



"""simple docstring"""


import copy
import os
from typing import Union

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


__A   :							Optional[int]										=   logging.get_logger(__name__)

__A   :							Tuple										=   {
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}
class 			_a (					lowerCAmelCase):
   """simple docstring"""
   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     :    int				,   __UpperCamelCase     :    Dict=5_0_2_4_4				,   __UpperCamelCase     :    int=7_6_8				,   __UpperCamelCase     :    Optional[Any]=6_4				,   __UpperCamelCase     :    Any=2_0_4_8				,   __UpperCamelCase     :    Tuple=1_2				,   __UpperCamelCase     :    List[str]=1_2				,   __UpperCamelCase     :    int=3_2				,   __UpperCamelCase     :    Tuple=1_2_8				,   __UpperCamelCase     :    Union[str, Any]=0.1				,   __UpperCamelCase     :    List[str]=1e-6				,   __UpperCamelCase     :    Tuple=1.0				,   __UpperCamelCase     :    str="gelu_new"				,   __UpperCamelCase     :    Optional[int]=0				,   __UpperCamelCase     :    int=False				,   __UpperCamelCase     :    Optional[int]=0				,   __UpperCamelCase     :    List[Any]=1				,   __UpperCamelCase     :    Optional[int]=False				,   __UpperCamelCase     :    Optional[Any]=True				,   **__UpperCamelCase     :    Optional[int]				,   )->List[Any]:
       _UpperCAmelCase					=    vocab_size
       _UpperCAmelCase					=    hidden_size
       _UpperCAmelCase					=    d_kv
       _UpperCAmelCase					=    d_ff
       _UpperCAmelCase					=    num_layers
       _UpperCAmelCase					=    num_heads
       _UpperCAmelCase					=    relative_attention_num_buckets
       _UpperCAmelCase					=    relative_attention_max_distance
       _UpperCAmelCase					=    dropout_rate
       _UpperCAmelCase					=    layer_norm_epsilon
       _UpperCAmelCase					=    initializer_factor
       _UpperCAmelCase					=    use_cache

       _UpperCAmelCase					=    eos_token_id
       _UpperCAmelCase					=    decoder_start_token_id

       # for backwards compatibility
       _UpperCAmelCase					=    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  lowercase__						(		cls     :    str				,   __UpperCamelCase     :    Union[str, os.PathLike]				,   **__UpperCamelCase     :    Dict   )->"PretrainedConfig":
       cls._set_token_in_kwargs(__UpperCamelCase   )

       _UpperCAmelCase		,						_UpperCAmelCase					=    cls.get_config_dict(__UpperCamelCase				,   **__UpperCamelCase   )

       # get the text config dict if we are loading from Pix2StructConfig
       if config_dict.get('''model_type'''   ) == "pix2struct":
           _UpperCAmelCase					=    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 			_a (					lowerCAmelCase):
   """simple docstring"""
   UpperCamelCase__			      =	"""pix2struct_vision_model"""



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

       _UpperCAmelCase					=    hidden_size
       _UpperCAmelCase					=    patch_embed_hidden_size
       _UpperCAmelCase					=    d_ff
       _UpperCAmelCase					=    dropout_rate
       _UpperCAmelCase					=    num_hidden_layers
       _UpperCAmelCase					=    num_attention_heads
       _UpperCAmelCase					=    initializer_range
       _UpperCAmelCase					=    initializer_factor
       _UpperCAmelCase					=    attention_dropout
       _UpperCAmelCase					=    layer_norm_eps
       _UpperCAmelCase					=    dense_act_fn
       _UpperCAmelCase					=    seq_len
       _UpperCAmelCase					=    relative_attention_num_buckets
       _UpperCAmelCase					=    relative_attention_max_distance
       _UpperCAmelCase					=    d_kv



   @classmethod
   def  lowercase__						(		cls     :    Optional[Any]				,   __UpperCamelCase     :    Union[str, os.PathLike]				,   **__UpperCamelCase     :    List[Any]   )->"PretrainedConfig":
       cls._set_token_in_kwargs(__UpperCamelCase   )

       _UpperCAmelCase		,						_UpperCAmelCase					=    cls.get_config_dict(__UpperCamelCase				,   **__UpperCamelCase   )

       # get the vision config dict if we are loading from Pix2StructConfig
       if config_dict.get('''model_type'''   ) == "pix2struct":
           _UpperCAmelCase					=    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 			_a (					lowerCAmelCase):
   """simple docstring"""
   UpperCamelCase__			      =	"""pix2struct"""
   UpperCamelCase__			      =	True



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

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

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

       _UpperCAmelCase					=    PixaStructTextConfig(**__UpperCamelCase   )
       _UpperCAmelCase					=    PixaStructVisionConfig(**__UpperCamelCase   )

       _UpperCAmelCase					=    self.text_config.decoder_start_token_id
       _UpperCAmelCase					=    self.text_config.pad_token_id
       _UpperCAmelCase					=    self.text_config.eos_token_id

       _UpperCAmelCase					=    initializer_factor
       _UpperCAmelCase					=    initializer_range

       _UpperCAmelCase					=    self.initializer_range
       _UpperCAmelCase					=    self.initializer_range

       _UpperCAmelCase					=    is_vqa



   @classmethod
   def  lowercase__						(		cls     :    List[str]				,   __UpperCamelCase     :    PixaStructTextConfig				,   __UpperCamelCase     :    PixaStructVisionConfig				,   **__UpperCamelCase     :    int   )->int:
       return cls(text_config=text_config.to_dict()				,   vision_config=vision_config.to_dict()				,   **__UpperCamelCase   )



   def  lowercase__						(		self     :    Union[str, Any]   )->List[Any]:
       _UpperCAmelCase					=    copy.deepcopy(self.__dict__   )
       _UpperCAmelCase					=    self.text_config.to_dict()
       _UpperCAmelCase					=    self.vision_config.to_dict()
       _UpperCAmelCase					=    self.__class__.model_type
       return output








"""simple docstring"""


import functools







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]				):






				'''simple docstring'''



				if not isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for day in days				):
								raise ValueError('''The parameter days should be a list of integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) != 3 or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for cost in costs				):
								raise ValueError('''The parameter costs should be a list of three integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) == 0:
								return 0

				if min(_SCREAMING_SNAKE_CASE				) <= 0:
								raise ValueError('''All days elements should be greater than 0'''				)

				if max(_SCREAMING_SNAKE_CASE				) >= 366:
								raise ValueError('''All days elements should be less than 366'''				)

				_UpperCAmelCase					=    set(_SCREAMING_SNAKE_CASE				)

				@functools.cache
				def dynamic_programming(_SCREAMING_SNAKE_CASE   :						int				) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1				)

								return min(
								    costs[0] + dynamic_programming(index + 1				)					,     costs[1] + dynamic_programming(index + 7				)					,     costs[2] + dynamic_programming(index + 30				)					,     )

				return dynamic_programming(1				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



