
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "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",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCAmelCase    					=   [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def      A (       A_							:					Union[str, Any]    ,							A_							:					str    ,							A_							:					str    ,							A_							:					List[str]    ,							A_							:					str    ,							A_							:					Optional[int]       ):
						for attribute in key.split('''.'''       ):
												if is_finetuned:
																		if attribute in ["quantizer", "project_q", "project_hid"]:
																								# those layers are only relevant for pretraining and should be dropped
																								return

																		if attribute == "ctc_proj":
																								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																								snake_case       :     Any =    '''lm_head'''

												snake_case       :     List[Any] =    getattr(A_    ,							A_       )

						if weight_type is not None:
												snake_case       :     Any =    getattr(A_    ,							A_       ).shape
						else:
												snake_case       :     List[str] =    hf_pointer.shape

						assert hf_shape == value.shape, (
						    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":
												snake_case       :     Dict =    value
						elif weight_type == "weight_g":
												snake_case       :     List[str] =    value
						elif weight_type == "weight_v":
												snake_case       :     List[Any] =    value
						elif weight_type == "bias":
												snake_case       :     Optional[Any] =    value
						else:
												snake_case       :     Tuple =    value

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

def      A (       A_							:					List[Any]    ,							A_							:					Tuple    ,							A_							:					str       ):
						snake_case       :     Dict =    []
						snake_case       :     Tuple =    fairseq_model.state_dict()

						snake_case       :     Optional[int] =    hf_model.unispeech.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case       :     Union[str, Any] =    False
												if "conv_layers" in name:
																		load_conv_layer(
																		    A_    ,							A_    ,							A_    ,							A_    ,							hf_model.config.feat_extract_norm == '''group'''    ,							)
																		snake_case       :     str =    True
												else:
																		for key, mapped_key in MAPPING.items():
																								snake_case       :     Optional[Any] =    '''unispeech.''' + 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]:
																														snake_case       :     List[Any] =    True
																														if "*" in mapped_key:
																																				snake_case       :     List[Any] =    name.split(A_       )[0].split('''.'''       )[-2]
																																				snake_case       :     int =    mapped_key.replace('''*'''    ,							A_       )
																														if "weight_g" in name:
																																				snake_case       :     Dict =    '''weight_g'''
																														elif "weight_v" in name:
																																				snake_case       :     List[Any] =    '''weight_v'''
																														elif "bias" in name:
																																				snake_case       :     List[Any] =    '''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				snake_case       :     Optional[int] =    '''weight'''
																														else:
																																				snake_case       :     Union[str, Any] =    None
																														set_recursively(A_    ,							A_    ,							A_    ,							A_    ,							A_    ,							A_       )
																								continue
												if not is_used:
																		unused_weights.append(A_       )

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

def      A (       A_							:					int    ,							A_							:					Dict    ,							A_							:					Any    ,							A_							:					Union[str, Any]    ,							A_							:					List[str]       ):
						snake_case       :     Union[str, Any] =    full_name.split('''conv_layers.'''       )[-1]
						snake_case       :     Dict =    name.split('''.'''       )
						snake_case       :     str =    int(items[0]       )
						snake_case       :     int =    int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		snake_case       :     Optional[Any] =    value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		snake_case       :     Tuple =    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:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		snake_case       :     List[str] =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		snake_case       :     Optional[int] =    value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(A_       )

@torch.no_grad()
def      A (       A_							:					List[str]    ,							A_							:					Optional[int]    ,							A_							:					Any=None    ,							A_							:					List[Any]=None    ,							A_							:					Any=True       ):
						if config_path is not None:
												snake_case       :     Optional[Any] =    UniSpeechConfig.from_pretrained(A_       )
						else:
												snake_case       :     Optional[int] =    UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		snake_case       :     int =    Dictionary.load_from_json(A_       )

