





import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

import pyarrow as pa
import pyarrow.parquet as pq
import pytest

import datasets
import datasets.config



@pytest.fixture(scope='session'				)
def A_	(		)  ->		List[str]:
       a__						:	int =				10
       a__						:	Optional[Any] =				datasets.Features(
           {
               'tokens': datasets.Sequence(datasets.Value('string'				)				),
               'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive']				)				),
               'answers': datasets.Sequence(
                   {
                       'text': datasets.Value('string'				),
                       'answer_start': datasets.Value('int32'				),
                   }				),
               'id': datasets.Value('int64'				),
           }				)
       a__						:	Tuple =				datasets.Dataset.from_dict(
           {
               'tokens': [['foo'] * 5] * n,
               'labels': [[1] * 5] * n,
               'answers': [{'answer_start': [97], 'text': ['1976']}] * 10,
               'id': list(range(A__				)				),
           }		,      features=A__		,      )
       return dataset



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		Tuple:
       a__						:	Optional[Any] =				str(tmp_path_factory.mktemp('data'				) / 'file.arrow'				)
       dataset.map(cache_file_name=A__				)
       return filename


# FILE_CONTENT + files


lowercase      :       Dict									=  '\\n    Text data.\n    Second line of data.'



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Any:
       a__						:	str =				tmp_path_factory.mktemp('data'				) / 'file.txt'
       a__						:	Tuple =				FILE_CONTENT
       with open(A__		,      'w'				) as f:
              f.write(A__				)
       return filename



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		int:
       import bza

       a__						:	int =				tmp_path_factory.mktemp('data'				) / 'file.txt.bz2'
       a__						:	Dict =				bytes(A__		,      'utf-8'				)
       with bza.open(A__		,      'wb'				) as f:
              f.write(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Any:
       import gzip

       a__						:	str =				str(tmp_path_factory.mktemp('data'				) / 'file.txt.gz'				)
       a__						:	Optional[Any] =				bytes(A__		,      'utf-8'				)
       with gzip.open(A__		,      'wb'				) as f:
              f.write(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Dict:






       if datasets.config.LZ4_AVAILABLE:
              import lza.frame

              a__						:	Optional[Any] =				tmp_path_factory.mktemp('data'				) / 'file.txt.lz4'
              a__						:	Optional[int] =				bytes(A__		,      'utf-8'				)
              with lza.frame.open(A__		,      'wb'				) as f:
                     f.write(A__				)
              return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		int:






       if datasets.config.PY7ZR_AVAILABLE:
              import pyazr

              a__						:	Any =				tmp_path_factory.mktemp('data'				) / 'file.txt.7z'
              with pyazr.SevenZipFile(A__		,      'w'				) as archive:
                     archive.write(A__		,      arcname=os.path.basename(A__				)				)
              return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		Optional[int]:
       import tarfile

       a__						:	Any =				tmp_path_factory.mktemp('data'				) / 'file.txt.tar'
       with tarfile.TarFile(A__		,      'w'				) as f:
              f.add(A__		,      arcname=os.path.basename(A__				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		List[str]:
       import lzma

       a__						:	str =				tmp_path_factory.mktemp('data'				) / 'file.txt.xz'
       a__						:	List[str] =				bytes(A__		,      'utf-8'				)
       with lzma.open(A__		,      'wb'				) as f:
              f.write(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		List[Any]:
       import zipfile

       a__						:	List[Any] =				tmp_path_factory.mktemp('data'				) / 'file.txt.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename(A__				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Optional[int]:






       if datasets.config.ZSTANDARD_AVAILABLE:
              import zstandard as zstd

              a__						:	Tuple =				tmp_path_factory.mktemp('data'				) / 'file.txt.zst'
              a__						:	Union[str, Any] =				bytes(A__		,      'utf-8'				)
              with zstd.open(A__		,      'wb'				) as f:
                     f.write(A__				)
              return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Optional[int]:
       a__						:	Dict =				tmp_path_factory.mktemp('data'				) / 'file.xml'
       a__						:	int =				textwrap.dedent(
           '\\n    <?xml version="1.0" encoding="UTF-8" ?>\n    <tmx version="1.4">\n      <header segtype="sentence" srclang="ca" />\n      <body>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n        </tu>\n        <tu>\n          <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n          <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n        </tu>\n      </body>\n    </tmx>'				)
       with open(A__		,      'w'				) as f:
              f.write(A__				)
       return filename


