import warnings
from contextlib import contextmanager

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



class 			SCREAMING_SNAKE_CASE_			(       _a      ):
  '''simple docstring'''


  lowercase		:  Dict							  =					"""Wav2Vec2FeatureExtractor"""
  lowercase		:  Tuple							  =					"""AutoTokenizer"""







  def __init__(					self     :       str   ,						SCREAMING_SNAKE_CASE__     :       List[Any]   ,						SCREAMING_SNAKE_CASE__     :       str  )     ->					Tuple:
         super().__init__(SCREAMING_SNAKE_CASE__   ,						SCREAMING_SNAKE_CASE__  )
         A    :  Any								=self.feature_extractor
         A    :  Dict								=False







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(					cls     :       Tuple   ,						SCREAMING_SNAKE_CASE__     :       str   ,						**SCREAMING_SNAKE_CASE__     :       Any  )     ->					Union[str, Any]:

         try:
                return super().from_pretrained(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_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: '   ,						SCREAMING_SNAKE_CASE__   ,						)

                A    :  Optional[Any]								=WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )
                A    :  int								=WavaVecaCTCTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

                return cls(feature_extractor=SCREAMING_SNAKE_CASE__   ,						tokenizer=SCREAMING_SNAKE_CASE__  )







  def __call__(					self     :       int   ,						*SCREAMING_SNAKE_CASE__     :       Optional[int]   ,						**SCREAMING_SNAKE_CASE__     :       Tuple  )     ->					List[Any]:

         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.'  )
                A    :  Any								=kwargs.pop('raw_speech'  )
         else:
                A    :  Union[str, Any]								=kwargs.pop('audio'   ,						SCREAMING_SNAKE_CASE__  )
         A    :  int								=kwargs.pop('sampling_rate'   ,						SCREAMING_SNAKE_CASE__  )
         A    :  Optional[int]								=kwargs.pop('text'   ,						SCREAMING_SNAKE_CASE__  )
         if len(SCREAMING_SNAKE_CASE__  ) > 0:
                A    :  Optional[int]								=args[0]
                A    :  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:
                A    :  Union[str, Any]								=self.feature_extractor(SCREAMING_SNAKE_CASE__   ,						*SCREAMING_SNAKE_CASE__   ,						sampling_rate=SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )
         if text is not None:
                A    :  List[str]								=self.tokenizer(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

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







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[int]   ,						*SCREAMING_SNAKE_CASE__     :       List[str]   ,						**SCREAMING_SNAKE_CASE__     :       Any  )     ->					int:

         if self._in_target_context_manager:
                return self.current_processor.pad(*SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

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

         if input_features is not None:
                A    :  Optional[int]								=self.feature_extractor.pad(SCREAMING_SNAKE_CASE__   ,						*SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )
         if labels is not None:
                A    :  Dict								=self.tokenizer.pad(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

         if labels is None:
                return input_features
         elif input_features is None:
                return labels
         else:
                A    :  Any								=labels['input_ids']
                return input_features







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Optional[Any]   ,						*SCREAMING_SNAKE_CASE__     :       str   ,						**SCREAMING_SNAKE_CASE__     :       int  )     ->					List[Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )







  def 		SCREAMING_SNAKE_CASE_		(					self     :       Dict   ,						*SCREAMING_SNAKE_CASE__     :       str   ,						**SCREAMING_SNAKE_CASE__     :       List[Any]  )     ->					Union[str, Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )







  @contextmanager
  def 		SCREAMING_SNAKE_CASE_		(					self     :       int  )     ->					Optional[int]:
         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    :  Optional[int]								=True
         A    :  int								=self.tokenizer
         yield
         A    :  Any								=self.feature_extractor
         A    :  List[Any]								=False










from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


