





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

_lowerCamelCase   				=			TypeVar('T')




class   UpperCamelCase_       (     Generic[T]    ):


							def __init__(							self    :Tuple							,				__A    :list[T]							,				__A    :Callable[[T, T], T]		)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      None

									SCREAMING_SNAKE_CASE__			=      len(__A		)
									SCREAMING_SNAKE_CASE__			=      [any_type for _ in range(self.N		)] + arr
									SCREAMING_SNAKE_CASE__			=      fnc
									self.build()


							def      _snake_case  (							self    :Optional[int]		)   ->   None:



									"""simple docstring"""



									for p in range(self.N - 1							,				0							,				-1		):
											SCREAMING_SNAKE_CASE__			=      self.fn(self.st[p * 2]							,				self.st[p * 2 + 1]		)


							def      _snake_case  (							self    :Optional[Any]							,				__A    :int							,				__A    :T		)   ->   None:



									"""simple docstring"""



									p += self.N
									SCREAMING_SNAKE_CASE__			=      v
									while p > 1:
											SCREAMING_SNAKE_CASE__			=      p // 2
											SCREAMING_SNAKE_CASE__			=      self.fn(self.st[p * 2]							,				self.st[p * 2 + 1]		)


							def      _snake_case  (							self    :List[str]							,				__A    :int							,				__A    :int		)   ->   T | None:  # noqa: E741



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      l + self.N, r + self.N

									SCREAMING_SNAKE_CASE__			=      None
									while l <= r:
											if l % 2 == 1:
													SCREAMING_SNAKE_CASE__			=      self.st[l] if res is None else self.fn(__A							,				self.st[l]		)
											if r % 2 == 0:
													SCREAMING_SNAKE_CASE__			=      self.st[r] if res is None else self.fn(__A							,				self.st[r]		)
											SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (l + 1) // 2, (r - 1) // 2
									return res


if __name__ == "__main__":
	from functools import reduce

	_lowerCamelCase   				=			[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

	_lowerCamelCase   				=			{
	    0: 7,
	    1: 2,
	    2: 6,
	    3: -14,
	    4: 5,
	    5: 4,
	    6: 7,
	    7: -10,
	    8: 9,
	    9: 10,
	    10: 12,
	    11: 1,
	}

	_lowerCamelCase   				=			SegmentTree(test_array, min)
	_lowerCamelCase   				=			SegmentTree(test_array, max)
	_lowerCamelCase   				=			SegmentTree(test_array, lambda a, b: a + b)






	def 				SCREAMING_SNAKE_CASE__							(       ):
			for i in range(len(UpperCamelCase__    )    ):
					for j in range(UpperCamelCase__					,   len(UpperCamelCase__    )    ):
							SCREAMING_SNAKE_CASE__			=      reduce(UpperCamelCase__					,   test_array[i : j + 1]    )
							SCREAMING_SNAKE_CASE__			=      reduce(UpperCamelCase__					,   test_array[i : j + 1]    )
							SCREAMING_SNAKE_CASE__			=      reduce(lambda UpperCamelCase__					,   UpperCamelCase__    : a + b					,   test_array[i : j + 1]    )
							assert min_range == min_segment_tree.query(UpperCamelCase__					,   UpperCamelCase__    )
							assert max_range == max_segment_tree.query(UpperCamelCase__					,   UpperCamelCase__    )
							assert sum_range == sum_segment_tree.query(UpperCamelCase__					,   UpperCamelCase__    )

	test_all_segments()

	for index, value in test_updates.items():
		_lowerCamelCase   				=			value
		min_segment_tree.update(index, value)
		max_segment_tree.update(index, value)
		sum_segment_tree.update(index, value)
		test_all_segments()






import copy
import os
from typing import Union

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


_lowerCamelCase   				=			logging.get_logger(__name__)

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_text_model"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{
							    "hidden_size": "hidden_size",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :Union[str, Any]							,				__A    :Any=5_0244							,				__A    :Optional[Any]=768							,				__A    :Tuple=64							,				__A    :List[str]=2048							,				__A    :int=12							,				__A    :str=12							,				__A    :Any=32							,				__A    :Tuple=128							,				__A    :int=0.1							,				__A    :str=1E-6							,				__A    :Optional[Any]=1.0							,				__A    :Union[str, Any]="gelu_new"							,				__A    :Any=0							,				__A    :List[str]=False							,				__A    :Optional[Any]=0							,				__A    :int=1							,				__A    :Optional[int]=False							,				__A    :Optional[Any]=True							,				**__A    :List[Any]							,				)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      d_kv
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      num_layers
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      layer_norm_epsilon
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      use_cache

									SCREAMING_SNAKE_CASE__			=      eos_token_id
									SCREAMING_SNAKE_CASE__			=      decoder_start_token_id

									# for backwards compatibility
									SCREAMING_SNAKE_CASE__			=      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    :Optional[int]							,				__A    :Union[str, os.PathLike]							,				**__A    :Optional[int]		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      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   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_vision_model"


							def __init__(							self    :Optional[int]							,				__A    :int=768							,				__A    :Optional[Any]=768							,				__A    :Union[str, Any]=2048							,				__A    :int=64							,				__A    :Union[str, Any]=12							,				__A    :str=12							,				__A    :Any="gelu_new"							,				__A    :List[Any]=1E-6							,				__A    :Dict=0.0							,				__A    :int=0.0							,				__A    :int=1E-10							,				__A    :Dict=1.0							,				__A    :int=4096							,				__A    :int=32							,				__A    :int=128							,				**__A    :Tuple							,				)   ->   str:



									"""simple docstring"""



									super().__init__(**__A		)

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


							@classmethod
							def      _snake_case  (							cls    :str							,				__A    :Union[str, os.PathLike]							,				**__A    :str		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      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   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct"
							lowerCamelCase_           =					True


							def __init__(							self    :str							,				__A    :Optional[Any]=None							,				__A    :List[str]=None							,				__A    :Optional[Any]=1.0							,				__A    :Optional[Any]=0.0_2							,				__A    :Any=False							,				__A    :Tuple=False							,				__A    :Any=True							,				**__A    :Dict							,				)   ->   Union[str, Any]:



									"""simple docstring"""



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

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

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

									SCREAMING_SNAKE_CASE__			=      PixaStructTextConfig(**__A		)
									SCREAMING_SNAKE_CASE__			=      PixaStructVisionConfig(**__A		)

									SCREAMING_SNAKE_CASE__			=      self.text_config.decoder_start_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.pad_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.eos_token_id

									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      initializer_range

									SCREAMING_SNAKE_CASE__			=      self.initializer_range
									SCREAMING_SNAKE_CASE__			=      self.initializer_range

									SCREAMING_SNAKE_CASE__			=      is_vqa


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :PixaStructTextConfig							,				__A    :PixaStructVisionConfig							,				**__A    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :str		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output