





import copy
import os
from typing import Union

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


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}
class        snake_case_		(			UpperCamelCase__):
		lowerCamelCase    :Optional[int]						=     "bridgetower_vision_model"







		def __init__(		self			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=3			,     __lowercase=1_6			,     __lowercase=2_8_8			,     __lowercase=1			,     __lowercase=1e-05			,     __lowercase=False			,     __lowercase=True			,     __lowercase=False			,     **__lowercase			,     )				->   List[str]:
				super().__init__(**_a   )
				lowerCamelCase   : int								=hidden_size
				lowerCamelCase   : Optional[int]								=num_hidden_layers
				lowerCamelCase   : Tuple								=num_channels
				lowerCamelCase   : Optional[Any]								=patch_size
				lowerCamelCase   : Optional[Any]								=image_size
				lowerCamelCase   : List[Any]								=initializer_factor
				lowerCamelCase   : Tuple								=layer_norm_eps
				lowerCamelCase   : List[Any]								=stop_gradient
				lowerCamelCase   : Dict								=share_layernorm
				lowerCamelCase   : Optional[Any]								=remove_last_layer







		@classmethod
		def 			__lowercase   (		cls			,     __lowercase			,     **__lowercase   )				->   "PretrainedConfig":
				lowerCamelCase   : List[Any]								=cls.get_config_dict(_a			,     **_a   )

				if config_dict.get('''model_type'''   ) == "bridgetower":
						lowerCamelCase   : 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(_a			,     **_a   )

class        snake_case_		(			UpperCamelCase__):
		lowerCamelCase    :Optional[int]						=     "bridgetower_text_model"







		def __init__(		self			,     __lowercase=5_0_2_6_5			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=1_2			,     __lowercase=1			,     __lowercase=3_0_7_2			,     __lowercase="gelu"			,     __lowercase=0.1			,     __lowercase=0.1			,     __lowercase=5_1_4			,     __lowercase=1			,     __lowercase=1e-05			,     __lowercase=1			,     __lowercase=0			,     __lowercase=2			,     __lowercase="absolute"			,     __lowercase=True			,     **__lowercase			,     )				->   Optional[Any]:
				super().__init__(**_a   )

				lowerCamelCase   : List[str]								=vocab_size
				lowerCamelCase   : Optional[int]								=hidden_size
				lowerCamelCase   : Union[str, Any]								=num_hidden_layers
				lowerCamelCase   : Optional[Any]								=num_attention_heads
				lowerCamelCase   : Optional[Any]								=hidden_act
				lowerCamelCase   : List[str]								=initializer_factor
				lowerCamelCase   : Any								=intermediate_size
				lowerCamelCase   : List[str]								=hidden_dropout_prob
				lowerCamelCase   : Tuple								=attention_probs_dropout_prob
				lowerCamelCase   : List[Any]								=max_position_embeddings
				lowerCamelCase   : Optional[int]								=type_vocab_size
				lowerCamelCase   : Optional[Any]								=layer_norm_eps
				lowerCamelCase   : List[Any]								=position_embedding_type
				lowerCamelCase   : List[str]								=use_cache
				lowerCamelCase   : Tuple								=pad_token_id
				lowerCamelCase   : List[Any]								=bos_token_id
				lowerCamelCase   : Dict								=eos_token_id







		@classmethod
		def 			__lowercase   (		cls			,     __lowercase			,     **__lowercase   )				->   "PretrainedConfig":
				lowerCamelCase   : Tuple								=cls.get_config_dict(_a			,     **_a   )

				if config_dict.get('''model_type'''   ) == "bridgetower":
						lowerCamelCase   : int								=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(_a			,     **_a   )

class        snake_case_		(			UpperCamelCase__):
		lowerCamelCase    :Union[str, Any]						=     "bridgetower"







