
import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__snake_case								=  """\
    Text data.
    Second line of data."""

__snake_case								=  """file"""






@pytest.fixture(scope='session'		)
def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= tmp_path_factory.mktemp('data'		) / (FILE_PATH + '.zstd')
					SCREAMING_SNAKE_CASE__													= bytes(UpperCamelCase_	,	'utf-8'		)
					with zstd.open(UpperCamelCase_	,	'wb'		) as f:
										f.write(UpperCamelCase_		)
					return path






@pytest.fixture
def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


					with open(os.path.join(tmpfs.local_root_dir	,	UpperCamelCase_		)	,	'w'		) as f:
										f.write(UpperCamelCase_		)
					return FILE_PATH






@pytest.mark.parametrize('compression_format'	,	['gzip', 'xz', 'zstd']		)
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
					SCREAMING_SNAKE_CASE__													= input_paths[compression_format]
					SCREAMING_SNAKE_CASE__													= tmp_path / 'cache'
					SCREAMING_SNAKE_CASE__													= DownloadConfig(cache_dir=UpperCamelCase_	,	extract_compressed_file=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= cached_path(UpperCamelCase_	,	download_config=UpperCamelCase_		)
					with open(UpperCamelCase_		) as f:
										SCREAMING_SNAKE_CASE__													= f.read()
					with open(UpperCamelCase_		) as f:
										SCREAMING_SNAKE_CASE__													= f.read()
					assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('default_extracted'	,	[True, False]		)
@pytest.mark.parametrize('default_cache_dir'	,	[True, False]		)
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 'custom_cache'
					SCREAMING_SNAKE_CASE__													= 'custom_extracted_dir'
					SCREAMING_SNAKE_CASE__													= tmp_path / 'custom_extracted_path'
					if default_extracted:
										SCREAMING_SNAKE_CASE__													= ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
					else:
										monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'	,	UpperCamelCase_		)
										monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'	,	str(UpperCamelCase_		)		)
										SCREAMING_SNAKE_CASE__													= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					SCREAMING_SNAKE_CASE__													= xz_file
					SCREAMING_SNAKE_CASE__													= (
					    DownloadConfig(extract_compressed_file=UpperCamelCase_		)
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir	,	extract_compressed_file=UpperCamelCase_		)
					)
					SCREAMING_SNAKE_CASE__													= cached_path(UpperCamelCase_	,	download_config=UpperCamelCase_		)
					assert Path(UpperCamelCase_		).parent.parts[-2:] == expected






def 							_lowercase							(     UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= str(Path(UpperCamelCase_		).resolve()		)
					assert cached_path(UpperCamelCase_		) == text_file
					# relative path
					SCREAMING_SNAKE_CASE__													= str(Path(UpperCamelCase_		).resolve().relative_to(Path(os.getcwd()		)		)		)
					assert cached_path(UpperCamelCase_		) == text_file






def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= str(tmp_path.resolve() / '__missing_file__.txt'		)
					with pytest.raises(UpperCamelCase_		):
										cached_path(UpperCamelCase_		)
					# relative path
					SCREAMING_SNAKE_CASE__													= './__missing_file__.txt'
					with pytest.raises(UpperCamelCase_		):
										cached_path(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= get_from_cache(F'tmp://{tmpfs_file}'		)
					with open(UpperCamelCase_		) as f:
										SCREAMING_SNAKE_CASE__													= f.read()
					assert output_file_content == FILE_CONTENT






@patch('datasets.config.HF_DATASETS_OFFLINE'	,	UpperCamelCase_		)
def 							_lowercase							(     )		->		Union[str, Any]:
					'''simple docstring'''


					with pytest.raises(UpperCamelCase_		):
										cached_path('https://huggingface.co'		)






@patch('datasets.config.HF_DATASETS_OFFLINE'	,	UpperCamelCase_		)
def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= tmp_path_factory.mktemp('data'		) / 'file.html'
					with pytest.raises(UpperCamelCase_		):
										http_get('https://huggingface.co'	,	temp_file=UpperCamelCase_		)
					with pytest.raises(UpperCamelCase_		):
										http_head('https://huggingface.co'		)






@patch('datasets.config.HF_DATASETS_OFFLINE'	,	UpperCamelCase_		)
def 							_lowercase							(     UpperCamelCase_		)		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= tmp_path_factory.mktemp('data'		) / 'file.html'
					with pytest.raises(UpperCamelCase_		):
										ftp_get('ftp://huggingface.co'	,	temp_file=UpperCamelCase_		)
					with pytest.raises(UpperCamelCase_		):
										ftp_head('ftp://huggingface.co'		)






@patch('datasets.config.HF_DATASETS_OFFLINE'	,	UpperCamelCase_		)
def 							_lowercase							(     UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= tmp_path_factory.mktemp('data'		) / 'file.html'
					with pytest.raises(UpperCamelCase_		):
										fsspec_get('s3://huggingface.co'	,	temp_file=UpperCamelCase_		)
					with pytest.raises(UpperCamelCase_		):
										fsspec_head('s3://huggingface.co'		)







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Union[str, Any]				       ="""Wav2Vec2FeatureExtractor"""
	A__    :   Any				       ="""AutoTokenizer"""
	def __init__(			self							:   List[Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   str							):
						super().__init__(UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False
	@classmethod
	def      A_				(			cls							:   Union[str, Any]       ,   UpperCAmelCase_							:   Any       ,   **UpperCAmelCase_							:   Optional[Any]							):
						try:
											return super().from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						except OSError:
											warnings.warn(
											    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '       ,   UpperCAmelCase_       ,   )

											SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)

											return cls(feature_extractor=UpperCAmelCase_       ,   tokenizer=UpperCAmelCase_							)
	def __call__(			self							:   Any       ,   *UpperCAmelCase_							:   Dict       ,   **UpperCAmelCase_							:   Any							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
											SCREAMING_SNAKE_CASE__													= kwargs.pop('raw_speech'							)
						else:
											SCREAMING_SNAKE_CASE__													= kwargs.pop('audio'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('sampling_rate'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('text'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'							)

						if audio is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   sampling_rate=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if text is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											SCREAMING_SNAKE_CASE__													= encodings['input_ids']
											return inputs
	def      A_				(			self							:   List[str]       ,   *UpperCAmelCase_							:   List[Any]       ,   **UpperCAmelCase_							:   Optional[int]							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor.pad(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= kwargs.pop('input_features'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('labels'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if input_features is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor.pad(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if labels is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.pad(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											SCREAMING_SNAKE_CASE__													= labels['input_ids']
											return input_features
	def      A_				(			self							:   List[Any]       ,   *UpperCAmelCase_							:   List[str]       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.batch_decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Tuple       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)





	@contextmanager
	def      A_				(			self							:   str							):
						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your audio inputs, or in a separate call.'							)
						SCREAMING_SNAKE_CASE__													= True
						SCREAMING_SNAKE_CASE__													= self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False





