index.ntotal   ,				10     )

        # single query
        SCREAMING_SNAKE_CASE__											=			np.zeros(5   ,				dtype=np.floataa     )
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search(A_     )
        self.assertRaises(A_   ,				index.search   ,				query.reshape(-1   ,				1     )     )
        self.assertGreater(scores[0]   ,				0     )
        self.assertEqual(indices[0]   ,				1     )

        # batched queries
        SCREAMING_SNAKE_CASE__											=			np.eye(5   ,				dtype=np.floataa     )[::-1]
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search_batch(A_     )
        self.assertRaises(A_   ,				index.search_batch   ,				queries[0]     )
        SCREAMING_SNAKE_CASE__											=			[scores[0] for scores in total_scores]
        SCREAMING_SNAKE_CASE__											=			[indices[0] for indices in total_indices]
        self.assertGreater(np.min(A_     )   ,				0     )
        self.assertListEqual([4, 3, 2, 1, 0]   ,				A_     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			FaissIndex(string_factory='''Flat'''     )
        index.add_vectors(np.eye(5   ,				dtype=np.floataa     )     )
        self.assertIsInstance(index.faiss_index   ,				faiss.IndexFlat     )
        SCREAMING_SNAKE_CASE__											=			FaissIndex(string_factory='''LSH'''     )
        index.add_vectors(np.eye(5   ,				dtype=np.floataa     )     )
        self.assertIsInstance(index.faiss_index   ,				faiss.IndexLSH     )
        with self.assertRaises(A_     ):
            SCREAMING_SNAKE_CASE__											=			FaissIndex(string_factory='''Flat'''   ,				custom_index=faiss.IndexFlat(5     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			faiss.IndexFlat(5     )
        SCREAMING_SNAKE_CASE__											=			FaissIndex(custom_index=A_     )
        index.add_vectors(np.eye(5   ,				dtype=np.floataa     )     )
        self.assertIsInstance(index.faiss_index   ,				faiss.IndexFlat     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        import faiss

        SCREAMING_SNAKE_CASE__											=			FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
        index.add_vectors(np.eye(5   ,				dtype=np.floataa     )     )

        # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
        # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
        # see https://bugs.python.org/issue14243 and
        # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
        with tempfile.NamedTemporaryFile(delete=A_     ) as tmp_file:
            index.save(tmp_file.name     )
            SCREAMING_SNAKE_CASE__											=			FaissIndex.load(tmp_file.name     )
        os.unlink(tmp_file.name     )

        SCREAMING_SNAKE_CASE__											=			np.zeros(5   ,				dtype=np.floataa     )
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search(A_     )
        self.assertGreater(scores[0]   ,				0     )
        self.assertEqual(indices[0]   ,				1     )





@require_faiss
def 							__snake_case							(    lowerCAmelCase_	)				->						List[str]:
    import faiss

    SCREAMING_SNAKE_CASE__											=			FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT	)
    index.add_vectors(np.eye(5 ,							dtype=np.floataa	)	)

    SCREAMING_SNAKE_CASE__											=			'''index.faiss'''
    SCREAMING_SNAKE_CASE__											=			f'''mock://{index_name}'''
    index.save(lowerCAmelCase_ ,							storage_options=mockfs.storage_options	)
    SCREAMING_SNAKE_CASE__											=			FaissIndex.load(lowerCAmelCase_ ,							storage_options=mockfs.storage_options	)

    SCREAMING_SNAKE_CASE__											=			np.zeros(5 ,							dtype=np.floataa	)
    SCREAMING_SNAKE_CASE__											=			1
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search(lowerCAmelCase_	)
    assert scores[0] > 0
    assert indices[0] == 1



@require_elasticsearch
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def  lowercase_					(       self     ):





        '''simple docstring'''

        from elasticsearch import Elasticsearch

        with patch('''elasticsearch.Elasticsearch.search'''     ) as mocked_search, patch(
            '''elasticsearch.client.IndicesClient.create'''     ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk'''     ) as mocked_bulk:
            SCREAMING_SNAKE_CASE__											=			Elasticsearch()
            SCREAMING_SNAKE_CASE__											=			{'''acknowledged''': True}
            SCREAMING_SNAKE_CASE__											=			ElasticSearchIndex(es_client=A_     )
            mocked_bulk.return_value([(True, None)] * 3     )
            index.add_documents(['''foo''', '''bar''', '''foobar''']     )

            # single query
            SCREAMING_SNAKE_CASE__											=			'''foo'''
            SCREAMING_SNAKE_CASE__											=			{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search(A_     )
            self.assertEqual(scores[0]   ,				1     )
            self.assertEqual(indices[0]   ,				0     )

            # single query with timeout
            SCREAMING_SNAKE_CASE__											=			'''foo'''
            SCREAMING_SNAKE_CASE__											=			{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search(A_   ,				request_timeout=30     )
            self.assertEqual(scores[0]   ,				1     )
            self.assertEqual(indices[0]   ,				0     )

            # batched queries
            SCREAMING_SNAKE_CASE__											=			['''foo''', '''bar''', '''foobar''']
            SCREAMING_SNAKE_CASE__											=			{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search_batch(A_     )
            SCREAMING_SNAKE_CASE__											=			[scores[0] for scores in total_scores]
            SCREAMING_SNAKE_CASE__											=			[indices[0] for indices in total_indices]
            self.assertGreater(np.min(A_     )   ,				0     )
            self.assertListEqual([1, 1, 1]   ,				A_     )

            # batched queries with timeout
            SCREAMING_SNAKE_CASE__											=			['''foo''', '''bar''', '''foobar''']
            SCREAMING_SNAKE_CASE__											=			{'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			index.search_batch(A_   ,				request_timeout=30     )
            SCREAMING_SNAKE_CASE__											=			[scores[0] for scores in total_scores]
            SCREAMING_SNAKE_CASE__											=			[indices[0] for indices in total_indices]
            self.assertGreater(np.min(A_     )   ,				0     )
            self.assertListEqual([1, 1, 1]   ,				A_     )







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,
  )