		def __init__(		self			,     __lowercase=True			,     __lowercase="gelu"			,     __lowercase=7_6_8			,     __lowercase=1			,     __lowercase=1e-05			,     __lowercase=False			,     __lowercase="add"			,     __lowercase=1_2			,     __lowercase=6			,     __lowercase=False			,     __lowercase=False			,     __lowercase=None			,     __lowercase=None			,     **__lowercase			,     )				->   List[Any]:
				# TODO: remove this once the Hub files are updated.
				lowerCamelCase   : Optional[Any]								=kwargs.pop('''text_config_dict'''			,     _a   )
				lowerCamelCase   : Optional[Any]								=kwargs.pop('''vision_config_dict'''			,     _a   )

				super().__init__(**_a   )
				lowerCamelCase   : Dict								=share_cross_modal_transformer_layers
				lowerCamelCase   : List[Any]								=hidden_act
				lowerCamelCase   : List[Any]								=hidden_size
				lowerCamelCase   : Union[str, Any]								=initializer_factor
				lowerCamelCase   : str								=layer_norm_eps
				lowerCamelCase   : Dict								=share_link_tower_layers
				lowerCamelCase   : Optional[Any]								=link_tower_type
				lowerCamelCase   : List[str]								=num_attention_heads
				lowerCamelCase   : List[Any]								=num_hidden_layers
				lowerCamelCase   : Union[str, Any]								=tie_word_embeddings
				lowerCamelCase   : Union[str, Any]								=init_layernorm_from_vision_encoder

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

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

				lowerCamelCase   : Optional[Any]								=BridgeTowerTextConfig(**_a   )
				lowerCamelCase   : str								=BridgeTowerVisionConfig(**_a   )







		@classmethod
		def 			__lowercase   (		cls			,     __lowercase			,     __lowercase			,     **__lowercase   )				->   Tuple:
				return cls(text_config=text_config.to_dict()			,     vision_config=vision_config.to_dict()			,     **_a   )







		def 			__lowercase   (		self   )				->   Optional[Any]:
				lowerCamelCase   : Optional[int]								=copy.deepcopy(self.__dict__   )
				lowerCamelCase   : List[Any]								=self.text_config.to_dict()
				lowerCamelCase   : int								=self.vision_config.to_dict()
				lowerCamelCase   : int								=self.__class__.model_type
				return output








import string

import numpy


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     int:
		return b if a == 0 else greatest_common_divisor(b % a							,       SCREAMING_SNAKE_CASE_		)
class        snake_case_		:
		lowerCamelCase    :Tuple						=     string.ascii_uppercase + string.digits
		# This cipher takes alphanumerics into account
		# i.e. a total of 36 characters

		# take x and return x % len(key_string)
		lowerCamelCase    :str						=     numpy.vectorize(lambda _A: x % 36)

		lowerCamelCase    :str						=     numpy.vectorize(_A)







		def __init__(		self			,     __lowercase   )				->   None:
				lowerCamelCase   : Union[str, Any]								=self.modulus(__lowercase   )  # mod36 calc's on the encrypt key
				self.check_determinant()  # validate the determinant of the encryption key
				lowerCamelCase   : int								=encrypt_key.shape[0]







		def 			__lowercase   (		self			,     __lowercase   )				->   int:
				return self.key_string.index(__lowercase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				return self.key_string[round(__lowercase   )]







		def 			__lowercase   (		self   )				->   None:
				lowerCamelCase   : Tuple								=round(numpy.linalg.det(self.encrypt_key   )   )

				if det < 0:
						lowerCamelCase   : Dict								=det % len(self.key_string   )

				lowerCamelCase   : Dict								=len(self.key_string   )
				if greatest_common_divisor(__lowercase			,     len(self.key_string   )   ) != 1:
						lowerCamelCase   : Union[str, Any]								=(
						    F"determinant modular {req_l} of encryption key({det}) "
						    F"is not co prime w.r.t {req_l}.\nTry another key."
						)
						raise ValueError(__lowercase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				lowerCamelCase   : int								=[char for char in text.upper() if char in self.key_string]

				lowerCamelCase   : Tuple								=chars[-1]
				while len(__lowercase   ) % self.break_key != 0:
						chars.append(__lowercase   )

				return "".join(__lowercase   )







