


"""simple docstring"""




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE					=						{"""configuration_cvt""": ["""CVT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CvtConfig"""]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE					=						[
			    """CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """CvtForImageClassification""",
			    """CvtModel""",
			    """CvtPreTrainedModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE					=						[
			    """TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFCvtForImageClassification""",
			    """TFCvtModel""",
			    """TFCvtPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_cvt import (
						    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CvtForImageClassification,
						    CvtModel,
						    CvtPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_cvt import (
						    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCvtForImageClassification,
						    TFCvtModel,
						    TFCvtPreTrainedModel,
						)


else:
			import sys

			__SCREAMING_SNAKE_CASE					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
   lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'




   def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
          super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
          _a          =				self.feature_extractor
          _a          =				False




   def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
          # 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."       )
                 _a          =				kwargs.pop("raw_speech"       )
          else:
                 _a          =				kwargs.pop("audio"				,  UpperCamelCase__       )
          _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
          _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
          if len(UpperCamelCase__       ) > 0:
                 _a          =				args[0]
                 _a          =				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:
                 _a          =				self.feature_extractor(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
          if text is not None:
                 _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )

          if text is None:
                 return inputs
          elif audio is None:
                 return encodings
          else:
                 _a          =				encodings["input_ids"]
                 return inputs




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
          return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
          return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )




   @contextmanager
   def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
          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."       )
          _a          =				True
          _a          =				self.tokenizer
          yield
          _a          =				self.feature_extractor
          _a          =				False
