



'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
					pass

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


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

snake_case_			:     str  	=  {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """align_text_model"""





	def __init__(			self :    List[Any]			,lowerCamelCase__ :    Any=30522			,lowerCamelCase__ :    Any=768			,lowerCamelCase__ :    Any=12			,lowerCamelCase__ :    List[str]=12			,lowerCamelCase__ :    Union[str, Any]=3072			,lowerCamelCase__ :    str="gelu"			,lowerCamelCase__ :    List[Any]=0.1			,lowerCamelCase__ :    Optional[int]=0.1			,lowerCamelCase__ :    str=512			,lowerCamelCase__ :    Optional[int]=2			,lowerCamelCase__ :    Dict=0.0_2			,lowerCamelCase__ :    Union[str, Any]=1E-12			,lowerCamelCase__ :    str=0			,lowerCamelCase__ :    List[Any]="absolute"			,lowerCamelCase__ :    Any=True			,**lowerCamelCase__ :    Optional[Any]			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)

							_UpperCamelCase :		str			    =       vocab_size
							_UpperCamelCase :		Union[str, Any]			    =       hidden_size
							_UpperCamelCase :		Union[str, Any]			    =       num_hidden_layers
							_UpperCamelCase :		str			    =       num_attention_heads
							_UpperCamelCase :		List[str]			    =       hidden_act
							_UpperCamelCase :		Tuple			    =       intermediate_size
							_UpperCamelCase :		Dict			    =       hidden_dropout_prob
							_UpperCamelCase :		Tuple			    =       attention_probs_dropout_prob
							_UpperCamelCase :		Optional[Any]			    =       max_position_embeddings
							_UpperCamelCase :		List[str]			    =       type_vocab_size
							_UpperCamelCase :		Tuple			    =       initializer_range
							_UpperCamelCase :		List[str]			    =       layer_norm_eps
							_UpperCamelCase :		str			    =       position_embedding_type
							_UpperCamelCase :		Any			    =       use_cache
							_UpperCamelCase :		Any			    =       pad_token_id





	@classmethod
	def      UpperCamelCase_  (			cls :    Tuple			,lowerCamelCase__ :    Union[str, os.PathLike]			,**lowerCamelCase__ :    Any			):







							'''simple docstring'''
							cls._set_token_in_kwargs(lowerCamelCase__			)

							_UpperCamelCase    ,      _UpperCamelCase :		Dict			    =       cls.get_config_dict(lowerCamelCase__			,**lowerCamelCase__			)

							# get the text config dict if we are loading from AlignConfig
							if config_dict.get('model_type'			) == "align":
													_UpperCamelCase :		List[str]			    =       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(lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """align_vision_model"""





	def __init__(			self :    Optional[int]			,lowerCamelCase__ :    int = 3			,lowerCamelCase__ :    int = 600			,lowerCamelCase__ :    float = 2.0			,lowerCamelCase__ :    float = 3.1			,lowerCamelCase__ :    int = 8			,lowerCamelCase__ :    List[int] = [3, 3, 5, 3, 5, 5, 3]			,lowerCamelCase__ :    List[int] = [32, 16, 24, 40, 80, 112, 192]			,lowerCamelCase__ :    List[int] = [16, 24, 40, 80, 112, 192, 320]			,lowerCamelCase__ :    List[int] = []			,lowerCamelCase__ :    List[int] = [1, 2, 2, 2, 1, 2, 1]			,lowerCamelCase__ :    List[int] = [1, 2, 2, 3, 3, 4, 1]			,lowerCamelCase__ :    List[int] = [1, 6, 6, 6, 6, 6, 6]			,lowerCamelCase__ :    float = 0.2_5			,lowerCamelCase__ :    str = "swish"			,lowerCamelCase__ :    int = 2560			,lowerCamelCase__ :    str = "mean"			,lowerCamelCase__ :    float = 0.0_2			,lowerCamelCase__ :    float = 0.0_0_1			,lowerCamelCase__ :    float = 0.9_9			,lowerCamelCase__ :    float = 0.2			,**lowerCamelCase__ :    Tuple			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)

							_UpperCamelCase :		Any			    =       num_channels
							_UpperCamelCase :		Union[str, Any]			    =       image_size
							_UpperCamelCase :		Union[str, Any]			    =       width_coefficient
							_UpperCamelCase :		List[str]			    =       depth_coefficient
							_UpperCamelCase :		str			    =       depth_divisor
							_UpperCamelCase :		Dict			    =       kernel_sizes
							_UpperCamelCase :		List[str]			    =       in_channels
							_UpperCamelCase :		Tuple			    =       out_channels
							_UpperCamelCase :		str			    =       depthwise_padding
							_UpperCamelCase :		Tuple			    =       strides
							_UpperCamelCase :		Tuple			    =       num_block_repeats
							_UpperCamelCase :		List[Any]			    =       expand_ratios
							_UpperCamelCase :		Union[str, Any]			    =       squeeze_expansion_ratio
							_UpperCamelCase :		Optional[int]			    =       hidden_act
							_UpperCamelCase :		Dict			    =       hidden_dim
							_UpperCamelCase :		List[str]			    =       pooling_type
							_UpperCamelCase :		str			    =       initializer_range
							_UpperCamelCase :		Union[str, Any]			    =       batch_norm_eps
							_UpperCamelCase :		List[Any]			    =       batch_norm_momentum
							_UpperCamelCase :		List[str]			    =       drop_connect_rate
							_UpperCamelCase :		Optional[int]			    =       sum(lowerCamelCase__			) * 4