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

																		# fairseq has the <pad> and <s> switched
																		snake_case       :     Any =    42
																		snake_case       :     Any =    43
																		with open(A_    ,							'''w'''    ,							encoding='''utf-8'''       ) as vocab_handle:
																								json.dump(A_    ,							A_       )
																		snake_case       :     List[Any] =    WavaVecaPhonemeCTCTokenizer(
																		    A_    ,							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=A_    ,							)
																		snake_case       :     Union[str, Any] =    True if config.feat_extract_norm == '''layer''' else False
																		snake_case       :     Tuple =    WavaVecaFeatureExtractor(
																		    feature_size=1    ,							sampling_rate=16000    ,							padding_value=0    ,							do_normalize=A_    ,							return_attention_mask=A_    ,							)
																		snake_case       :     Any =    WavaVecaProcessor(feature_extractor=A_    ,							tokenizer=A_       )
																		processor.save_pretrained(A_       )

												snake_case       :     List[str] =    UniSpeechForCTC(A_       )
						else:
												snake_case       :     Union[str, Any] =    UniSpeechForPreTraining(A_       )

						if is_finetuned:
												snake_case       :     Any =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]    ,							arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       ), '''w2v_path''': checkpoint_path}       )
						else:
												snake_case       :     Tuple =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

						snake_case       :     Optional[Any] =    model[0].eval()

						recursively_load_weights(A_    ,							A_    ,							A_       )

						hf_unispeech.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   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"
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_unispeech_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)



'''simple docstring'''





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 (       ):
						snake_case       :     Dict =    10
						snake_case       :     List[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'''       ),
						    }       )
						snake_case       :     Dict =    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_							:					List[Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''file.arrow'''       )
						dataset.map(cache_file_name=A_       )
						return filename


# FILE_CONTENT + files


UpperCAmelCase    					=   "\\n    Text data.\n    Second line of data."

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[str]       ):
						snake_case       :     Any =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt'''
						snake_case       :     Union[str, Any] =    FILE_CONTENT
						with open(A_    ,							'''w'''       ) as f:
												f.write(A_       )
						return filename

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]       ):
						import bza

						snake_case       :     Tuple =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.bz2'''
						snake_case       :     Union[str, Any] =    bytes(A_    ,							'''utf-8'''       )
						with bza.open(A_    ,							'''wb'''       ) as f:
												f.write(A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					List[Any]       ):
						import gzip

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

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]       ):
						if datasets.config.LZ4_AVAILABLE:
												import lza.frame

												snake_case       :     Union[str, Any] =    tmp_path_factory.mktemp('''data'''       ) / '''file.txt.lz4'''
												snake_case       :     Tuple =    bytes(A_    ,							'''utf-8'''       )
												with lza.frame.open(A_    ,							'''wb'''       ) as f:
																		f.write(A_       )
												return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Union[str, Any]    ,							A_							:					str       ):
						if datasets.config.PY7ZR_AVAILABLE:
												import pyazr

												snake_case       :     Optional[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_							:					List[str]    ,							A_							:					Optional[int]       ):
						import tarfile

						snake_case       :     Union[str, 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_							:					Any       ):
						import lzma

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

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[int]    ,							A_							:					Union[str, Any]       ):
						import zipfile

						snake_case       :     List[str] =    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_							:					List[str]       ):
						if datasets.config.ZSTANDARD_AVAILABLE:
												import zstandard as zstd

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

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


UpperCAmelCase    					=   [
    {"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},
]
UpperCAmelCase    					=   [
    {"col_1": "4", "col_2": 4, "col_3": 4.0},
    {"col_1": "5", "col_2": 5, "col_3": 5.0},
]
UpperCAmelCase    					=   {
    "col_1": ["0", "1", "2", "3"],
    "col_2": [0, 1, 2, 3],
    "col_3": [0.0, 1.0, 2.0, 3.0],
}

UpperCAmelCase    					=   [
    {"col_3": 0.0, "col_1": "0", "col_2": 0},
    {"col_3": 1.0, "col_1": "1", "col_2": 1},
]

UpperCAmelCase    					=   [
    {"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 (       ):
						return DATA_DICT_OF_LISTS

