

import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCAmelCase__										= {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	if got_ver is None or want_ver is None:
		raise ValueError(
		    f"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
		    f""" reinstalling {pkg}."""  )
	if not ops[op](version.parse(UpperCamelCase  )   ,version.parse(UpperCamelCase  )  ):
		raise ImportError(
		    f"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = None  )    ->    None:
	UpperCAmelCase_		:      str       	=					f"""\n{hint}""" if hint is not None else ''

	# non-versioned check
	if re.match(r'^[\w_\-\d]+$'   ,UpperCamelCase  ):
		UpperCAmelCase_		:      Optional[int]       	=					requirement, None, None
	else:
		UpperCAmelCase_		:      Any       	=					re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'   ,UpperCamelCase  )
		if not match:
			raise ValueError(
			    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
			    f""" got {requirement}"""  )
		UpperCAmelCase_		:      List[str]       	=					match[0]
		UpperCAmelCase_		:      Tuple       	=					want_full.split(','  )  # there could be multiple requirements
		UpperCAmelCase_		:      Union[str, Any]       	=					{}
		for w in want_range:
			UpperCAmelCase_		:      Union[str, Any]       	=					re.findall(r'^([\s!=<>]{1,2})(.+)'   ,UpperCamelCase  )
			if not match:
				raise ValueError(
				    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
				    f""" but got {requirement}"""  )
			UpperCAmelCase_		:      List[str]       	=					match[0]
			UpperCAmelCase_		:      Optional[int]       	=					want_ver
			if op not in ops:
				raise ValueError(f"""{requirement}: need one of {list(ops.keys()  )}, but got {op}"""  )

    # special case
	if pkg == "python":
		UpperCAmelCase_		:      Optional[Any]       	=					'.'.join([str(UpperCamelCase  ) for x in sys.version_info[:3]]  )
		for op, want_ver in wanted.items():
			_compare_versions(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
		return

	# check if any version is installed
	try:
		UpperCAmelCase_		:      List[str]       	=					importlib.metadata.version(UpperCamelCase  )
	except importlib.metadata.PackageNotFoundError:
		raise importlib.metadata.PackageNotFoundError(
		    f"""The '{requirement}' distribution was not found and is required by this application. {hint}"""  )

	# check that the right version is installed if version number or a range was provided
	if want_ver is not None:
		for op, want_ver in wanted.items():
			_compare_versions(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	UpperCAmelCase_		:      List[str]       	=					'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
	return require_version(UpperCamelCase   ,UpperCamelCase  )









'''simple docstring'''


import warnings
from contextlib import contextmanager

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



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "Wav2Vec2FeatureExtractor"
							_lowerCamelCase		:      Any=   "AutoTokenizer"


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      int       	=					self.feature_extractor
								UpperCAmelCase_		:      str       	=					False


							@classmethod
							def 						_snake_case							(			cls    ,			_snake_case    ,			**_snake_case)							->				List[Any]:
								try:
									return super().from_pretrained(_snake_case    ,			**_snake_case)
								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: '    ,			_snake_case    ,			)

									UpperCAmelCase_		:      Any       	=					WavaVecaFeatureExtractor.from_pretrained(_snake_case    ,			**_snake_case)
									UpperCAmelCase_		:      List[str]       	=					WavaVecaCTCTokenizer.from_pretrained(_snake_case    ,			**_snake_case)

									return cls(feature_extractor=_snake_case    ,			tokenizer=_snake_case)


							def __call__(			self    ,			*_snake_case    ,			**_snake_case)							->				Optional[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor(*_snake_case    ,			**_snake_case)

								if "raw_speech" in kwargs:
									warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.')
									UpperCAmelCase_		:      List[str]       	=					kwargs.pop('raw_speech')
								else:
									UpperCAmelCase_		:      Dict       	=					kwargs.pop('audio'    ,			_snake_case)
								UpperCAmelCase_		:      str       	=					kwargs.pop('sampling_rate'    ,			_snake_case)
								UpperCAmelCase_		:      List[str]       	=					kwargs.pop('text'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      List[str]       	=					args[0]
									UpperCAmelCase_		:      Tuple       	=					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:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.feature_extractor(_snake_case    ,			*_snake_case    ,			sampling_rate=_snake_case    ,			**_snake_case)
								if text is not None:
									UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer(_snake_case    ,			**_snake_case)

								if text is None:
									return inputs
								elif audio is None:
									return encodings
								else:
									UpperCAmelCase_		:      List[str]       	=					encodings['input_ids']
									return inputs


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				str:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor.pad(*_snake_case    ,			**_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					kwargs.pop('input_features'    ,			_snake_case)
								UpperCAmelCase_		:      Dict       	=					kwargs.pop('labels'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      Optional[Any]       	=					args[0]
									UpperCAmelCase_		:      int       	=					args[1:]

								if input_features is not None:
									UpperCAmelCase_		:      Dict       	=					self.feature_extractor.pad(_snake_case    ,			*_snake_case    ,			**_snake_case)
								if labels is not None:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.tokenizer.pad(_snake_case    ,			**_snake_case)

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					labels['input_ids']
									return input_features


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Dict:
								return self.tokenizer.batch_decode(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Tuple:
								return self.tokenizer.decode(*_snake_case    ,			**_snake_case)





							@contextmanager
							def 						_snake_case							(			self)							->				Tuple:
								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.')
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      Tuple       	=					self.tokenizer
								yield
								UpperCAmelCase_		:      Tuple       	=					self.feature_extractor
								UpperCAmelCase_		:      Tuple       	=					False