lowercase      :       Dict									=  [
    {'col_1': '0', 'col_2': 0, 'col_3': 0.0},
    {'col_1': '1', 'col_2': 1, 'col_3': 1.0},
    {'col_1': '2', 'col_2': 2, 'col_3': 2.0},
    {'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
lowercase      :       List[str]									=  [
    {'col_1': '4', 'col_2': 4, 'col_3': 4.0},
    {'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
lowercase      :       int									=  {
    'col_1': ['0', '1', '2', '3'],
    'col_2': [0, 1, 2, 3],
    'col_3': [0.0, 1.0, 2.0, 3.0],
}

lowercase      :       Union[str, Any]									=  [
    {'col_3': 0.0, 'col_1': '0', 'col_2': 0},
    {'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]

lowercase      :       Tuple									=  [
    {'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
    {'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
    {'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
    {'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]



@pytest.fixture(scope='session'				)
def A_	(		)  ->		Dict:
       return DATA_DICT_OF_LISTS



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Any:
       a__						:	Any =				datasets.Dataset.from_dict(A__				)
       a__						:	str =				str(tmp_path_factory.mktemp('data'				) / 'dataset.arrow'				)
       dataset.map(cache_file_name=A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		str:
       a__						:	Any =				str(tmp_path_factory.mktemp('data'				) / 'dataset.sqlite'				)
       with contextlib.closing(sqlitea.connect(A__				)				) as con:
              a__						:	int =				con.cursor()
              cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)'				)
              for item in DATA:
                     cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)'		,      tuple(item.values()				)				)
              con.commit()
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Optional[Any]:
       a__						:	str =				str(tmp_path_factory.mktemp('data'				) / 'dataset.csv'				)
       with open(A__		,      'w'		,      newline=''				) as f:
              a__						:	List[Any] =				csv.DictWriter(A__		,      fieldnames=['col_1', 'col_2', 'col_3']				)
              writer.writeheader()
              for item in DATA:
                     writer.writerow(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Tuple:
       a__						:	Optional[int] =				str(tmp_path_factory.mktemp('data'				) / 'dataset2.csv'				)
       with open(A__		,      'w'		,      newline=''				) as f:
              a__						:	int =				csv.DictWriter(A__		,      fieldnames=['col_1', 'col_2', 'col_3']				)
              writer.writeheader()
              for item in DATA:
                     writer.writerow(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		Tuple:
       import bza

       a__						:	List[Any] =				tmp_path_factory.mktemp('data'				) / 'dataset.csv.bz2'
       with open(A__		,      'rb'				) as f:
              a__						:	List[str] =				f.read()
       # data = bytes(FILE_CONTENT, "utf-8")
       with bza.open(A__		,      'wb'				) as f:
              f.write(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Optional[int]:
       a__						:	List[Any] =				tmp_path_factory.mktemp('data'				) / 'dataset.csv.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename(A__				)				)
              f.write(A__		,      arcname=os.path.basename(A__				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		int:
       a__						:	Union[str, Any] =				tmp_path_factory.mktemp('data'				) / 'dataset.csv.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename(csv_path.replace('.csv'		,      '.CSV'				)				)				)
              f.write(A__		,      arcname=os.path.basename(csva_path.replace('.csv'		,      '.CSV'				)				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Any:
       a__						:	Dict =				tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.csv.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.join('main_dir'		,      os.path.basename(A__				)				)				)
              f.write(A__		,      arcname=os.path.join('main_dir'		,      os.path.basename(A__				)				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[int] =				str(tmp_path_factory.mktemp('data'				) / 'dataset.parquet'				)
       a__						:	List[str] =				pa.schema(
           {
               'col_1': pa.string(),
               'col_2': pa.intaa(),
               'col_3': pa.floataa(),
           }				)
       with open(A__		,      'wb'				) as f:
              a__						:	str =				pq.ParquetWriter(A__		,      schema=A__				)
              a__						:	Union[str, Any] =				pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(A__				)				)] for k in DATA[0]}		,      schema=A__				)
              writer.write_table(A__				)
              writer.close()
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		List[Any]:
       a__						:	List[Any] =				str(tmp_path_factory.mktemp('data'				) / 'dataset.json'				)
       a__						:	Optional[Any] =				{'data': DATA}
       with open(A__		,      'w'				) as f:
              json.dump(A__		,      A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Tuple:
       a__						:	Any =				str(tmp_path_factory.mktemp('data'				) / 'dataset.json'				)
       a__						:	Tuple =				{'data': DATA_DICT_OF_LISTS}
       with open(A__		,      'w'				) as f:
              json.dump(A__		,      A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Optional[int]:
       a__						:	Union[str, Any] =				str(tmp_path_factory.mktemp('data'				) / 'dataset.jsonl'				)
       with open(A__		,      'w'				) as f:
              for item in DATA:
                     f.write(json.dumps(A__				) + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	Tuple =				str(tmp_path_factory.mktemp('data'				) / 'dataset2.jsonl'				)
       with open(A__		,      'w'				) as f:
              for item in DATA:
                     f.write(json.dumps(A__				) + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Tuple:
       a__						:	List[Any] =				str(tmp_path_factory.mktemp('data'				) / 'dataset_312.jsonl'				)
       with open(A__		,      'w'				) as f:
              for item in DATA_312:
                     f.write(json.dumps(A__				) + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		int:
       a__						:	List[str] =				str(tmp_path_factory.mktemp('data'				) / 'dataset-str.jsonl'				)
       with open(A__		,      'w'				) as f:
              for item in DATA_STR:
                     f.write(json.dumps(A__				) + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		Optional[Any]:
       import gzip

