



"""simple docstring"""







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:    str				   =     logging.get_logger(__name__)

lowercase__		:    str				   =     {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""pix2struct_text_model"""
      _SCREAMING_SNAKE_CASE      =					["""past_key_values"""]
      _SCREAMING_SNAKE_CASE      =					{
          """hidden_size""": """hidden_size""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }



      def __init__(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Any=5_0_2_4_4   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=6_4   ,      SCREAMING_SNAKE_CASE_    :				Any=2_0_4_8   ,      SCREAMING_SNAKE_CASE_    :				int=1_2   ,      SCREAMING_SNAKE_CASE_    :				str=1_2   ,      SCREAMING_SNAKE_CASE_    :				str=3_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2_8   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.1   ,      SCREAMING_SNAKE_CASE_    :				int=1E-6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="gelu_new"   ,      SCREAMING_SNAKE_CASE_    :				int=0   ,      SCREAMING_SNAKE_CASE_    :				Any=False   ,      SCREAMING_SNAKE_CASE_    :				int=0   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1   ,      SCREAMING_SNAKE_CASE_    :				Tuple=False   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      **SCREAMING_SNAKE_CASE_    :				List[Any]   ,      ):
        lowerCAmelCase_       :    Tuple								=			vocab_size
        lowerCAmelCase_       :    int								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			d_kv
        lowerCAmelCase_       :    Optional[int]								=			d_ff
        lowerCAmelCase_       :    List[Any]								=			num_layers
        lowerCAmelCase_       :    Optional[Any]								=			num_heads
        lowerCAmelCase_       :    Optional[int]								=			relative_attention_num_buckets
        lowerCAmelCase_       :    str								=			relative_attention_max_distance
        lowerCAmelCase_       :    Tuple								=			dropout_rate
        lowerCAmelCase_       :    int								=			layer_norm_epsilon
        lowerCAmelCase_       :    Dict								=			initializer_factor
        lowerCAmelCase_       :    int								=			use_cache

        lowerCAmelCase_       :    Optional[int]								=			eos_token_id
        lowerCAmelCase_       :    Any								=			decoder_start_token_id

        # for backwards compatibility
        lowerCAmelCase_       :    int								=			dense_act_fn

        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_   ,      eos_token_id=SCREAMING_SNAKE_CASE_   ,      decoder_start_token_id=SCREAMING_SNAKE_CASE_   ,      tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,      is_decoder=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_   ,      )





      @classmethod
      def 					SCREAMING_SNAKE_CASE__			(				cls    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Union[str, os.PathLike]   ,      **SCREAMING_SNAKE_CASE_    :				int	):
        cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_						,lowerCAmelCase_       :    int								=			cls.get_config_dict(SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        # get the text config dict if we are loading from Pix2StructConfig
        if config_dict.get('model_type'	) == "pix2struct":
          lowerCAmelCase_       :    List[str]								=			config_dict['text_config']

        if "model_type" in config_dict and hasattr(cls   ,      'model_type'	) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
              F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."	)

        return cls.from_dict(SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""pix2struct_vision_model"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Any=2_0_4_8   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=6_4   ,      SCREAMING_SNAKE_CASE_    :				Any=1_2   ,      SCREAMING_SNAKE_CASE_    :				Any=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]="gelu_new"   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-6   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				int=0.0   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1E-10   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1.0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=4_0_9_6   ,      SCREAMING_SNAKE_CASE_    :				str=3_2   ,      SCREAMING_SNAKE_CASE_    :				Dict=1_2_8   ,      **SCREAMING_SNAKE_CASE_    :				Dict   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Any								=			hidden_size
        lowerCAmelCase_       :    str								=			patch_embed_hidden_size
        lowerCAmelCase_       :    Optional[int]								=			d_ff
        lowerCAmelCase_       :    str								=			dropout_rate
        lowerCAmelCase_       :    List[Any]								=			num_hidden_layers
        lowerCAmelCase_       :    List[Any]								=			num_attention_heads
        lowerCAmelCase_       :    List[Any]								=			initializer_range
        lowerCAmelCase_       :    Any								=			initializer_factor
        lowerCAmelCase_       :    Any								=			attention_dropout
        lowerCAmelCase_       :    Any								=			layer_norm_eps
        lowerCAmelCase_       :    Any								=			dense_act_fn
        lowerCAmelCase_       :    int								=			seq_len
        lowerCAmelCase_       :    List[Any]								=			relative_attention_num_buckets
        lowerCAmelCase_       :    Tuple								=			relative_attention_max_distance
        lowerCAmelCase_       :    Any								=			d_kv





