





'''simple docstring'''


import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE		       =							{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_SCREAMING_SNAKE_CASE		       =							logging.get_logger(__name__)

class       lowerCAmelCase_						(						UpperCAmelCase__						):
 __lowerCamelCase  :							Optional[int]				     =    """mask2former"""
 __lowerCamelCase  :							Union[str, Any]				     =    ["""swin"""]
 __lowerCamelCase  :							Optional[Any]				     =    {"""hidden_size""": """hidden_dim"""}




 def __init__(		self	,   _lowerCAmelCase = None	,   _lowerCAmelCase = 256	,   _lowerCAmelCase = 256	,   _lowerCAmelCase = 256	,   _lowerCAmelCase = 1024	,   _lowerCAmelCase = "relu"	,   _lowerCAmelCase = 6	,   _lowerCAmelCase = 10	,   _lowerCAmelCase = 8	,   _lowerCAmelCase = 0.0	,   _lowerCAmelCase = 2048	,   _lowerCAmelCase = False	,   _lowerCAmelCase = False	,   _lowerCAmelCase = 4	,   _lowerCAmelCase = 255	,   _lowerCAmelCase = 100	,   _lowerCAmelCase = 0.1	,   _lowerCAmelCase = 2.0	,   _lowerCAmelCase = 5.0	,   _lowerCAmelCase = 5.0	,   _lowerCAmelCase = 12544	,   _lowerCAmelCase = 3.0	,   _lowerCAmelCase = 0.75	,   _lowerCAmelCase = 0.02	,   _lowerCAmelCase = 1.0	,   _lowerCAmelCase = True	,   _lowerCAmelCase = [4, 8, 16, 32]	,   _lowerCAmelCase = None	,   **_lowerCAmelCase	,   )			->							List[str]:

     if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."						)
         _lowerCAmelCase			=					CONFIG_MAPPING['swin'](
             image_size=224	,   in_channels=3	,   patch_size=4	,   embed_dim=96	,   depths=[2, 2, 18, 2]	,   num_heads=[3, 6, 12, 24]	,   window_size=7	,   drop_path_rate=0.3	,   use_absolute_embeddings=snake_case_	,   out_features=["stage1", "stage2", "stage3", "stage4"]	,   )

     if isinstance(snake_case_	,   snake_case_						):
         _lowerCAmelCase			=					backbone_config.pop("model_type"						)
         _lowerCAmelCase			=					CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase			=					config_class.from_dict(snake_case_						)

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
         logger.warning_once(
             f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
             f'''Supported model types: {','.join(self.backbones_supported						)}'''						)

     _lowerCAmelCase			=					backbone_config
     _lowerCAmelCase			=					feature_size
     _lowerCAmelCase			=					mask_feature_size
     _lowerCAmelCase			=					hidden_dim
     _lowerCAmelCase			=					encoder_feedforward_dim
     _lowerCAmelCase			=					activation_function
     _lowerCAmelCase			=					encoder_layers
     _lowerCAmelCase			=					decoder_layers
     _lowerCAmelCase			=					num_attention_heads
     _lowerCAmelCase			=					dropout
     _lowerCAmelCase			=					dim_feedforward
     _lowerCAmelCase			=					pre_norm
     _lowerCAmelCase			=					enforce_input_projection
     _lowerCAmelCase			=					common_stride
     _lowerCAmelCase			=					ignore_value
     _lowerCAmelCase			=					num_queries
     _lowerCAmelCase			=					no_object_weight
     _lowerCAmelCase			=					class_weight
     _lowerCAmelCase			=					mask_weight
     _lowerCAmelCase			=					dice_weight
     _lowerCAmelCase			=					train_num_points
     _lowerCAmelCase			=					oversample_ratio
     _lowerCAmelCase			=					importance_sample_ratio
     _lowerCAmelCase			=					init_std
     _lowerCAmelCase			=					init_xavier_std
     _lowerCAmelCase			=					use_auxiliary_loss
     _lowerCAmelCase			=					feature_strides
     _lowerCAmelCase			=					output_auxiliary_logits
     _lowerCAmelCase			=					decoder_layers

     super().__init__(**snake_case_						)




 @classmethod
 def 							_snake_case      (		cls	,   _lowerCAmelCase	,   **_lowerCAmelCase						)			->							List[Any]:
     return cls(
         backbone_config=snake_case_	,   **snake_case_	,   )





 def 							_snake_case      (		self						)			->							int:
     _lowerCAmelCase			=					copy.deepcopy(self.__dict__						)
     _lowerCAmelCase			=					self.backbone_config.to_dict()
     _lowerCAmelCase			=					self.__class__.model_type
     return output









"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""image_processor""", """tokenizer"""]
      lowercase_      : Union[str, Any]	    =   """ViltImageProcessor"""
      lowercase_      : Any	    =   ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, 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.'					,      snake_case_					,      )
          A_  :       Dict										=  kwargs.pop('feature_extractor'  )

        A_  :       Dict										=  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__(snake_case_					,      snake_case_  )
        A_  :       List[str]										=  self.image_processor

      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = True					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  self.tokenizer(
            text=snake_case_					,      add_special_tokens=snake_case_					,      padding=snake_case_					,      truncation=snake_case_					,      max_length=snake_case_					,      stride=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_token_type_ids=snake_case_					,      return_attention_mask=snake_case_					,      return_overflowing_tokens=snake_case_					,      return_special_tokens_mask=snake_case_					,      return_offsets_mapping=snake_case_					,      return_length=snake_case_					,      verbose=snake_case_					,      return_tensors=snake_case_					,      **snake_case_					,      )
        # add pixel_values + pixel_mask
        A_  :       Optional[int]										=  self.image_processor(snake_case_					,      return_tensors=snake_case_  )
        encoding.update(snake_case_  )

        return encoding

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.decode(*snake_case_					,      **snake_case_  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.tokenizer.model_input_names
        A_  :       Any										=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      snake_case_					,      )
        return self.image_processor_class

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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