import copy
import os
from typing import Union

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


SCREAMING_SNAKE_CASE		:     Dict         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Optional[Any]         =	{
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}

class        UpperCamelCase						(				__a				):
						a__		:Any      =			'''blip_text_model'''





						def __init__(self			,     __UpperCamelCase=30_524			,     __UpperCamelCase=768			,     __UpperCamelCase=768			,     __UpperCamelCase=3_072			,     __UpperCamelCase=768			,     __UpperCamelCase=12			,     __UpperCamelCase=8			,     __UpperCamelCase=512			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=1E-1_2			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.0			,     __UpperCamelCase=0.02			,     __UpperCamelCase=30_522			,     __UpperCamelCase=2			,     __UpperCamelCase=0			,     __UpperCamelCase=102			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				List[str]:
												super().__init__(
												    pad_token_id=__UpperCamelCase			,     bos_token_id=__UpperCamelCase			,     eos_token_id=__UpperCamelCase			,     sep_token_id=__UpperCamelCase			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							Tuple  =		vocab_size
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							str  =		encoder_hidden_size
												UpperCamelCase_		:							Optional[Any]  =		intermediate_size
												UpperCamelCase_		:							List[Any]  =		projection_dim
												UpperCamelCase_		:							Optional[Any]  =		hidden_dropout_prob
												UpperCamelCase_		:							Optional[Any]  =		num_hidden_layers
												UpperCamelCase_		:							Any  =		num_attention_heads
												UpperCamelCase_		:							List[str]  =		max_position_embeddings
												UpperCamelCase_		:							Optional[int]  =		layer_norm_eps
												UpperCamelCase_		:							Dict  =		hidden_act
												UpperCamelCase_		:							List[Any]  =		initializer_range
												UpperCamelCase_		:							Union[str, Any]  =		attention_probs_dropout_prob
												UpperCamelCase_		:							Tuple  =		is_decoder
												UpperCamelCase_		:							Dict  =		use_cache





						@classmethod
						def        A_							(cls			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				"PretrainedConfig":
												cls._set_token_in_kwargs(__UpperCamelCase						)

												UpperCamelCase_,UpperCamelCase_		:							Tuple  =		cls.get_config_dict(__UpperCamelCase			,     **__UpperCamelCase						)

												# get the text config dict if we are loading from BlipConfig
												if config_dict.get("""model_type"""						) == "blip":
																		UpperCamelCase_		:							Tuple  =		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(__UpperCamelCase			,     **__UpperCamelCase						)


class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			'''blip_vision_model'''





						def __init__(self			,     __UpperCamelCase=768			,     __UpperCamelCase=3_072			,     __UpperCamelCase=512			,     __UpperCamelCase=12			,     __UpperCamelCase=12			,     __UpperCamelCase=384			,     __UpperCamelCase=16			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=1E-5			,     __UpperCamelCase=0.0			,     __UpperCamelCase=1E-1_0			,     **__UpperCamelCase			,     )				->				List[Any]:
												super().__init__(**__UpperCamelCase						)

												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Union[str, Any]  =		intermediate_size
												UpperCamelCase_		:							Optional[int]  =		projection_dim
												UpperCamelCase_		:							List[Any]  =		num_hidden_layers
												UpperCamelCase_		:							Tuple  =		num_attention_heads
												UpperCamelCase_		:							Tuple  =		patch_size
												UpperCamelCase_		:							Optional[Any]  =		image_size
												UpperCamelCase_		:							List[Any]  =		initializer_range
												UpperCamelCase_		:							Optional[Any]  =		attention_dropout
												UpperCamelCase_		:							Optional[Any]  =		layer_norm_eps
												UpperCamelCase_		:							Dict  =		hidden_act





						@classmethod
						def        A_							(cls			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				"PretrainedConfig":
												cls._set_token_in_kwargs(__UpperCamelCase						)

												UpperCamelCase_,UpperCamelCase_		:							str  =		cls.get_config_dict(__UpperCamelCase			,     **__UpperCamelCase						)

												# get the vision config dict if we are loading from BlipConfig
												if config_dict.get("""model_type"""						) == "blip":
																		UpperCamelCase_		:							Optional[Any]  =		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(__UpperCamelCase			,     **__UpperCamelCase						)


class        UpperCamelCase						(				__a				):
						a__		:Dict      =			'''blip'''
						a__		:List[Any]      =			True





						def __init__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=512			,     __UpperCamelCase=2.6_592			,     __UpperCamelCase=256			,     **__UpperCamelCase			,     )				->				Dict:
												super().__init__(**__UpperCamelCase						)

												if text_config is None:
																		UpperCamelCase_		:							List[str]  =		{}
																		logger.info("""`text_config` is `None`. Initializing the `BlipTextConfig` with default values."""						)

												if vision_config is None:
																		UpperCamelCase_		:							Dict  =		{}
																		logger.info("""`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."""						)

												UpperCamelCase_		:							str  =		BlipTextConfig(**__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		BlipVisionConfig(**__UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		self.vision_config.hidden_size

												UpperCamelCase_		:							int  =		projection_dim
												UpperCamelCase_		:							Any  =		logit_scale_init_value
												UpperCamelCase_		:							Union[str, Any]  =		1.0
												UpperCamelCase_		:							str  =		0.02
												UpperCamelCase_		:							List[str]  =		image_text_hidden_size





						@classmethod
						def        A_							(cls			,     __UpperCamelCase			,     __UpperCamelCase			,     **__UpperCamelCase						)				->				Optional[int]:
												return cls(text_config=text_config.to_dict()			,     vision_config=vision_config.to_dict()			,     **__UpperCamelCase						)





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							str  =		copy.deepcopy(self.__dict__						)
												UpperCamelCase_		:							Dict  =		self.text_config.to_dict()
												UpperCamelCase_		:							Tuple  =		self.vision_config.to_dict()
												UpperCamelCase_		:							List[Any]  =		self.__class__.model_type
												return output







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812






def     lowerCAmelCase_			(   ):
						print("""Making key files..."""			)
						make_key_files("""rsa"""		, 1024			)
						print("""Key files generation successful."""			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		random.randrange(2 ** (key_size - 1)		, 2 ** (key_size)			)
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			) == 1:
																		break

						print("""Calculating d that is mod inverse of e..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				int			):
						if os.path.exists(f'''{name}_pubkey.txt'''			) or os.path.exists(f'''{name}_privkey.txt'''			):
												print("""\nWARNING:"""			)
												print(
												    f'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
												    """Use a different name or delete these files and re-run this program."""			)
												sys.exit()

						UpperCamelCase_,UpperCamelCase_		:							str  =		generate_key(_SCREAMING_SNAKE_CASE			)
						print(f'''\nWriting public key to file {name}_pubkey.txt...'''			)
						with open(f'''{name}_pubkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{public_key[0]},{public_key[1]}'''			)

						print(f'''Writing private key to file {name}_privkey.txt...'''			)
						with open(f'''{name}_privkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{private_key[0]},{private_key[1]}'''			)


if __name__ == "__main__":
	main()






