
'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class     a (					__magic_name__     ):
				_snake_case		     =			'''AutoFeatureExtractor'''
				_snake_case		     =			'''Speech2Text2Tokenizer'''




				def __init__(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Dict   ):
										super().__init__(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     self.feature_extractor
										snake_case							:  int			=     False




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

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

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




				def 						__snake_case				(				self						:					List[str],     *SCREAMING_SNAKE_CASE_						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Dict   ):
										return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     *SCREAMING_SNAKE_CASE_						:					Optional[int],     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_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.'''   )
										snake_case							:  Union[str, Any]			=     True
										snake_case							:  List[str]			=     self.tokenizer
										yield
										snake_case							:  Optional[int]			=     self.feature_extractor
										snake_case							:  Tuple			=     False






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__     : Dict				=   {"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[Any]				=   ["""DetrFeatureExtractor"""]
							SCREAMING_SNAKE_CASE__     : str				=   ["""DetrImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   [
							    """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DetrForObjectDetection""",
							    """DetrForSegmentation""",
							    """DetrModel""",
							    """DetrPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
