
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__a         =   {
    'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
    'feature_extraction_wav2vec2': ['Wav2Vec2FeatureExtractor'],
    'processing_wav2vec2': ['Wav2Vec2Processor'],
    'tokenization_wav2vec2': ['Wav2Vec2CTCTokenizer', 'Wav2Vec2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Wav2Vec2ForAudioFrameClassification',
						    'Wav2Vec2ForCTC',
						    'Wav2Vec2ForMaskedLM',
						    'Wav2Vec2ForPreTraining',
						    'Wav2Vec2ForSequenceClassification',
						    'Wav2Vec2ForXVector',
						    'Wav2Vec2Model',
						    'Wav2Vec2PreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFWav2Vec2ForCTC',
						    'TFWav2Vec2Model',
						    'TFWav2Vec2PreTrainedModel',
						    'TFWav2Vec2ForSequenceClassification',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'FlaxWav2Vec2ForCTC',
						    'FlaxWav2Vec2ForPreTraining',
						    'FlaxWav2Vec2Model',
						    'FlaxWav2Vec2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
						from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
						from .processing_wavaveca import WavaVecaProcessor
						from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavaveca import (
												    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavaVecaForAudioFrameClassification,
												    WavaVecaForCTC,
												    WavaVecaForMaskedLM,
												    WavaVecaForPreTraining,
												    WavaVecaForSequenceClassification,
												    WavaVecaForXVector,
												    WavaVecaModel,
												    WavaVecaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFWavaVecaForCTC,
												    TFWavaVecaForSequenceClassification,
												    TFWavaVecaModel,
												    TFWavaVecaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_wavaveca import (
												    FlaxWavaVecaForCTC,
												    FlaxWavaVecaForPreTraining,
												    FlaxWavaVecaModel,
												    FlaxWavaVecaPreTrainedModel,
												)


else:
						import sys

						__a         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


__a         =   namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 							a		(				snake_case__:   Dict		,  snake_case__:   List[str]   ):


				'''simple docstring'''
				return (abs(source - target   ) / target) < 0.0_1




@pytest.mark.integration
def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   _TestCommandArgs(dataset=snake_case__		,  all_configs=snake_case__		,  save_infos=snake_case__   )
				lowercase_	    =   TestCommand(*snake_case__   )
				test_command.run()
				lowercase_	    =   os.path.join(snake_case__		,  '''README.md'''   )
				assert os.path.exists(snake_case__   )
				lowercase_	    =   DatasetInfosDict.from_directory(snake_case__   )
				lowercase_	    =   DatasetInfosDict(
				    {
				        '''default''': DatasetInfo(
				            features=Features(
				                {
				                    '''tokens''': Sequence(Value('''string'''   )   ),
				                    '''ner_tags''': Sequence(
				                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']   )   ),
				                    '''langs''': Sequence(Value('''string'''   )   ),
				                    '''spans''': Sequence(Value('''string'''   )   ),
				                }   )		,  splits=[
				                {
				                    '''name''': '''train''',
				                    '''num_bytes''': 2_351_563,
				                    '''num_examples''': 10_000,
				                },
				                {
				                    '''name''': '''validation''',
				                    '''num_bytes''': 238_418,
				                    '''num_examples''': 1_000,
				                },
				            ]		,  download_size=3_940_680		,  dataset_size=2_589_981		,  )
				    }   )
				assert dataset_infos.keys() == expected_dataset_infos.keys()
				for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
								lowercase_    ,      lowercase_	    =   getattr(dataset_infos['''default''']		,  snake_case__   ), getattr(expected_dataset_infos['''default''']		,  snake_case__   )
								if key == "num_bytes":
												assert is_apercent_close(snake_case__		,  snake_case__   )
								elif key == "splits":
												assert list(snake_case__   ) == list(snake_case__   )
												for split in result:
																assert result[split].name == expected[split].name
																assert result[split].num_examples == expected[split].num_examples
																assert is_apercent_close(result[split].num_bytes		,  expected[split].num_bytes   )
								else:
												result == expected




