"""simple docstring"""






import os
import sys

from . import rsa_key_generator as rkg

lowerCAmelCase__  :  int											=     128
lowerCAmelCase__  :  Tuple											=     256



def   a_						( lowerCamelCase ,      lowerCamelCase = DEFAULT_BLOCK_SIZE	):
				UpperCAmelCase__					=							message.encode('ascii'	)

				UpperCAmelCase__					=							[]
				for block_start in range(0 ,      len(lowerCamelCase	) ,      lowerCamelCase	):
								UpperCAmelCase__					=							0
								for i in range(lowerCamelCase ,      min(block_start + block_size ,      len(lowerCamelCase	)	)	):
												block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
								block_ints.append(lowerCamelCase	)
				return block_ints



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = DEFAULT_BLOCK_SIZE	):
				UpperCAmelCase__					=							[]
				for block_int in block_ints:
								UpperCAmelCase__					=							[]
								for i in range(block_size - 1 ,      -1 ,      -1	):
												if len(lowerCamelCase	) + i < message_length:
																UpperCAmelCase__					=							block_int // (BYTE_SIZE**i)
																UpperCAmelCase__					=							block_int % (BYTE_SIZE**i)
																block_message.insert(0 ,      chr(lowerCamelCase	)	)
								message.extend(lowerCamelCase	)
				return "".join(lowerCamelCase	)



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = DEFAULT_BLOCK_SIZE	):
				UpperCAmelCase__					=							[]
				UpperCAmelCase__				,     UpperCAmelCase__					=							key

				for block in get_blocks_from_text(lowerCamelCase ,      lowerCamelCase	):
								encrypted_blocks.append(pow(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)	)
				return encrypted_blocks



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = DEFAULT_BLOCK_SIZE ,      ):
				UpperCAmelCase__					=							[]
				UpperCAmelCase__				,     UpperCAmelCase__					=							key
				for block in encrypted_blocks:
								decrypted_blocks.append(pow(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)	)
				return get_text_from_blocks(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)



def   a_						( lowerCamelCase	):
				with open(lowerCamelCase	) as fo:
								UpperCAmelCase__					=							fo.read()
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							content.split(','	)
				return (int(lowerCamelCase	), int(lowerCamelCase	), int(lowerCamelCase	))



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = DEFAULT_BLOCK_SIZE ,      ):
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							read_key_file(lowerCamelCase	)
				if key_size < block_size * 8:
								sys.exit(
								    'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
								    'requires the block size to be equal to or greater than the key size. '
								    'Either decrease the block size or use different keys.'.format(
								        block_size * 8 ,      lowerCamelCase	)	)

				UpperCAmelCase__					=							[str(lowerCamelCase	) for i in encrypt_message(lowerCamelCase ,      (n, e) ,      lowerCamelCase	)]

				UpperCAmelCase__					=							','.join(lowerCamelCase	)
				UpperCAmelCase__					=							f'''{len(lowerCamelCase	)}_{block_size}_{encrypted_content}'''
				with open(lowerCamelCase ,      'w'	) as fo:
								fo.write(lowerCamelCase	)
				return encrypted_content



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							read_key_file(lowerCamelCase	)
				with open(lowerCamelCase	) as fo:
								UpperCAmelCase__					=							fo.read()
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							content.split('_'	)
				UpperCAmelCase__					=							int(lowerCamelCase	)
				UpperCAmelCase__					=							int(lowerCamelCase	)

				if key_size < block_size * 8:
								sys.exit(
								    'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
								    'requires the block size to be equal to or greater than the key size. '
								    'Did you specify the correct key file and encrypted file?'.format(
								        block_size * 8 ,      lowerCamelCase	)	)

				UpperCAmelCase__					=							[]
				for block in encrypted_message.split(','	):
								encrypted_blocks.append(int(lowerCamelCase	)	)

				return decrypt_message(lowerCamelCase ,      lowerCamelCase ,      (n, d) ,      lowerCamelCase	)



def   a_						( ):
				UpperCAmelCase__					=							'encrypted_file.txt'
				UpperCAmelCase__					=							input(r'Encrypt\Decrypt [e\d]: '	)

				if response.lower().startswith('e'	):
								UpperCAmelCase__					=							'encrypt'
				elif response.lower().startswith('d'	):
								UpperCAmelCase__					=							'decrypt'

				if mode == "encrypt":
								if not os.path.exists('rsa_pubkey.txt'	):
												rkg.make_key_files('rsa' ,      1_0_2_4	)

								UpperCAmelCase__					=							input('\nEnter message: '	)
								UpperCAmelCase__					=							'rsa_pubkey.txt'
								print(f'''Encrypting and writing to {filename}...'''	)
								UpperCAmelCase__					=							encrypt_and_write_to_file(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)

								print('\nEncrypted text:'	)
								print(lowerCamelCase	)

				elif mode == "decrypt":
								UpperCAmelCase__					=							'rsa_privkey.txt'
								print(f'''Reading from {filename} and decrypting...'''	)
								UpperCAmelCase__					=							read_from_file_and_decrypt(lowerCamelCase ,      lowerCamelCase	)
								print('writing decryption to rsa_decryption.txt...'	)
								with open('rsa_decryption.txt' ,      'w'	) as dec:
												dec.write(lowerCamelCase	)

								print('\nDecryption:'	)
								print(lowerCamelCase	)


if __name__ == "__main__":
						main()