       a__						:	List[Any] =				str(tmp_path_factory.mktemp('data'				) / 'dataset.txt.gz'				)
       with open(A__		,      'rb'				) as orig_file:
              with gzip.open(A__		,      'wb'				) as zipped_file:
                     zipped_file.writelines(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		int:
       import gzip

       a__						:	str =				str(tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.gz'				)
       with open(A__		,      'rb'				) as orig_file:
              with gzip.open(A__		,      'wb'				) as zipped_file:
                     zipped_file.writelines(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       a__						:	Optional[int] =				tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename(A__				)				)
              f.write(A__		,      arcname=os.path.basename(A__				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__		,      A__				)  ->		Optional[int]:
       a__						:	List[Any] =				tmp_path_factory.mktemp('data'				) / 'dataset_nested.jsonl.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.join('nested'		,      os.path.basename(A__				)				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Any:
       a__						:	Optional[int] =				tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.jsonl.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.join('main_dir'		,      os.path.basename(A__				)				)				)
              f.write(A__		,      arcname=os.path.join('main_dir'		,      os.path.basename(A__				)				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       a__						:	Optional[int] =				tmp_path_factory.mktemp('data'				) / 'dataset.jsonl.tar'
       with tarfile.TarFile(A__		,      'w'				) as f:
              f.add(A__		,      arcname=os.path.basename(A__				)				)
              f.add(A__		,      arcname=os.path.basename(A__				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__		,      A__				)  ->		Union[str, Any]:
       a__						:	List[Any] =				tmp_path_factory.mktemp('data'				) / 'dataset_nested.jsonl.tar'
       with tarfile.TarFile(A__		,      'w'				) as f:
              f.add(A__		,      arcname=os.path.join('nested'		,      os.path.basename(A__				)				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				['0', '1', '2', '3']
       a__						:	List[str] =				str(tmp_path_factory.mktemp('data'				) / 'dataset.txt'				)
       with open(A__		,      'w'				) as f:
              for item in data:
                     f.write(item + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	Any =				['0', '1', '2', '3']
       a__						:	Union[str, Any] =				str(tmp_path_factory.mktemp('data'				) / 'dataset2.txt'				)
       with open(A__		,      'w'				) as f:
              for item in data:
                     f.write(item + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Optional[Any]:
       a__						:	Dict =				['0', '1', '2', '3']
       a__						:	Optional[int] =				tmp_path_factory.mktemp('data'				) / 'dataset.abc'
       with open(A__		,      'w'				) as f:
              for item in data:
                     f.write(item + '\n'				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Any:
       a__						:	List[str] =				tmp_path_factory.mktemp('data'				) / 'dataset.text.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename(A__				)				)
              f.write(A__		,      arcname=os.path.basename(A__				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Any:
       a__						:	Optional[int] =				tmp_path_factory.mktemp('data'				) / 'dataset_with_dir.text.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.join('main_dir'		,      os.path.basename(A__				)				)				)
              f.write(A__		,      arcname=os.path.join('main_dir'		,      os.path.basename(A__				)				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__		,      A__				)  ->		Any:
       a__						:	Tuple =				tmp_path_factory.mktemp('data'				) / 'dataset.ext.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename('unsupported.ext'				)				)
              f.write(A__		,      arcname=os.path.basename('unsupported_2.ext'				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	int =				'\n'.join(['First', 'Second\u2029with Unicode new line', 'Third']				)
       a__						:	Tuple =				str(tmp_path_factory.mktemp('data'				) / 'dataset_with_unicode_new_lines.txt'				)
       with open(A__		,      'w'		,      encoding='utf-8'				) as f:
              f.write(A__				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		)  ->		int:
       return os.path.join('tests'		,      'features'		,      'data'		,      'test_image_rgb.jpg'				)



