




"""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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

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




class      UpperCAmelCase_  (						_lowerCamelCase							):
				lowercase__						 =				'''align_text_model'''




				def __init__(   self						:	Any     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Dict=768     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	Optional[Any]=12     ,			snake_case_						:	Optional[int]=3_072     ,			snake_case_						:	Optional[Any]="gelu"     ,			snake_case_						:	Dict=0.1     ,			snake_case_						:	Optional[Any]=0.1     ,			snake_case_						:	List[Any]=512     ,			snake_case_						:	Optional[int]=2     ,			snake_case_						:	Optional[Any]=0.02     ,			snake_case_						:	str=1e-12     ,			snake_case_						:	Any=0     ,			snake_case_						:	Optional[int]="absolute"     ,			snake_case_						:	Union[str, Any]=True     ,			**snake_case_						:	Dict     ,			)				->							Tuple:




					'''simple docstring'''

					super().__init__(**lowercase_				)

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




				@classmethod
				def 							__magic_name__							(   cls						:	Optional[Any]     ,			snake_case_						:	Union[str, os.PathLike]     ,			**snake_case_						:	Tuple				)				->							List[Any]:




					'''simple docstring'''

					cls._set_token_in_kwargs(lowercase_				)

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

					# get the text config dict if we are loading from AlignConfig
					if config_dict.get("model_type"				) == "align":
						A__	=    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      UpperCAmelCase_  (						_lowerCamelCase							):
				lowercase__						 =				'''align_vision_model'''




				def __init__(   self						:	Tuple     ,			snake_case_						:	int = 3     ,			snake_case_						:	int = 600     ,			snake_case_						:	float = 2.0     ,			snake_case_						:	float = 3.1     ,			snake_case_						:	int = 8     ,			snake_case_						:	List[int] = [3, 3, 5, 3, 5, 5, 3]     ,			snake_case_						:	List[int] = [32, 16, 24, 40, 80, 112, 192]     ,			snake_case_						:	List[int] = [16, 24, 40, 80, 112, 192, 320]     ,			snake_case_						:	List[int] = []     ,			snake_case_						:	List[int] = [1, 2, 2, 2, 1, 2, 1]     ,			snake_case_						:	List[int] = [1, 2, 2, 3, 3, 4, 1]     ,			snake_case_						:	List[int] = [1, 6, 6, 6, 6, 6, 6]     ,			snake_case_						:	float = 0.25     ,			snake_case_						:	str = "swish"     ,			snake_case_						:	int = 2_560     ,			snake_case_						:	str = "mean"     ,			snake_case_						:	float = 0.02     ,			snake_case_						:	float = 0.001     ,			snake_case_						:	float = 0.99     ,			snake_case_						:	float = 0.2     ,			**snake_case_						:	List[Any]     ,			)				->							Optional[int]:




					'''simple docstring'''

					super().__init__(**lowercase_				)

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




				@classmethod
				def 							__magic_name__							(   cls						:	List[str]     ,			snake_case_						:	Union[str, os.PathLike]     ,			**snake_case_						:	str				)				->							List[str]:




					'''simple docstring'''

					cls._set_token_in_kwargs(lowercase_				)

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

					# get the vision config dict if we are loading from AlignConfig
					if config_dict.get("model_type"				) == "align":
						A__	=    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      UpperCAmelCase_  (						_lowerCamelCase							):
				lowercase__						 =				'''align'''
				lowercase__						 =				True




				def __init__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Any=None     ,			snake_case_						:	List[str]=640     ,			snake_case_						:	Any=1.0     ,			snake_case_						:	Union[str, Any]=0.02     ,			**snake_case_						:	int     ,			)				->							str:




					'''simple docstring'''

					super().__init__(**lowercase_				)

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

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

					A__	=    AlignTextConfig(**lowercase_				)
					A__	=    AlignVisionConfig(**lowercase_				)

					A__	=    projection_dim
					A__	=    temperature_init_value
					A__	=    initializer_range




				@classmethod
				def 							__magic_name__							(   cls						:	Any     ,			snake_case_						:	AlignTextConfig     ,			snake_case_						:	AlignVisionConfig     ,			**snake_case_						:	int				)				->							Dict:




					'''simple docstring'''

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




				def 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




					'''simple docstring'''

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











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

 if n % 2 == 0:
  return n == 2

 # this means n is odd
 A__	=    n - 1
 A__	=    0
 while d % 2 == 0:
  d /= 2
  exp += 1

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	abs(int(input("Enter bound : ").strip()))
      print("Here's the list of primes:")
      print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





