


'''simple docstring'''






from __future__ import annotations

from random import choice


def    _snake_case  (	A			)		->       int:
							return choice(A			)


def    _snake_case  (	A					,       A			)		->       int:
							lowerCAmelCase__			  =      random_pivot(A			)

							# partition based on pivot
							# linear time
							lowerCAmelCase__			  =      [e for e in lst if e < pivot]
							lowerCAmelCase__			  =      [e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(A			) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(A			) < k - 1:
														return kth_number(A					,       k - len(A			) - 1			)
							# pivot is in elements smaller than k
							else:
														return kth_number(A					,       A			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()



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			:			Dict												=					{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}
class 						lowerCamelCase_							(					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			,							snake_case_=5_0_2_4_4			,							snake_case_=7_6_8			,							snake_case_=6_4			,							snake_case_=2_0_4_8			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_=3_2			,							snake_case_=1_2_8			,							snake_case_=0.1			,							snake_case_=1e-6			,							snake_case_=1.0			,							snake_case_="gelu_new"			,							snake_case_=0			,							snake_case_=False			,							snake_case_=0			,							snake_case_=1			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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

								__lowercase			       = eos_token_id
								__lowercase			       = decoder_start_token_id

								# for backwards compatibility
								__lowercase			       = dense_act_fn

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






					@classmethod
					def A   (					cls			,							snake_case_			,							**snake_case_    ) ->      "PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_    )

								__lowercase       ,			__lowercase			       = cls.get_config_dict(snake_case_			,							**snake_case_    )

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




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct_vision_model"






					def __init__(					self			,							snake_case_=7_6_8			,							snake_case_=7_6_8			,							snake_case_=2_0_4_8			,							snake_case_=6_4			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_="gelu_new"			,							snake_case_=1e-6			,							snake_case_=0.0			,							snake_case_=0.0			,							snake_case_=1e-1_0			,							snake_case_=1.0			,							snake_case_=4_0_9_6			,							snake_case_=3_2			,							snake_case_=1_2_8			,							**snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

								super().__init__(**snake_case_    )

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






					@classmethod
					def A   (					cls			,							snake_case_			,							**snake_case_    ) ->      "PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_    )

								__lowercase       ,			__lowercase			       = cls.get_config_dict(snake_case_			,							**snake_case_    )

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get('''model_type'''    ) == "pix2struct":
											__lowercase			       = 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(snake_case_			,							**snake_case_    )




class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct"
					__UpperCAmelCase							  =					True






					def __init__(					self			,							snake_case_=None			,							snake_case_=None			,							snake_case_=1.0			,							snake_case_=0.0_2			,							snake_case_=False			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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

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

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

								__lowercase			       = PixaStructTextConfig(**snake_case_    )
								__lowercase			       = PixaStructVisionConfig(**snake_case_    )

								__lowercase			       = self.text_config.decoder_start_token_id
								__lowercase			       = self.text_config.pad_token_id
								__lowercase			       = self.text_config.eos_token_id

								__lowercase			       = initializer_factor
								__lowercase			       = initializer_range

								__lowercase			       = self.initializer_range
								__lowercase			       = self.initializer_range

								__lowercase			       = is_vqa






					@classmethod
					def A   (					cls			,							snake_case_			,							snake_case_			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

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






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

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