@pytest.fixture(scope='session'				)
def A_	(		)  ->		Dict:
       return os.path.join('tests'		,      'features'		,      'data'		,      'test_audio_44100.wav'				)



@pytest.fixture(scope='session'				)
def A_	(		A__		,      A__				)  ->		Any:
       a__						:	Any =				tmp_path_factory.mktemp('data'				) / 'dataset.img.zip'
       with zipfile.ZipFile(A__		,      'w'				) as f:
              f.write(A__		,      arcname=os.path.basename(A__				)				)
              f.write(A__		,      arcname=os.path.basename(A__				).replace('.jpg'		,      '2.jpg'				)				)
       return path



@pytest.fixture(scope='session'				)
def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	Any =				tmp_path_factory.mktemp('data_dir'				)

       (data_dir / "subdir").mkdir()
       with open(data_dir / 'subdir' / 'train.txt'		,      'w'				) as f:
              f.write('foo\n' * 10				)
       with open(data_dir / 'subdir' / 'test.txt'		,      'w'				) as f:
              f.write('bar\n' * 10				)
       # hidden file
       with open(data_dir / 'subdir' / '.test.txt'		,      'w'				) as f:
              f.write('bar\n' * 10				)

       # hidden directory
       (data_dir / ".subdir").mkdir()
       with open(data_dir / '.subdir' / 'train.txt'		,      'w'				) as f:
              f.write('foo\n' * 10				)
       with open(data_dir / '.subdir' / 'test.txt'		,      'w'				) as f:
              f.write('bar\n' * 10				)

       return data_dir









import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							Optional[Any]													=     {
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
snake_case__					:							Optional[int]													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						List[str]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).shape
      else:
            __A          =				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":
            __A          =				value
      elif weight_type == "weight_g":
            __A          =				value
      elif weight_type == "weight_v":
            __A          =				value
      elif weight_type == "bias":
            __A          =				value
      else:
            __A          =				value

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



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A          =				'''unispeech_sat.''' + 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]:
                              if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
                                    # special case since naming is very similar
                                    continue
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              if "weight_g" in name:
                                    __A          =				'''weight_g'''
                              elif "weight_v" in name:
                                    __A          =				'''weight_v'''
                              elif "bias" in name:
                                    __A          =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __A          =				'''weight'''
                              else:
                                    __A          =				None
                              set_recursively(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        continue
            if not is_used:
                  unused_weights.append(lowerCamelCase )

      logger.warning(F"""Unused weights: {unused_weights}""" )



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Any					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   List[str] )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				full_name.split('''conv_layers.''' )[-1]
      __A          =				name.split('''.''' )
      __A          =				int(items[0] )
      __A          =				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.""" )
                  __A          =				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.""" )
                  __A          =				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[layer_id].layer_norm.bias.data.shape} was found.""" )
                  __A          =				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[layer_id].layer_norm.weight.data.shape} was found.""" )
                  __A          =				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 _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[int]=True )	->						List[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechSatConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechSatConfig()

      __A          =				''''''

      if is_finetuned:
            __A          =				UniSpeechSatForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechSatForPreTraining(lowerCamelCase )

      __A   ,						__A   ,						__A          =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]					,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
      __A          =				model[0].eval()

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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'
       )
       snake_case__					:							Any													=     parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