"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__  :  Any											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"gpt2"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(    self       :     Any   ,lowerCamelCase__       :     List[Any]=50_257   ,lowerCamelCase__       :     Union[str, Any]=1_024   ,lowerCamelCase__       :     Dict=768   ,lowerCamelCase__       :     List[str]=12   ,lowerCamelCase__       :     Dict=12   ,lowerCamelCase__       :     List[str]=None   ,lowerCamelCase__       :     List[str]="gelu_new"   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Dict=0.1   ,lowerCamelCase__       :     str=0.1   ,lowerCamelCase__       :     Union[str, Any]=1e-5   ,lowerCamelCase__       :     Optional[Any]=0.0_2   ,lowerCamelCase__       :     str="cls_index"   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[Any]=None   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]=50_256   ,lowerCamelCase__       :     Union[str, Any]=50_256   ,lowerCamelCase__       :     Union[str, Any]=False   ,lowerCamelCase__       :     List[Any]=False   ,**lowerCamelCase__       :     Tuple   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							n_positions
									UpperCAmelCase__					=							n_embd
									UpperCAmelCase__					=							n_layer
									UpperCAmelCase__					=							n_head
									UpperCAmelCase__					=							n_inner
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							resid_pdrop
									UpperCAmelCase__					=							embd_pdrop
									UpperCAmelCase__					=							attn_pdrop
									UpperCAmelCase__					=							layer_norm_epsilon
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							summary_type
									UpperCAmelCase__					=							summary_use_proj
									UpperCAmelCase__					=							summary_activation
									UpperCAmelCase__					=							summary_first_dropout
									UpperCAmelCase__					=							summary_proj_to_labels
									UpperCAmelCase__					=							scale_attn_weights
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							scale_attn_by_inverse_layer_idx
									UpperCAmelCase__					=							reorder_and_upcast_attn

									UpperCAmelCase__					=							bos_token_id
									UpperCAmelCase__					=							eos_token_id

									super().__init__(bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,**lowerCamelCase__      )






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					def __init__(    self       :     List[str]   ,lowerCamelCase__       :     PretrainedConfig   ,lowerCamelCase__       :     str = "default"   ,lowerCamelCase__       :     List[PatchingSpec] = None   ,lowerCamelCase__       :     bool = False   ,):
									super().__init__(lowerCamelCase__   ,task=lowerCamelCase__   ,patching_specs=lowerCamelCase__   ,use_past=lowerCamelCase__      )
									if not getattr(self._config   ,'pad_token_id'   ,lowerCamelCase__      ):
													# TODO: how to do that better?
													UpperCAmelCase__					=							0







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
									if self.use_past:
													self.fill_with_past_key_values_(lowerCamelCase__   ,direction='inputs'      )
													UpperCAmelCase__					=							{0: 'batch', 1: 'past_sequence + sequence'}
									else:
													UpperCAmelCase__					=							{0: 'batch', 1: 'sequence'}

									return common_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return self._config.n_layer







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return self._config.n_head







					def 		__lowerCAmelCase						(    self       :     int   ,lowerCamelCase__       :     PreTrainedTokenizer   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     int = -1   ,lowerCamelCase__       :     bool = False   ,lowerCamelCase__       :     Optional[TensorType] = None   ,):
									UpperCAmelCase__					=							super(lowerCamelCase__   ,self      ).generate_dummy_inputs(
									    lowerCamelCase__   ,batch_size=lowerCamelCase__   ,seq_length=lowerCamelCase__   ,is_pair=lowerCamelCase__   ,framework=lowerCamelCase__      )

									# We need to order the input in the way they appears in the forward()
									UpperCAmelCase__					=							OrderedDict({'input_ids': common_inputs['input_ids']}      )

									# Need to add the past_keys
									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
													else:
																	import torch

																	UpperCAmelCase__				,     UpperCAmelCase__					=							common_inputs['input_ids'].shape
																	# Not using the same length for past_key_values
																	UpperCAmelCase__					=							seqlen + 2
																	UpperCAmelCase__					=							(
																	    batch,
																	    self.num_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // self.num_attention_heads,
																	)
																	UpperCAmelCase__					=							[
																	    (torch.zeros(lowerCamelCase__      ), torch.zeros(lowerCamelCase__      )) for _ in range(self.num_layers      )
																	]

									UpperCAmelCase__					=							common_inputs['attention_mask']
									if self.use_past:
													UpperCAmelCase__					=							ordered_inputs['attention_mask'].dtype
													UpperCAmelCase__					=							torch.cat(
													    [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase__   ,lowerCamelCase__   ,dtype=lowerCamelCase__      )]   ,dim=1      )

									return ordered_inputs







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 13