	@classmethod
	def      UpperCamelCase_  (			cls :    Tuple			,lowerCamelCase__ :    Union[str, os.PathLike]			,**lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							cls._set_token_in_kwargs(lowerCamelCase__			)

							_UpperCamelCase    ,      _UpperCamelCase :		Any			    =       cls.get_config_dict(lowerCamelCase__			,**lowerCamelCase__			)

							# get the vision config dict if we are loading from AlignConfig
							if config_dict.get('model_type'			) == "align":
													_UpperCamelCase :		str			    =       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(lowerCamelCase__			,**lowerCamelCase__			)






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """align"""
	lowercase__      =      True





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    Tuple=None			,lowerCamelCase__ :    Any=None			,lowerCamelCase__ :    List[str]=640			,lowerCamelCase__ :    Tuple=1.0			,lowerCamelCase__ :    str=0.0_2			,**lowerCamelCase__ :    int			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)

							if text_config is None:
													_UpperCamelCase :		Any			    =       {}
													logger.info('text_config is None. Initializing the AlignTextConfig with default values.'			)

							if vision_config is None:
													_UpperCamelCase :		str			    =       {}
													logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'			)

							_UpperCamelCase :		Optional[int]			    =       AlignTextConfig(**lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       AlignVisionConfig(**lowerCamelCase__			)

							_UpperCamelCase :		Optional[int]			    =       projection_dim
							_UpperCamelCase :		List[Any]			    =       temperature_init_value
							_UpperCamelCase :		List[Any]			    =       initializer_range





	@classmethod
	def      UpperCamelCase_  (			cls :    Dict			,lowerCamelCase__ :    AlignTextConfig			,lowerCamelCase__ :    AlignVisionConfig			,**lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							return cls(text_config=text_config.to_dict()			,vision_config=vision_config.to_dict()			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       copy.deepcopy(self.__dict__			)
							_UpperCamelCase :		Any			    =       self.text_config.to_dict()
							_UpperCamelCase :		Tuple			    =       self.vision_config.to_dict()
							_UpperCamelCase :		Dict			    =       self.__class__.model_type
							return output







'''simple docstring'''
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						_UpperCamelCase :		str			    =       (low + high) // 2
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Dict			    =       max_subarray(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       max_subarray(UpperCAmelCase_  ,  mid + 1  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Tuple			    =       max_cross_sum(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						if left_sum >= right_sum and left_sum >= cross_sum:
												return left_low, left_high, left_sum
						elif right_sum >= left_sum and right_sum >= cross_sum:
												return right_low, right_high, right_sum
						return cross_left, cross_right, cross_sum






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       float('-inf'							), -1
						_UpperCamelCase    ,      _UpperCamelCase :		int			    =       float('-inf'							), -1

						_UpperCamelCase :		int | float			    =       0
						for i in range(UpperCAmelCase_  ,  low - 1  ,  -1							):
												summ += arr[i]
												if summ > left_sum:
																		_UpperCamelCase :		Optional[int]			    =       summ
																		_UpperCamelCase :		Union[str, Any]			    =       i

						_UpperCamelCase :		List[Any]			    =       0
						for i in range(mid + 1  ,  high + 1							):
												summ += arr[i]
												if summ > right_sum:
																		_UpperCamelCase :		List[Any]			    =       summ
																		_UpperCamelCase :		List[str]			    =       i

						return max_left, max_right, (left_sum + right_sum)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       [randint(1  ,  UpperCAmelCase_							) for _ in range(UpperCAmelCase_							)]
						_UpperCamelCase :		Optional[Any]			    =       time.time()
						max_subarray(UpperCAmelCase_  ,  0  ,  input_size - 1							)
						_UpperCamelCase :		str			    =       time.time()
						return end - start






def A__   (					):
						_UpperCamelCase :		Any			    =       [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
						_UpperCamelCase :		Dict			    =       [time_max_subarray(UpperCAmelCase_							) for input_size in input_sizes]
						print('No of Inputs\t\tTime Taken'							)
						for input_size, runtime in zip(UpperCAmelCase_  ,  UpperCAmelCase_							):
												print(UpperCAmelCase_  ,  '\t\t'  ,  UpperCAmelCase_							)
						plt.plot(UpperCAmelCase_  ,  UpperCAmelCase_							)
						plt.xlabel('Number of Inputs'							)
						plt.ylabel('Time taken in seconds'							)
						plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()


