import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
					raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
UpperCamelCase      			=     logging.get_logger(__name__)

UpperCamelCase      			=     'Hello world! cécé herlolip'



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Optional[int]:
		__A							:   Dict	  =				FairseqRobertaModel.from_pretrained(_lowercase    )
		roberta.eval()  # disable dropout
		__A							:   Optional[int]	  =				roberta.model.encoder.sentence_encoder
		__A							:   Tuple	  =				RobertaConfig(
		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=roberta.args.encoder_embed_dim ,     num_hidden_layers=roberta.args.encoder_layers ,     num_attention_heads=roberta.args.encoder_attention_heads ,     intermediate_size=roberta.args.encoder_ffn_embed_dim ,     max_position_embeddings=514 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     )
		if classification_head:
				__A							:   Any	  =				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
		print("Our BERT config:" ,     _lowercase    )

		__A							:   Tuple	  =				RobertaForSequenceClassification(_lowercase    ) if classification_head else RobertaForMaskedLM(_lowercase    )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		__A							:   Optional[Any]	  =				roberta_sent_encoder.embed_tokens.weight
		__A							:   Optional[Any]	  =				roberta_sent_encoder.embed_positions.weight
		__A							:   Union[str, Any]	  =				torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.
		__A							:   List[str]	  =				roberta_sent_encoder.emb_layer_norm.weight
		__A							:   Union[str, Any]	  =				roberta_sent_encoder.emb_layer_norm.bias

		for i in range(config.num_hidden_layers    ):
				# Encoder: start of layer
				__A							:   BertLayer	  =				model.roberta.encoder.layer[i]
				__A							:   TransformerSentenceEncoderLayer	  =				roberta_sent_encoder.layers[i]

				# self attention
				__A							:   BertSelfAttention	  =				layer.attention.self
				assert (
				    roberta_layer.self_attn.k_proj.weight.data.shape
				    == roberta_layer.self_attn.q_proj.weight.data.shape
				    == roberta_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size)    )
				)

				__A							:   List[str]	  =				roberta_layer.self_attn.q_proj.weight
				__A							:   Any	  =				roberta_layer.self_attn.q_proj.bias
				__A							:   int	  =				roberta_layer.self_attn.k_proj.weight
				__A							:   int	  =				roberta_layer.self_attn.k_proj.bias
				__A							:   List[Any]	  =				roberta_layer.self_attn.v_proj.weight
				__A							:   List[Any]	  =				roberta_layer.self_attn.v_proj.bias

				# self-attention output
				__A							:   BertSelfOutput	  =				layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				__A							:   int	  =				roberta_layer.self_attn.out_proj.weight
				__A							:   List[Any]	  =				roberta_layer.self_attn.out_proj.bias
				__A							:   Dict	  =				roberta_layer.self_attn_layer_norm.weight
				__A							:   Union[str, Any]	  =				roberta_layer.self_attn_layer_norm.bias

				# intermediate
				__A							:   BertIntermediate	  =				layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				__A							:   Any	  =				roberta_layer.fca.weight
				__A							:   Tuple	  =				roberta_layer.fca.bias

				# output
				__A							:   BertOutput	  =				layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				__A							:   Optional[Any]	  =				roberta_layer.fca.weight
				__A							:   str	  =				roberta_layer.fca.bias
				__A							:   Optional[int]	  =				roberta_layer.final_layer_norm.weight
				__A							:   int	  =				roberta_layer.final_layer_norm.bias
				# end of layer

		if classification_head:
				__A							:   Any	  =				roberta.model.classification_heads["mnli"].dense.weight
				__A							:   Tuple	  =				roberta.model.classification_heads["mnli"].dense.bias
				__A							:   Optional[int]	  =				roberta.model.classification_heads["mnli"].out_proj.weight
				__A							:   Any	  =				roberta.model.classification_heads["mnli"].out_proj.bias
		else:
				# LM Head
				__A							:   int	  =				roberta.model.encoder.lm_head.dense.weight
				__A							:   Tuple	  =				roberta.model.encoder.lm_head.dense.bias
				__A							:   Optional[int]	  =				roberta.model.encoder.lm_head.layer_norm.weight
				__A							:   Any	  =				roberta.model.encoder.lm_head.layer_norm.bias
				__A							:   Any	  =				roberta.model.encoder.lm_head.weight
				__A							:   Union[str, Any]	  =				roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		__A							:   torch.Tensor	  =				roberta.encode(_lowercase    ).unsqueeze(0    )  # batch of size 1

