



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Union[str, Any]       =						"AutoFeatureExtractor"
   A__      : List[str]       =						"Speech2Text2Tokenizer"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Dict							)	->      str:
       super().__init__(lowerCamelCase_  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					self.feature_extractor
       UpperCAmelCase_     :       Dict  		=					False


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

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


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


   def       A__    (    self:     str  ,*lowerCamelCase_:     Any  ,**lowerCamelCase_:     List[Any]							)	->      List[str]:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)






   @contextmanager
   def       A__    (    self:     Dict							)	->      Optional[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."""							)
       UpperCAmelCase_     :       int  		=					True
       UpperCAmelCase_     :       Tuple  		=					self.tokenizer
       yield
       UpperCAmelCase_     :       Tuple  		=					self.feature_extractor
       UpperCAmelCase_     :       Dict  		=					False





from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
      import detectrona







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"layoutlmv2"


   def __init__(    self:     Tuple  ,lowerCamelCase_:     Dict=30522  ,lowerCamelCase_:     str=768  ,lowerCamelCase_:     Any=12  ,lowerCamelCase_:     Tuple=12  ,lowerCamelCase_:     int=3072  ,lowerCamelCase_:     int="gelu"  ,lowerCamelCase_:     Dict=0.1  ,lowerCamelCase_:     List[Any]=0.1  ,lowerCamelCase_:     List[Any]=512  ,lowerCamelCase_:     Tuple=2  ,lowerCamelCase_:     Dict=0.0_2  ,lowerCamelCase_:     Dict=1e-12  ,lowerCamelCase_:     Dict=0  ,lowerCamelCase_:     List[str]=1024  ,lowerCamelCase_:     Optional[Any]=128  ,lowerCamelCase_:     List[Any]=32  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     Dict=256  ,lowerCamelCase_:     int=64  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Tuple=[7, 7, 256]  ,lowerCamelCase_:     Union[str, Any]=128  ,lowerCamelCase_:     Any=128  ,lowerCamelCase_:     int=True  ,lowerCamelCase_:     Union[str, Any]=True  ,lowerCamelCase_:     Optional[int]=False  ,lowerCamelCase_:     List[str]=None  ,**lowerCamelCase_:     str  ,)	->      Any:
       super().__init__(
           vocab_size=lowerCamelCase_  ,hidden_size=lowerCamelCase_  ,num_hidden_layers=lowerCamelCase_  ,num_attention_heads=lowerCamelCase_  ,intermediate_size=lowerCamelCase_  ,hidden_act=lowerCamelCase_  ,hidden_dropout_prob=lowerCamelCase_  ,attention_probs_dropout_prob=lowerCamelCase_  ,max_position_embeddings=lowerCamelCase_  ,type_vocab_size=lowerCamelCase_  ,initializer_range=lowerCamelCase_  ,layer_norm_eps=lowerCamelCase_  ,pad_token_id=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       Optional[int]  		=					max_ad_position_embeddings
       UpperCAmelCase_     :       Optional[int]  		=					max_rel_pos
       UpperCAmelCase_     :       Optional[int]  		=					rel_pos_bins
       UpperCAmelCase_     :       List[str]  		=					fast_qkv
       UpperCAmelCase_     :       int  		=					max_rel_ad_pos
       UpperCAmelCase_     :       int  		=					rel_ad_pos_bins
       UpperCAmelCase_     :       Optional[Any]  		=					convert_sync_batchnorm
       UpperCAmelCase_     :       str  		=					image_feature_pool_shape
       UpperCAmelCase_     :       List[Any]  		=					coordinate_size
       UpperCAmelCase_     :       int  		=					shape_size
       UpperCAmelCase_     :       Optional[Any]  		=					has_relative_attention_bias
       UpperCAmelCase_     :       str  		=					has_spatial_attention_bias
       UpperCAmelCase_     :       Tuple  		=					has_visual_segment_embedding
       UpperCAmelCase_     :       Optional[Any]  		=					(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )


   @classmethod
   def       A__    (    self:     Any							)	->      Dict:
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }






   def       A__    (    self:     Tuple							)	->      int:
       UpperCAmelCase_     :       Any  		=					detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
           UpperCAmelCase_     :       Optional[int]  		=					k.split("""."""							)
           UpperCAmelCase_     :       List[str]  		=					detectrona_config
           for attribute in attributes[:-1]:
               UpperCAmelCase_     :       Optional[Any]  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)
           setattr(lowerCamelCase_  ,attributes[-1]  ,lowerCamelCase_							)

       return detectrona_config