      @classmethod
      def 					SCREAMING_SNAKE_CASE__			(				cls    :				str   ,      SCREAMING_SNAKE_CASE_    :				Union[str, os.PathLike]   ,      **SCREAMING_SNAKE_CASE_    :				str	):
        cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_						,lowerCAmelCase_       :    Dict								=			cls.get_config_dict(SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        # get the vision config dict if we are loading from Pix2StructConfig
        if config_dict.get('model_type'	) == "pix2struct":
          lowerCAmelCase_       :    str								=			config_dict['vision_config']

        if "model_type" in config_dict and hasattr(cls   ,      'model_type'	) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
              F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."	)

        return cls.from_dict(SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""pix2struct"""
      _SCREAMING_SNAKE_CASE      =					True



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict=None   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=None   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				Tuple=False   ,      SCREAMING_SNAKE_CASE_    :				str=False   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      **SCREAMING_SNAKE_CASE_    :				int   ,      ):
        super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE_   ,      is_encoder_decoder=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        if text_config is None:
          lowerCAmelCase_       :    Tuple								=			{}
          logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'	)

        if vision_config is None:
          lowerCAmelCase_       :    str								=			{}
          logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'	)

        lowerCAmelCase_       :    Optional[int]								=			PixaStructTextConfig(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			PixaStructVisionConfig(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			self.text_config.decoder_start_token_id
        lowerCAmelCase_       :    str								=			self.text_config.pad_token_id
        lowerCAmelCase_       :    int								=			self.text_config.eos_token_id

        lowerCAmelCase_       :    int								=			initializer_factor
        lowerCAmelCase_       :    Tuple								=			initializer_range

        lowerCAmelCase_       :    List[str]								=			self.initializer_range
        lowerCAmelCase_       :    Optional[Any]								=			self.initializer_range

        lowerCAmelCase_       :    Optional[Any]								=			is_vqa



      @classmethod
      def 					SCREAMING_SNAKE_CASE__			(				cls    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				PixaStructTextConfig   ,      SCREAMING_SNAKE_CASE_    :				PixaStructVisionConfig   ,      **SCREAMING_SNAKE_CASE_    :				Union[str, Any]	):
        return cls(text_config=text_config.to_dict()   ,      vision_config=vision_config.to_dict()   ,      **SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			copy.deepcopy(self.__dict__	)
        lowerCAmelCase_       :    Any								=			self.text_config.to_dict()
        lowerCAmelCase_       :    Tuple								=			self.vision_config.to_dict()
        lowerCAmelCase_       :    Tuple								=			self.__class__.model_type
        return output










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    list[int]   ,       lowerCAmelCase__				:    list[int]						)						->       None:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			len(lowerCAmelCase__						)
  print('The following activities are selected:'						)

  # The first activity is always selected
  lowerCAmelCase_       :    str								=			0
  print(lowerCAmelCase__   ,       end=','						)

  # Consider rest of the activities
  for j in range(lowerCAmelCase__						):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(lowerCAmelCase__   ,       end=','						)
      lowerCAmelCase_       :    Tuple								=			j


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowercase__		:    List[str]				   =     [1, 3, 0, 5, 8, 5]
     lowercase__		:    Dict				   =     [2, 4, 6, 7, 9, 9]
     print_max_activities(start, finish)





