





"""simple docstring"""




import copy
import os
from typing import Union

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


a						: Tuple     	=			logging.get_logger(__name__)

a						: Tuple     	=			{
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					="""git_vision_model"""






							def __init__(   self      ,					lowerCAmelCase__=768      ,					lowerCAmelCase__=3072      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=3      ,					lowerCAmelCase__=224      ,					lowerCAmelCase__=16      ,					lowerCAmelCase__="quick_gelu"      ,					lowerCAmelCase__=1E-5      ,					lowerCAmelCase__=0.0      ,					lowerCAmelCase__=0.02      ,					**lowerCAmelCase__      ,					)	-> Any:
								super().__init__(**lowerCAmelCase__      )

								a      :		str	=				hidden_size
								a      :		Optional[int]	=				intermediate_size
								a      :		List[str]	=				num_hidden_layers
								a      :		Any	=				num_attention_heads
								a      :		List[str]	=				num_channels
								a      :		int	=				patch_size
								a      :		Tuple	=				image_size
								a      :		Dict	=				initializer_range
								a      :		Optional[int]	=				attention_dropout
								a      :		Dict	=				layer_norm_eps
								a      :		Optional[int]	=				hidden_act






							@classmethod
							def     __a   (   cls      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      )	-> "PretrainedConfig":
								cls._set_token_in_kwargs(lowerCAmelCase__      )

								a,       a      :		Any	=				cls.get_config_dict(lowerCAmelCase__      ,					**lowerCAmelCase__      )

								# get the vision config dict if we are loading from GITConfig
								if config_dict.get("model_type"      ) == "git":
									a      :		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        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					int      					="""git"""






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=3_0522      ,					lowerCAmelCase__=768      ,					lowerCAmelCase__=6      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=3072      ,					lowerCAmelCase__="gelu"      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=1024      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=1E-12      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__="absolute"      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__=101      ,					lowerCAmelCase__=102      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__(bos_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      ,					pad_token_id=lowerCAmelCase__      ,					**lowerCAmelCase__      )

								if vision_config is None:
									a      :		List[Any]	=				{}
									logger.info("vision_config is None. initializing the GitVisionConfig with default values."      )

								a      :		List[str]	=				GitVisionConfig(**lowerCAmelCase__      )
								a      :		int	=				vocab_size
								a      :		Optional[Any]	=				hidden_size
								a      :		Tuple	=				num_hidden_layers
								a      :		Union[str, Any]	=				num_attention_heads
								a      :		Union[str, Any]	=				hidden_act
								a      :		List[Any]	=				intermediate_size
								a      :		Tuple	=				hidden_dropout_prob
								a      :		List[Any]	=				attention_probs_dropout_prob
								a      :		Union[str, Any]	=				max_position_embeddings
								a      :		Tuple	=				initializer_range
								a      :		int	=				layer_norm_eps
								a      :		List[str]	=				position_embedding_type
								a      :		int	=				use_cache
								a      :		List[Any]	=				tie_word_embeddings
								a      :		str	=				num_image_with_embedding

								a      :		List[Any]	=				bos_token_id
								a      :		int	=				eos_token_id






							def     __a   (   self      )	-> int:
								a      :		Union[str, Any]	=				copy.deepcopy(self.__dict__      )
								a      :		Optional[int]	=				self.vision_config.to_dict()
								a      :		List[str]	=				self.__class__.model_type
								return output









"""simple docstring"""




import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int = 8					) ->str:






	'''simple docstring'''






	a      :		List[Any]	=				ascii_letters + digits + punctuation
	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	i -= len(_lowercase					)
	a      :		List[str]	=				i // 3
	a      :		Any	=				i % 3
	# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
	#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
	a      :		int	=				(
	    chars_incl
	    + random(_lowercase    ,       quotient + remainder					)
	    + random(_lowercase    ,       _lowercase					)
	    + random(_lowercase    ,       _lowercase					)
	)
	a      :		List[str]	=				list(_lowercase					)
	shuffle(_lowercase					)
	return "".join(_lowercase					)

	# random is a generalised function for letters, characters and numbers






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	return "".join(secrets.choice(_lowercase					) for _ in range(_lowercase					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]    ,       _lowercase						:    int					) ->List[str]:






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[Any]    ,       _lowercase						:    Optional[int]					) ->int:






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    Optional[Any]					) ->Any:






	'''simple docstring'''






	pass  # Put your code here...






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    int = 8					) ->bool:






	'''simple docstring'''






	if len(_lowercase					) < min_length:
		# Your Password must be at least 8 characters long
		return False

	a      :		List[str]	=				any(char in ascii_uppercase for char in password					)
	a      :		Optional[int]	=				any(char in ascii_lowercase for char in password					)
	a      :		List[str]	=				any(char in digits for char in password					)
	a      :		int	=				any(char in punctuation for char in password					)

	return upper and lower and num and spec_char
	# Passwords should contain UPPERCASE, lowerase
	# numbers, and special characters






def       _SCREAMING_SNAKE_CASE		(	) ->Union[str, Any]:






	'''simple docstring'''






	a      :		Dict	=				int(input("Please indicate the max length of your password: "					).strip()					)
	a      :		str	=				input(
	    "Please indicate the characters that must be in your password: "					).strip()
	print("Password generated:"    ,       password_generator(_lowercase					)					)
	print(
	    "Alternative Password generated:"    ,       alternative_password_generator(_lowercase    ,       _lowercase					)    ,       )
	print("[If you are thinking of using this passsword, You better save it.]"					)


if __name__ == "__main__":
		main()