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     str =    datasets.Dataset.from_dict(A_       )
						snake_case       :     List[Any] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.arrow'''       )
						dataset.map(cache_file_name=A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.sqlite'''       )
						with contextlib.closing(sqlitea.connect(A_       )       ) as con:
												snake_case       :     Optional[Any] =    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_							:					List[str]       ):
						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.csv'''       )
						with open(A_    ,							'''w'''    ,							newline=''''''       ) as f:
												snake_case       :     Optional[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_							:					Optional[int]       ):
						snake_case       :     Optional[int] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset2.csv'''       )
						with open(A_    ,							'''w'''    ,							newline=''''''       ) as f:
												snake_case       :     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_							:					Optional[Any]    ,							A_							:					Optional[int]       ):
						import bza

						snake_case       :     Optional[int] =    tmp_path_factory.mktemp('''data'''       ) / '''dataset.csv.bz2'''
						with open(A_    ,							'''rb'''       ) as f:
												snake_case       :     Any =    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_							:					str    ,							A_							:					int    ,							A_							:					List[Any]       ):
						snake_case       :     Optional[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_							:					Dict    ,							A_							:					Optional[Any]    ,							A_							:					Dict       ):
						snake_case       :     str =    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_							:					Dict    ,							A_							:					Dict    ,							A_							:					Optional[int]       ):
						snake_case       :     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_							:					Dict       ):
						snake_case       :     List[str] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.parquet'''       )
						snake_case       :     Tuple =    pa.schema(
						    {
						        '''col_1''': pa.string(),
						        '''col_2''': pa.intaa(),
						        '''col_3''': pa.floataa(),
						    }       )
						with open(A_    ,							'''wb'''       ) as f:
												snake_case       :     Dict =    pq.ParquetWriter(A_    ,							schema=A_       )
												snake_case       :     str =    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[str]       ):
						snake_case       :     Any =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.json'''       )
						snake_case       :     Optional[int] =    {'''data''': DATA}
						with open(A_    ,							'''w'''       ) as f:
												json.dump(A_    ,							A_       )
						return path

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					str       ):
						snake_case       :     List[str] =    str(tmp_path_factory.mktemp('''data'''       ) / '''dataset.json'''       )
						snake_case       :     Any =    {'''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[Any]       ):
						snake_case       :     Optional[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_							:					List[Any]       ):
						snake_case       :     Optional[int] =    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_							:					List[Any]       ):
						snake_case       :     Dict =    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       ):
						snake_case       :     Dict =    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_							:					Optional[int]    ,							A_							:					Optional[int]       ):
						import gzip

						snake_case       :     Dict =    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_							:					Optional[int]    ,							A_							:					Any       ):
						import gzip

						snake_case       :     List[Any] =    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_							:					str    ,							A_							:					List[str]    ,							A_							:					Optional[int]       ):
						snake_case       :     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_							:					List[str]    ,							A_							:					Any    ,							A_							:					Union[str, Any]    ,							A_							:					int       ):
						snake_case       :     List[str] =    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_							:					List[str]    ,							A_							:					Dict    ,							A_							:					Any       ):
						snake_case       :     Optional[Any] =    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_							:					List[Any]    ,							A_							:					Tuple    ,							A_							:					List[str]       ):
						snake_case       :     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_							:					Tuple    ,							A_							:					Any    ,							A_							:					Tuple    ,							A_							:					Optional[Any]       ):
						snake_case       :     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_							:					int       ):
						snake_case       :     int =    ['''0''', '''1''', '''2''', '''3''']
						snake_case       :     Optional[int] =    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_							:					List[str]       ):
						snake_case       :     Union[str, Any] =    ['''0''', '''1''', '''2''', '''3''']
						snake_case       :     Optional[int] =    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[int]       ):
						snake_case       :     Optional[int] =    ['''0''', '''1''', '''2''', '''3''']
						snake_case       :     str =    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_							:					Optional[Any]    ,							A_							:					Any    ,							A_							:					Union[str, Any]       ):
						snake_case       :     Dict =    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_							:					List[str]    ,							A_							:					Union[str, Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     List[Any] =    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_							:					Any    ,							A_							:					Union[str, Any]    ,							A_							:					str       ):
						snake_case       :     List[Any] =    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]       ):
						snake_case       :     Any =    '''\n'''.join(['''First''', '''Second\u2029with Unicode new line''', '''Third''']       )
						snake_case       :     Optional[int] =    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 (       ):
						return os.path.join('''tests'''    ,							'''features'''    ,							'''data'''    ,							'''test_image_rgb.jpg'''       )

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

@pytest.fixture(scope='''session'''       )
def      A (       A_							:					Optional[Any]    ,							A_							:					Optional[Any]       ):
						snake_case       :     Union[str, 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_							:					Optional[Any]       ):
						snake_case       :     Dict =    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

