




'''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


a  :				str									=  logging.get_logger(__name__)

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






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"align_text_model"


				def __init__(		self    :					Any       ,				lowercase_    :					str=3_0522       ,				lowercase_    :					int=768       ,				lowercase_    :					Tuple=12       ,				lowercase_    :					int=12       ,				lowercase_    :					Dict=3072       ,				lowercase_    :					List[Any]="gelu"       ,				lowercase_    :					str=0.1       ,				lowercase_    :					Optional[Any]=0.1       ,				lowercase_    :					Any=512       ,				lowercase_    :					Dict=2       ,				lowercase_    :					Optional[int]=0.02       ,				lowercase_    :					Union[str, Any]=1e-12       ,				lowercase_    :					Optional[int]=0       ,				lowercase_    :					Union[str, Any]="absolute"       ,				lowercase_    :					Tuple=True       ,				**lowercase_    :					List[str]       ,				):
									super().__init__(**lowercase_					)

									snake_case_									=				vocab_size
									snake_case_									=				hidden_size
									snake_case_									=				num_hidden_layers
									snake_case_									=				num_attention_heads
									snake_case_									=				hidden_act
									snake_case_									=				intermediate_size
									snake_case_									=				hidden_dropout_prob
									snake_case_									=				attention_probs_dropout_prob
									snake_case_									=				max_position_embeddings
									snake_case_									=				type_vocab_size
									snake_case_									=				initializer_range
									snake_case_									=				layer_norm_eps
									snake_case_									=				position_embedding_type
									snake_case_									=				use_cache
									snake_case_									=				pad_token_id


				@classmethod
				def   A_ (		cls    :					int       ,				lowercase_    :					Union[str, os.PathLike]       ,				**lowercase_    :					Tuple					):
									cls._set_token_in_kwargs(lowercase_					)

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

									# get the text config dict if we are loading from AlignConfig
									if config_dict.get('''model_type'''					) == "align":
														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(lowercase_       ,				**lowercase_					)







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"align_vision_model"


				def __init__(		self    :					Any       ,				lowercase_    :					int = 3       ,				lowercase_    :					int = 600       ,				lowercase_    :					float = 2.0       ,				lowercase_    :					float = 3.1       ,				lowercase_    :					int = 8       ,				lowercase_    :					List[int] = [3, 3, 5, 3, 5, 5, 3]       ,				lowercase_    :					List[int] = [32, 16, 24, 40, 80, 112, 192]       ,				lowercase_    :					List[int] = [16, 24, 40, 80, 112, 192, 320]       ,				lowercase_    :					List[int] = []       ,				lowercase_    :					List[int] = [1, 2, 2, 2, 1, 2, 1]       ,				lowercase_    :					List[int] = [1, 2, 2, 3, 3, 4, 1]       ,				lowercase_    :					List[int] = [1, 6, 6, 6, 6, 6, 6]       ,				lowercase_    :					float = 0.25       ,				lowercase_    :					str = "swish"       ,				lowercase_    :					int = 2560       ,				lowercase_    :					str = "mean"       ,				lowercase_    :					float = 0.02       ,				lowercase_    :					float = 0.001       ,				lowercase_    :					float = 0.99       ,				lowercase_    :					float = 0.2       ,				**lowercase_    :					Any       ,				):
									super().__init__(**lowercase_					)

									snake_case_									=				num_channels
									snake_case_									=				image_size
									snake_case_									=				width_coefficient
									snake_case_									=				depth_coefficient
									snake_case_									=				depth_divisor
									snake_case_									=				kernel_sizes
									snake_case_									=				in_channels
									snake_case_									=				out_channels
									snake_case_									=				depthwise_padding
									snake_case_									=				strides
									snake_case_									=				num_block_repeats
									snake_case_									=				expand_ratios
									snake_case_									=				squeeze_expansion_ratio
									snake_case_									=				hidden_act
									snake_case_									=				hidden_dim
									snake_case_									=				pooling_type
									snake_case_									=				initializer_range
									snake_case_									=				batch_norm_eps
									snake_case_									=				batch_norm_momentum
									snake_case_									=				drop_connect_rate
									snake_case_									=				sum(lowercase_					) * 4


				@classmethod
				def   A_ (		cls    :					Optional[int]       ,				lowercase_    :					Union[str, os.PathLike]       ,				**lowercase_    :					Any					):
									cls._set_token_in_kwargs(lowercase_					)

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

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







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"align"
				snake_case_      =		True


				def __init__(		self    :					List[Any]       ,				lowercase_    :					int=None       ,				lowercase_    :					List[Any]=None       ,				lowercase_    :					Dict=640       ,				lowercase_    :					Optional[int]=1.0       ,				lowercase_    :					Any=0.02       ,				**lowercase_    :					Optional[int]       ,				):
									super().__init__(**lowercase_					)

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

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

									snake_case_									=				AlignTextConfig(**lowercase_					)
									snake_case_									=				AlignVisionConfig(**lowercase_					)

									snake_case_									=				projection_dim
									snake_case_									=				temperature_init_value
									snake_case_									=				initializer_range


				@classmethod
				def   A_ (		cls    :					Optional[int]       ,				lowercase_    :					AlignTextConfig       ,				lowercase_    :					AlignVisionConfig       ,				**lowercase_    :					List[str]					):
									return cls(text_config=text_config.to_dict()       ,				vision_config=vision_config.to_dict()       ,				**lowercase_					)


				def   A_ (		self    :					Tuple					):
									snake_case_									=				copy.deepcopy(self.__dict__					)
									snake_case_									=				self.text_config.to_dict()
									snake_case_									=				self.vision_config.to_dict()
									snake_case_									=				self.__class__.model_type
									return output


import random



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		a[left_index]
    __UpperCamelCase     :Any			  =		left_index + 1
    for j in range(left_index + 1  ,			SCREAMING_SNAKE_CASE	):
        if a[j] < pivot:
            __UpperCamelCase  ,       __UpperCamelCase     :str			  =		a[i], a[j]
            i += 1
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		a[i - 1], a[left_index]
    return i - 1



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if left < right:
        __UpperCamelCase     :int			  =		random.randint(SCREAMING_SNAKE_CASE  ,			right - 1	)
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        __UpperCamelCase     :Dict			  =		partition(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			pivot_index + 1  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the right of the pivot point



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		input('''Enter numbers separated by a comma:\n'''	).strip()
    __UpperCamelCase     :Union[str, Any]			  =		[int(SCREAMING_SNAKE_CASE	) for item in user_input.split(''','''	)]

    quick_sort_random(SCREAMING_SNAKE_CASE  ,			0  ,			len(SCREAMING_SNAKE_CASE	)	)

    print(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





