




'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a_      :		Dict							      =       logging.get_logger(__name__)

a_      :		str							      =       {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class   __UpperCamelCase       ( lowerCamelCase__				):
       lowercase	:    Tuple      						='dpt'






       def __init__(							self,		lowerCAmelCase=768,		lowerCAmelCase=12,		lowerCAmelCase=12,		lowerCAmelCase=3_072,		lowerCAmelCase="gelu",		lowerCAmelCase=0.0,		lowerCAmelCase=0.0,		lowerCAmelCase=0.0_2,		lowerCAmelCase=1e-12,		lowerCAmelCase=384,		lowerCAmelCase=16,		lowerCAmelCase=3,		lowerCAmelCase=False,		lowerCAmelCase=True,		lowerCAmelCase=[2, 5, 8, 11],		lowerCAmelCase="project",		lowerCAmelCase=[4, 2, 1, 0.5],		lowerCAmelCase=[96, 192, 384, 768],		lowerCAmelCase=256,		lowerCAmelCase=-1,		lowerCAmelCase=False,		lowerCAmelCase=True,		lowerCAmelCase=0.4,		lowerCAmelCase=255,		lowerCAmelCase=0.1,		lowerCAmelCase=[1, 1_024, 24, 24],		lowerCAmelCase=[0, 1],		lowerCAmelCase=None,		**lowerCAmelCase,		):



              """simple docstring"""







              super().__init__(**lowerCAmelCase  )

              lowerCamelCase_							=hidden_size
              lowerCamelCase_							=is_hybrid

              if self.is_hybrid:
                     if backbone_config is None:
                            logger.info('''Initializing the config with a `BiT` backbone.'''  )
                            lowerCamelCase_							={
                                '''global_padding''': '''same''',
                                '''layer_type''': '''bottleneck''',
                                '''depths''': [3, 4, 9],
                                '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                                '''embedding_dynamic_padding''': True,
                            }
                            lowerCamelCase_							=BitConfig(**lowerCAmelCase  )
                     elif isinstance(lowerCAmelCase,		lowerCAmelCase  ):
                            logger.info('''Initializing the config with a `BiT` backbone.'''  )
                            lowerCamelCase_							=BitConfig(**lowerCAmelCase  )
                     elif isinstance(lowerCAmelCase,		lowerCAmelCase  ):
                            lowerCamelCase_							=backbone_config
                     else:
                            raise ValueError(
                                f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''  )

                     lowerCamelCase_							=backbone_featmap_shape
                     lowerCamelCase_							=neck_ignore_stages

                     if readout_type != "project":
                            raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''  )
              else:
                     lowerCamelCase_							=None
                     lowerCamelCase_							=None
                     lowerCamelCase_							=[]

              lowerCamelCase_							=num_hidden_layers
              lowerCamelCase_							=num_attention_heads
              lowerCamelCase_							=intermediate_size
              lowerCamelCase_							=hidden_act
              lowerCamelCase_							=hidden_dropout_prob
              lowerCamelCase_							=attention_probs_dropout_prob
              lowerCamelCase_							=initializer_range
              lowerCamelCase_							=layer_norm_eps
              lowerCamelCase_							=image_size
              lowerCamelCase_							=patch_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=qkv_bias
              lowerCamelCase_							=backbone_out_indices
              if readout_type not in ["ignore", "add", "project"]:
                     raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''  )
              lowerCamelCase_							=readout_type
              lowerCamelCase_							=reassemble_factors
              lowerCamelCase_							=neck_hidden_sizes
              lowerCamelCase_							=fusion_hidden_size
              lowerCamelCase_							=head_in_index
              lowerCamelCase_							=use_batch_norm_in_fusion_residual
              # auxiliary head attributes (semantic segmentation)
              lowerCamelCase_							=use_auxiliary_head
              lowerCamelCase_							=auxiliary_loss_weight
              lowerCamelCase_							=semantic_loss_ignore_index
              lowerCamelCase_							=semantic_classifier_dropout






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=copy.deepcopy(self.__dict__  )

              if output["backbone_config"] is not None:
                     lowerCamelCase_							=self.backbone_config.to_dict()

              lowerCamelCase_							=self.__class__.model_type
              return output












"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       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							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       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       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       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:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

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







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      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.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

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

          lowercase							    =       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       ):
      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       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