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				lowerCamelCase   : str								=self.process_text(text.upper()   )
				lowerCamelCase   : Optional[int]								=''''''

				for i in range(0			,     len(__lowercase   ) - self.break_key + 1			,     self.break_key   ):
						lowerCamelCase   : Union[str, Any]								=text[i : i + self.break_key]
						lowerCamelCase   : Optional[int]								=[self.replace_letters(__lowercase   ) for char in batch]
						lowerCamelCase   : int								=numpy.array([vec]   ).T
						lowerCamelCase   : Optional[Any]								=self.modulus(self.encrypt_key.dot(__lowercase   )   ).T.tolist()[
						    0
						]
						lowerCamelCase   : int								=''''''.join(
						    self.replace_digits(__lowercase   ) for num in batch_encrypted   )
						encrypted += encrypted_batch

				return encrypted







		def 			__lowercase   (		self   )				->   numpy.ndarray:
				lowerCamelCase   : List[Any]								=round(numpy.linalg.det(self.encrypt_key   )   )

				if det < 0:
						lowerCamelCase   : int								=det % len(self.key_string   )
				lowerCamelCase   : Any								=None
				for i in range(len(self.key_string   )   ):
						if (det * i) % len(self.key_string   ) == 1:
								lowerCamelCase   : int								=i
								break

				lowerCamelCase   : Union[str, Any]								=(
				    det_inv
				    * numpy.linalg.det(self.encrypt_key   )
				    * numpy.linalg.inv(self.encrypt_key   )
				)

				return self.to_int(self.modulus(__lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				lowerCamelCase   : Optional[int]								=self.make_decrypt_key()
				lowerCamelCase   : int								=self.process_text(text.upper()   )
				lowerCamelCase   : Any								=''''''

				for i in range(0			,     len(__lowercase   ) - self.break_key + 1			,     self.break_key   ):
						lowerCamelCase   : int								=text[i : i + self.break_key]
						lowerCamelCase   : str								=[self.replace_letters(__lowercase   ) for char in batch]
						lowerCamelCase   : Optional[int]								=numpy.array([vec]   ).T
						lowerCamelCase   : Optional[int]								=self.modulus(decrypt_key.dot(__lowercase   )   ).T.tolist()[0]
						lowerCamelCase   : Tuple								=''''''.join(
						    self.replace_digits(__lowercase   ) for num in batch_decrypted   )
						decrypted += decrypted_batch

				return decrypted


def 					A__	(       )   ->     None:
		lowerCamelCase   : str								=int(input('''Enter the order of the encryption key: '''		)		)
		lowerCamelCase   : Optional[Any]								=[]

		print('''Enter each row of the encryption key with space separated integers'''		)
		for _ in range(SCREAMING_SNAKE_CASE_		):
				lowerCamelCase   : Optional[Any]								=[int(SCREAMING_SNAKE_CASE_		) for x in input().split()]
				hill_matrix.append(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase   : Tuple								=HillCipher(numpy.array(SCREAMING_SNAKE_CASE_		)		)

		print('''Would you like to encrypt or decrypt some text? (1 or 2)'''		)
		lowerCamelCase   : Tuple								=input('''\n1. Encrypt\n2. Decrypt\n'''		)
		if option == "1":
				lowerCamelCase   : List[str]								=input('''What text would you like to encrypt?: '''		)
				print('''Your encrypted text is:'''		)
				print(hc.encrypt(SCREAMING_SNAKE_CASE_		)		)
		elif option == "2":
				lowerCamelCase   : int								=input('''What text would you like to decrypt?: '''		)
				print('''Your decrypted text is:'''		)
				print(hc.decrypt(SCREAMING_SNAKE_CASE_		)		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				main()

