





'''simple docstring'''






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


A_		:  Optional[int]		 =						{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

A_		:  Union[str, Any]		 =						logging.get_logger(__name__)
class 					__snake_case  (					_a							):






   '''simple docstring'''





   lowerCamelCase__    							=      '''maskformer'''
   lowerCamelCase__    							=      {'''hidden_size''': '''mask_feature_size'''}
   lowerCamelCase__    							=      ['''resnet''', '''swin''']
   lowerCamelCase__    							=      ['''detr''']

   def __init__(							self	,		__SCREAMING_SNAKE_CASE = 2_5_6	,		__SCREAMING_SNAKE_CASE = 2_5_6	,		__SCREAMING_SNAKE_CASE = 0.1	,		__SCREAMING_SNAKE_CASE = False	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = None	,		__SCREAMING_SNAKE_CASE = 0.02	,		__SCREAMING_SNAKE_CASE = 1.0	,		__SCREAMING_SNAKE_CASE = 1.0	,		__SCREAMING_SNAKE_CASE = 1.0	,		__SCREAMING_SNAKE_CASE = 2_0.0	,		__SCREAMING_SNAKE_CASE = None	,		**__SCREAMING_SNAKE_CASE	,		):

    if backbone_config is None:
     # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
     snake_case__     :					int    =  SwinConfig(
         image_size=3_8_4	,		in_channels=3	,		patch_size=4	,		embed_dim=1_2_8	,		depths=[2, 2, 1_8, 2]	,		num_heads=[4, 8, 1_6, 3_2]	,		window_size=1_2	,		drop_path_rate=0.3	,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	,		)

    if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					Any    =  backbone_config.pop("""model_type"""       )
     snake_case__     :					Union[str, Any]    =  CONFIG_MAPPING[backbone_model_type]
     snake_case__     :					Optional[Any]    =  config_class.from_dict(__SCREAMING_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 MaskFormer. "
         f"Supported model types: {','.join(self.backbones_supported       )}"       )

    if decoder_config is None:
     # fall back to https://huggingface.co/facebook/detr-resnet-50
     snake_case__     :					Any    =  DetrConfig()
    else:
     # verify that the decoder is supported
     snake_case__     :					Optional[int]    =  (
         decoder_config.pop("""model_type"""       ) if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ) else decoder_config.model_type
     )
     if decoder_type not in self.decoders_supported:
      raise ValueError(
          f"Transformer Decoder {decoder_type} not supported, please use one of"
          f" {','.join(self.decoders_supported       )}"       )
     if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
      snake_case__     :					Union[str, Any]    =  CONFIG_MAPPING[decoder_type]
      snake_case__     :					Any    =  config_class.from_dict(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  backbone_config
    snake_case__     :					List[str]    =  decoder_config
    # main feature dimension for the model
    snake_case__     :					List[Any]    =  fpn_feature_size
    snake_case__     :					str    =  mask_feature_size
    # initializer
    snake_case__     :					Any    =  init_std
    snake_case__     :					List[str]    =  init_xavier_std
    # Hungarian matcher && loss
    snake_case__     :					Dict    =  cross_entropy_weight
    snake_case__     :					str    =  dice_weight
    snake_case__     :					Tuple    =  mask_weight
    snake_case__     :					Optional[Any]    =  use_auxiliary_loss
    snake_case__     :					Dict    =  no_object_weight
    snake_case__     :					str    =  output_auxiliary_logits

    snake_case__     :					Optional[Any]    =  self.decoder_config.encoder_attention_heads
    snake_case__     :					str    =  self.decoder_config.num_hidden_layers
    super().__init__(**__SCREAMING_SNAKE_CASE       )

   @classmethod
   def 		__UpperCamelCase						(							cls	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ):
    return cls(
        backbone_config=__SCREAMING_SNAKE_CASE	,		decoder_config=__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE	,		)


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  copy.deepcopy(self.__dict__       )
    snake_case__     :					Optional[Any]    =  self.backbone_config.to_dict()
    snake_case__     :					Optional[int]    =  self.decoder_config.to_dict()
    snake_case__     :					List[Any]    =  self.__class__.model_type
    return output












from typing import List, Optional, Union

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






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





       def __call__(				self	:Dict,      snake_case	:ImageInput = None,      snake_case	:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,      snake_case	:bool = True,      snake_case	:Union[bool, str, PaddingStrategy] = False,      snake_case	:Union[bool, str, TruncationStrategy] = None,      snake_case	:Optional[int] = None,      snake_case	:int = 0,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = False,      snake_case	:bool = True,      snake_case	:Optional[Union[str, TensorType]] = None,      **snake_case	:str,      ):



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =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_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =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_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





       def 				UpperCamelCase__						(				self	:List[Any],      *snake_case	:int,      **snake_case	:str):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

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





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.tokenizer.model_input_names
          _lowercase              =self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


