






import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE 							=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=	{
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}
class 							A_    (						__lowercase			):






 '''simple docstring'''




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


 def __init__(	self							,  _A=50244							,  _A=768							,  _A=64							,  _A=2048							,  _A=12							,  _A=12							,  _A=32							,  _A=128							,  _A=0.1							,  _A=1e-6							,  _A=1.0							,  _A="gelu_new"							,  _A=0							,  _A=False							,  _A=0							,  _A=1							,  _A=False							,  _A=True							,  **_A							,  ) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				vocab_size
     _UpperCAmelCase				:      List[str]			  =				hidden_size
     _UpperCAmelCase				:      List[Any]			  =				d_kv
     _UpperCAmelCase				:      Any			  =				d_ff
     _UpperCAmelCase				:      Optional[Any]			  =				num_layers
     _UpperCAmelCase				:      int			  =				num_heads
     _UpperCAmelCase				:      Optional[Any]			  =				relative_attention_num_buckets
     _UpperCAmelCase				:      Any			  =				relative_attention_max_distance
     _UpperCAmelCase				:      Dict			  =				dropout_rate
     _UpperCAmelCase				:      int			  =				layer_norm_epsilon
     _UpperCAmelCase				:      Optional[int]			  =				initializer_factor
     _UpperCAmelCase				:      Optional[Any]			  =				use_cache

     _UpperCAmelCase				:      List[Any]			  =				eos_token_id
     _UpperCAmelCase				:      Dict			  =				decoder_start_token_id

     # for backwards compatibility
     _UpperCAmelCase				:      Optional[Any]			  =				dense_act_fn

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


 @classmethod
 def  snake_case__				(	cls							,  _A							,  **_A) ->		"PretrainedConfig":

     """simple docstring"""



     cls._set_token_in_kwargs(_A)

     _UpperCAmelCase				:      Optional[int]			  =				cls.get_config_dict(_A							,  **_A)

     # get the text config dict if we are loading from Pix2StructConfig
     if config_dict.get('''model_type''') == "pix2struct":
         _UpperCAmelCase				:      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(_A							,  **_A)



class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			str =					"pix2struct_vision_model"


 def __init__(	self							,  _A=768							,  _A=768							,  _A=2048							,  _A=64							,  _A=12							,  _A=12							,  _A="gelu_new"							,  _A=1e-6							,  _A=0.0							,  _A=0.0							,  _A=1e-10							,  _A=1.0							,  _A=4096							,  _A=32							,  _A=128							,  **_A							,  ) ->		Union[str, Any]:

     """simple docstring"""



     super().__init__(**_A)

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


 @classmethod
 def  snake_case__				(	cls							,  _A							,  **_A) ->		"PretrainedConfig":

     """simple docstring"""



     cls._set_token_in_kwargs(_A)

     _UpperCAmelCase				:      Any			  =				cls.get_config_dict(_A							,  **_A)

     # get the vision config dict if we are loading from Pix2StructConfig
     if config_dict.get('''model_type''') == "pix2struct":
         _UpperCAmelCase				:      Union[str, 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(_A							,  **_A)



class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[int] =					"pix2struct"
 _SCREAMING_SNAKE_CASE							:			Dict =					True


 def __init__(	self							,  _A=None							,  _A=None							,  _A=1.0							,  _A=0.02							,  _A=False							,  _A=False							,  _A=True							,  **_A							,  ) ->		int:

     """simple docstring"""



     super().__init__(tie_word_embeddings=_A							,  is_encoder_decoder=_A							,  **_A)

     if text_config is None:
         _UpperCAmelCase				:      Union[str, Any]			  =				{}
         logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''')

     if vision_config is None:
         _UpperCAmelCase				:      Union[str, Any]			  =				{}
         logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''')

     _UpperCAmelCase				:      List[Any]			  =				PixaStructTextConfig(**_A)
     _UpperCAmelCase				:      Dict			  =				PixaStructVisionConfig(**_A)

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

     _UpperCAmelCase				:      Dict			  =				initializer_factor
     _UpperCAmelCase				:      Dict			  =				initializer_range

     _UpperCAmelCase				:      int			  =				self.initializer_range
     _UpperCAmelCase				:      Dict			  =				self.initializer_range

     _UpperCAmelCase				:      List[str]			  =				is_vqa


 @classmethod
 def  snake_case__				(	cls							,  _A							,  _A							,  **_A) ->		List[Any]:

     """simple docstring"""



     return cls(text_config=text_config.to_dict()							,  vision_config=vision_config.to_dict()							,  **_A)


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



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












from __future__ import annotations

def      _lowerCamelCase	(						__A    :							int )      ->					list[int]:
    _UpperCAmelCase				:      List[str]			  =				[True] * limit
    _UpperCAmelCase				:      Optional[int]			  =				False
    _UpperCAmelCase				:      Dict			  =				False
    _UpperCAmelCase				:      List[str]			  =				True

    for i in range(3		,		int(limit**0.5 + 1 )		,		2 ):
        _UpperCAmelCase				:      List[str]			  =				i * 2
        while index < limit:
            _UpperCAmelCase				:      Union[str, Any]			  =				False
            _UpperCAmelCase				:      int			  =				index + i

    _UpperCAmelCase				:      Optional[int]			  =				[2]

    for i in range(3		,		__A		,		2 ):
        if is_prime[i]:
            primes.append(__A )

    return primes

def      _lowerCamelCase	(						__A    :							int = 1_000_000 )      ->					int:
    _UpperCAmelCase				:      Any			  =				prime_sieve(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    _UpperCAmelCase				:      Tuple			  =				0

    for i in range(len(__A ) ):
        for j in range(i + length		,		len(__A ) ):
            _UpperCAmelCase				:      List[Any]			  =				sum(primes[i:j] )
            if sol >= ceiling:
                break

            if sol in primes:
                _UpperCAmelCase				:      List[str]			  =				j - i
                _UpperCAmelCase				:      Optional[Any]			  =				sol

    return largest


if __name__ == "__main__":
  print(F'{solution() = }')




