




from typing import TYPE_CHECKING

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


UpperCAmelCase__						:       List[str]							=    {
    """configuration_oneformer""": ["""ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OneFormerConfig"""],
    """processing_oneformer""": ["""OneFormerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Dict							=    ["""OneFormerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       """ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """OneFormerForUniversalSegmentation""",
       """OneFormerModel""",
       """OneFormerPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
   from .processing_oneformer import OneFormerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_oneformer import OneFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_oneformer import (
          ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          OneFormerForUniversalSegmentation,
          OneFormerModel,
          OneFormerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:       Dict							=    _LazyModule(__name__, globals()['__file__'], _import_structure)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"AutoFeatureExtractor"
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"Speech2Text2Tokenizer"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Dict:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    self.feature_extractor
     _lowercase: int						     =    False






   def __call__(	self   :       List[Any]						,					*UpperCamelCase_   :       Dict						,					**UpperCamelCase_   :       Optional[int]     )->			str:
     # 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.'     )
       _lowercase: Tuple						     =    kwargs.pop('raw_speech'     )
     else:
       _lowercase: Optional[Any]						     =    kwargs.pop('audio'						,					UpperCamelCase_     )
     _lowercase: int						     =    kwargs.pop('sampling_rate'						,					UpperCamelCase_     )
     _lowercase: Dict						     =    kwargs.pop('text'						,					UpperCamelCase_     )
     if len(UpperCamelCase_     ) > 0:
       _lowercase: List[Any]						     =    args[0]
       _lowercase: 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:
       _lowercase: Optional[int]						     =    self.feature_extractor(UpperCamelCase_						,					*UpperCamelCase_						,					sampling_rate=UpperCamelCase_						,					**UpperCamelCase_     )
     if text is not None:
       _lowercase: str						     =    self.tokenizer(UpperCamelCase_						,					**UpperCamelCase_     )

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






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       Tuple						,					**UpperCamelCase_   :       Dict     )->			List[str]:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					*UpperCamelCase_   :       List[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @contextmanager
   def  _a						(	self   :       Tuple     )->			Union[str, 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.'     )
     _lowercase: Any						     =    True
     _lowercase: Optional[Any]						     =    self.tokenizer
     yield
     _lowercase: Union[str, Any]						     =    self.feature_extractor
     _lowercase: str						     =    False
