
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	"AutoFeatureExtractor"
		_lowerCAmelCase		=	"Speech2Text2Tokenizer"





		def __init__(							self    :	str						,  lowerCamelCase__    :	Union[str, Any]						,  lowerCamelCase__    :	str				):
							super().__init__(lowerCamelCase__						,  lowerCamelCase__				)
							lowerCAmelCase		:					Dict  	=     self.feature_extractor
							lowerCAmelCase		:					Tuple  	=     False





		def __call__(							self    :	Optional[int]						,  *lowerCamelCase__    :	str						,  **lowerCamelCase__    :	str				):
							# For backward compatibility
							if self._in_target_context_manager:
												return self.current_processor(*lowerCamelCase__						,  **lowerCamelCase__				)

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

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												lowerCAmelCase		:					Union[str, Any]  	=     encodings['''input_ids''']
												return inputs





		def       _A							(							self    :	int						,  *lowerCamelCase__    :	Union[str, Any]						,  **lowerCamelCase__    :	Union[str, Any]				):
							return self.tokenizer.batch_decode(*lowerCamelCase__						,  **lowerCamelCase__				)





		def       _A							(							self    :	List[str]						,  *lowerCamelCase__    :	Optional[int]						,  **lowerCamelCase__    :	Union[str, Any]				):
							return self.tokenizer.decode(*lowerCamelCase__						,  **lowerCamelCase__				)





		@contextmanager
		def       _A							(							self    :	Any				):
							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.'''				)
							lowerCAmelCase		:					str  	=     True
							lowerCAmelCase		:					List[str]  	=     self.tokenizer
							yield
							lowerCAmelCase		:					Dict  	=     self.feature_extractor
							lowerCAmelCase		:					int  	=     False


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
