





import copy
import os
from typing import Union

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


_UpperCAmelCase				:       Union[str, Any]	   =				logging.get_logger(__name__)

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

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""pix2struct_text_model"""
 UpperCAmelCase__													=		["""past_key_values"""]
 UpperCAmelCase__													=		{
     """hidden_size""": """hidden_size""",
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(      self  :      str			,     UpperCAmelCase  :      Any=50244			,     UpperCAmelCase  :      Union[str, Any]=768			,     UpperCAmelCase  :      List[Any]=64			,     UpperCAmelCase  :      str=2048			,     UpperCAmelCase  :      Optional[int]=12			,     UpperCAmelCase  :      Optional[Any]=12			,     UpperCAmelCase  :      Tuple=32			,     UpperCAmelCase  :      Optional[Any]=128			,     UpperCAmelCase  :      List[str]=0.1			,     UpperCAmelCase  :      List[Any]=1e-6			,     UpperCAmelCase  :      List[str]=1.0			,     UpperCAmelCase  :      Any="gelu_new"			,     UpperCAmelCase  :      Optional[int]=0			,     UpperCAmelCase  :      Optional[int]=False			,     UpperCAmelCase  :      List[Any]=0			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      Dict=False			,     UpperCAmelCase  :      List[str]=True			,     **UpperCAmelCase  :      Optional[int]			,     )			->	Tuple:
     lowerCamelCase__    :      Union[str, Any]					 =		vocab_size
     lowerCamelCase__    :      List[str]					 =		hidden_size
     lowerCamelCase__    :      Optional[int]					 =		d_kv
     lowerCamelCase__    :      Union[str, Any]					 =		d_ff
     lowerCamelCase__    :      Dict					 =		num_layers
     lowerCamelCase__    :      Tuple					 =		num_heads
     lowerCamelCase__    :      str					 =		relative_attention_num_buckets
     lowerCamelCase__    :      Optional[Any]					 =		relative_attention_max_distance
     lowerCamelCase__    :      Tuple					 =		dropout_rate
     lowerCamelCase__    :      str					 =		layer_norm_epsilon
     lowerCamelCase__    :      str					 =		initializer_factor
     lowerCamelCase__    :      Optional[Any]					 =		use_cache

     lowerCamelCase__    :      Dict					 =		eos_token_id
     lowerCamelCase__    :      Dict					 =		decoder_start_token_id

     # for backwards compatibility
     lowerCamelCase__    :      Tuple					 =		dense_act_fn

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







 @classmethod
 def      A_	(      cls  :      Tuple			,     UpperCAmelCase  :      Union[str, os.PathLike]			,     **UpperCAmelCase  :      str	)			->	"PretrainedConfig":
     cls._set_token_in_kwargs(UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		cls.get_config_dict(UpperCAmelCase			,     **UpperCAmelCase	)

     # get the text config dict if we are loading from Pix2StructConfig
     if config_dict.get('model_type'	) == "pix2struct":
         lowerCamelCase__    :      Union[str, Any]					 =		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(UpperCAmelCase			,     **UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""pix2struct_vision_model"""
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      int=768			,     UpperCAmelCase  :      Optional[Any]=768			,     UpperCAmelCase  :      Optional[Any]=2048			,     UpperCAmelCase  :      str=64			,     UpperCAmelCase  :      Union[str, Any]=12			,     UpperCAmelCase  :      List[str]=12			,     UpperCAmelCase  :      Any="gelu_new"			,     UpperCAmelCase  :      List[Any]=1e-6			,     UpperCAmelCase  :      Any=0.0			,     UpperCAmelCase  :      List[Any]=0.0			,     UpperCAmelCase  :      Union[str, Any]=1e-10			,     UpperCAmelCase  :      List[Any]=1.0			,     UpperCAmelCase  :      Optional[int]=4096			,     UpperCAmelCase  :      Union[str, Any]=32			,     UpperCAmelCase  :      Union[str, Any]=128			,     **UpperCAmelCase  :      List[str]			,     )			->	Tuple:
     super().__init__(**UpperCAmelCase	)

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







 @classmethod
 def      A_	(      cls  :      Any			,     UpperCAmelCase  :      Union[str, os.PathLike]			,     **UpperCAmelCase  :      int	)			->	"PretrainedConfig":
     cls._set_token_in_kwargs(UpperCAmelCase	)

     lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		cls.get_config_dict(UpperCAmelCase			,     **UpperCAmelCase	)

     # get the vision config dict if we are loading from Pix2StructConfig
     if config_dict.get('model_type'	) == "pix2struct":
         lowerCamelCase__    :      Optional[Any]					 =		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(UpperCAmelCase			,     **UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""pix2struct"""
 UpperCAmelCase__													=		True
 def __init__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]=None			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      str=1.0			,     UpperCAmelCase  :      str=0.0_2			,     UpperCAmelCase  :      int=False			,     UpperCAmelCase  :      Optional[int]=False			,     UpperCAmelCase  :      List[Any]=True			,     **UpperCAmelCase  :      Dict			,     )			->	Optional[int]:
     super().__init__(tie_word_embeddings=UpperCAmelCase			,     is_encoder_decoder=UpperCAmelCase			,     **UpperCAmelCase	)

     if text_config is None:
         lowerCamelCase__    :      List[Any]					 =		{}
         logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'	)

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

     lowerCamelCase__    :      List[Any]					 =		PixaStructTextConfig(**UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		PixaStructVisionConfig(**UpperCAmelCase	)

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

     lowerCamelCase__    :      Optional[int]					 =		initializer_factor
     lowerCamelCase__    :      Any					 =		initializer_range

     lowerCamelCase__    :      Union[str, Any]					 =		self.initializer_range
     lowerCamelCase__    :      Optional[Any]					 =		self.initializer_range

     lowerCamelCase__    :      List[str]					 =		is_vqa
 @classmethod
 def      A_	(      cls  :      Dict			,     UpperCAmelCase  :      PixaStructTextConfig			,     UpperCAmelCase  :      PixaStructVisionConfig			,     **UpperCAmelCase  :      Union[str, Any]	)			->	str:
     return cls(text_config=text_config.to_dict()			,     vision_config=vision_config.to_dict()			,     **UpperCAmelCase	)







 def      A_	(      self  :      int	)			->	Optional[int]:
     lowerCamelCase__    :      Dict					 =		copy.deepcopy(self.__dict__	)
     lowerCamelCase__    :      List[Any]					 =		self.text_config.to_dict()
     lowerCamelCase__    :      Any					 =		self.vision_config.to_dict()
     lowerCamelCase__    :      List[Any]					 =		self.__class__.model_type
     return output








"""simple docstring"""





def   _A  (  UpperCamelCase_     :       list[int])	->						float:
   '''simple docstring'''


   if not nums:  # Makes sure that the list is not empty
      raise ValueError("List is empty")

   __lowercase								=						sum(UpperCamelCase_) / len(UpperCamelCase_)  # Calculate the average
   return sum(abs(x - average) for x in nums) / len(UpperCamelCase_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




