


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class snake_case__			(    __SCREAMING_SNAKE_CASE					):
       _lowerCAmelCase   =['''image_processor''', '''tokenizer''']
       _lowerCAmelCase   ='''AutoImageProcessor'''
       _lowerCAmelCase   ='''AutoTokenizer'''




       def __init__( self				:    Optional[Any]    ,				_lowerCamelCase				:    Union[str, Any]=None    ,				_lowerCamelCase				:    Dict=None    ,				**_lowerCamelCase				:    Optional[Any]			):
              snake_case__ :       List[Any]	   =							None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                         ' instead.'    ,				__lowerCamelCase    ,				)
                     snake_case__ :       List[Any]	   =							kwargs.pop('feature_extractor'			)

              snake_case__ :       int	   =							image_processor if image_processor is not None else feature_extractor
              if image_processor is None:
                     raise ValueError('You need to specify an `image_processor`.'			)
              if tokenizer is None:
                     raise ValueError('You need to specify a `tokenizer`.'			)

              super().__init__(__lowerCamelCase    ,				__lowerCamelCase			)
              snake_case__ :       List[str]	   =							self.image_processor
              snake_case__ :       Union[str, Any]	   =							False




       def __call__( self				:    Union[str, Any]    ,				*_lowerCamelCase				:    Optional[int]    ,				**_lowerCamelCase				:    Optional[Any]			):
              # For backward compatibility
              if self._in_target_context_manager:
                     return self.current_processor(*__lowerCamelCase    ,				**__lowerCamelCase			)

              snake_case__ :       Tuple	   =							kwargs.pop('images'    ,				__lowerCamelCase			)
              snake_case__ :       int	   =							kwargs.pop('text'    ,				__lowerCamelCase			)
              if len(__lowerCamelCase			) > 0:
                     snake_case__ :       Dict	   =							args[0]
                     snake_case__ :       Tuple	   =							args[1:]

              if images is None and text is None:
                     raise ValueError('You need to specify either an `images` or `text` input to process.'			)

              if images is not None:
                     snake_case__ :       Dict	   =							self.image_processor(__lowerCamelCase    ,				*__lowerCamelCase    ,				**__lowerCamelCase			)
              if text is not None:
                     snake_case__ :       Any	   =							self.tokenizer(__lowerCamelCase    ,				**__lowerCamelCase			)

              if text is None:
                     return inputs
              elif images is None:
                     return encodings
              else:
                     snake_case__ :       Optional[Any]	   =							encodings['input_ids']
                     return inputs




       def     UpperCAmelCase__    ( self				:    int    ,				*_lowerCamelCase				:    Union[str, Any]    ,				**_lowerCamelCase				:    Dict			):
              return self.tokenizer.batch_decode(*__lowerCamelCase    ,				**__lowerCamelCase			)




       def     UpperCAmelCase__    ( self				:    str    ,				*_lowerCamelCase				:    Dict    ,				**_lowerCamelCase				:    Union[str, Any]			):
              return self.tokenizer.decode(*__lowerCamelCase    ,				**__lowerCamelCase			)




       @contextmanager
       def     UpperCAmelCase__    ( self				:    List[str]			):
              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 images inputs, or in a separate call.'			)
              snake_case__ :       List[Any]	   =							True
              snake_case__ :       Optional[int]	   =							self.tokenizer
              yield
              snake_case__ :       List[Any]	   =							self.image_processor
              snake_case__ :       Dict	   =							False




       def     UpperCAmelCase__    ( self				:    int    ,				_lowerCamelCase				:    List[str]    ,				_lowerCamelCase				:    Union[str, Any]=False    ,				_lowerCamelCase				:    Optional[int]=None			):
              if added_vocab is None:
                     snake_case__ :       Tuple	   =							self.tokenizer.get_added_vocab()

              snake_case__ :       List[str]	   =							{}

              while tokens:
                     snake_case__ :       Union[str, Any]	   =							re.search(r'<s_(.*?)>'    ,				__lowerCamelCase    ,				re.IGNORECASE			)
                     if start_token is None:
                            break
                     snake_case__ :       str	   =							start_token.group(1			)
                     snake_case__ :       Dict	   =							re.search(rF'''</s_{key}>'''    ,				__lowerCamelCase    ,				re.IGNORECASE			)
                     snake_case__ :       Optional[Any]	   =							start_token.group()
                     if end_token is None:
                            snake_case__ :       Optional[int]	   =							tokens.replace(__lowerCamelCase    ,				''			)
                     else:
                            snake_case__ :       Optional[int]	   =							end_token.group()
                            snake_case__ :       Tuple	   =							re.escape(__lowerCamelCase			)
                            snake_case__ :       Optional[int]	   =							re.escape(__lowerCamelCase			)
                            snake_case__ :       Tuple	   =							re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''    ,				__lowerCamelCase    ,				re.IGNORECASE			)
                            if content is not None:
                                   snake_case__ :       Optional[Any]	   =							content.group(1			).strip()
                                   if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                          snake_case__ :       Optional[Any]	   =							self.tokenajson(__lowerCamelCase    ,				is_inner_value=__lowerCamelCase    ,				added_vocab=__lowerCamelCase			)
                                          if value:
                                                 if len(__lowerCamelCase			) == 1:
                                                        snake_case__ :       Optional[Any]	   =							value[0]
                                                 snake_case__ :       Optional[int]	   =							value
                                   else:  # leaf nodes
                                          snake_case__ :       List[Any]	   =							[]
                                          for leaf in content.split(r'<sep/>'			):
                                                 snake_case__ :       List[Any]	   =							leaf.strip()
                                                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                        snake_case__ :       int	   =							leaf[1:-2]  # for categorical special tokens
                                                 output[key].append(__lowerCamelCase			)
                                          if len(output[key]			) == 1:
                                                 snake_case__ :       int	   =							output[key][0]

                            snake_case__ :       Dict	   =							tokens[tokens.find(__lowerCamelCase			) + len(__lowerCamelCase			) :].strip()
                            if tokens[:6] == r"<sep/>":  # non-leaf nodes
                                   return [output] + self.tokenajson(tokens[6:]    ,				is_inner_value=__lowerCamelCase    ,				added_vocab=__lowerCamelCase			)

              if len(__lowerCamelCase			):
                     return [output] if is_inner_value else output
              else:
                     return [] if is_inner_value else {"text_sequence": tokens}




       @property
       def     UpperCAmelCase__    ( self				:    Union[str, Any]			):
              warnings.warn(
                  '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				__lowerCamelCase    ,				)
              return self.image_processor_class




       @property
       def     UpperCAmelCase__    ( self				:    List[str]			):
              warnings.warn(
                  '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				__lowerCamelCase    ,				)
              return self.image_processor










"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      root / filename
      for filename in [
          '''vision.cpp''',
          os.path.join('''cpu'''  ,						'''ms_deform_attn_cpu.cpp'''	),
          os.path.join('''cuda'''  ,						'''ms_deform_attn_cuda.cu'''	),
      ]
  ]

  load(
      '''MultiScaleDeformableAttention'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						extra_cflags=['''-DWITH_CUDA=1''']  ,						extra_cuda_cflags=[
          '''-DCUDA_HAS_FP16=1''',
          '''-D__CUDA_NO_HALF_OPERATORS__''',
          '''-D__CUDA_NO_HALF_CONVERSIONS__''',
          '''-D__CUDA_NO_HALF2_OPERATORS__''',
      ]  ,						)

  import MultiScaleDeformableAttention as MSDA

  return MSDA