		__A							:   Union[str, Any]	  =				model(_lowercase    )[0]
		if classification_head:
				__A							:   List[Any]	  =				roberta.model.classification_heads["mnli"](roberta.extract_features(_lowercase    )    )
		else:
				__A							:   List[Any]	  =				roberta.model(_lowercase    )[0]
		print(our_output.shape ,     their_output.shape    )
		__A							:   Union[str, Any]	  =				torch.max(torch.abs(our_output - their_output    )    ).item()
		print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
		__A							:   Tuple	  =				torch.allclose(_lowercase ,     _lowercase ,     atol=1E-3    )
		print("Do both models output the same tensors?" ,     "🔥" if success else "💩"    )
		if not success:
				raise Exception("Something went wRoNg"    )

		pathlib.Path(_lowercase    ).mkdir(parents=_lowercase ,     exist_ok=_lowercase    )
		print(F"Saving model to {pytorch_dump_folder_path}"    )
		model.save_pretrained(_lowercase    )


if __name__ == "__main__":
					UpperCamelCase      			=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
					)
					UpperCamelCase      			=     parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)






import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase      			=     128
UpperCamelCase      			=     256



def    lowerCamelCase_   (  _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				message.encode("ascii"    )

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



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	str:
		__A							:   list[str]	  =				[]
		for block_int in block_ints:
				__A							:   list[str]	  =				[]
				for i in range(block_size - 1 ,     -1 ,     -1    ):
						if len(_lowercase    ) + i < message_length:
								__A							:   Optional[Any]	  =				block_int // (BYTE_SIZE**i)
								__A							:   Union[str, Any]	  =				block_int % (BYTE_SIZE**i)
								block_message.insert(0 ,     chr(_lowercase    )    )
				message.extend(_lowercase    )
		return "".join(_lowercase    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				[]
		__A  ,       __A							:   List[Any]	  =				key

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



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A							:   Optional[Any]	  =				[]
		__A  ,       __A							:   Dict	  =				key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowercase ,     _lowercase ,     _lowercase    )    )
		return get_text_from_blocks(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	tuple[int, int, int]:
		with open(_lowercase    ) as fo:
				__A							:   Optional[int]	  =				fo.read()
		__A  ,       __A  ,       __A							:   Optional[int]	  =				content.split(","    )
		return (int(_lowercase    ), int(_lowercase    ), int(_lowercase    ))



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A  ,       __A  ,       __A							:   Optional[int]	  =				read_key_file(_lowercase    )
		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 ,     _lowercase    )    )

		__A							:   Optional[int]	  =				[str(_lowercase    ) for i in encrypt_message(_lowercase ,     (n, e) ,     _lowercase    )]

		__A							:   int	  =				",".join(_lowercase    )
		__A							:   List[Any]	  =				F"{len(_lowercase    )}_{block_size}_{encrypted_content}"
		with open(_lowercase ,     "w"    ) as fo:
				fo.write(_lowercase    )
		return encrypted_content



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	str:
		__A  ,       __A  ,       __A							:   List[str]	  =				read_key_file(_lowercase    )
		with open(_lowercase    ) as fo:
				__A							:   Optional[Any]	  =				fo.read()
		__A  ,       __A  ,       __A							:   int	  =				content.split("_"    )
		__A							:   Tuple	  =				int(_lowercase    )
		__A							:   Any	  =				int(_lowercase    )

		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 ,     _lowercase    )    )

		__A							:   Dict	  =				[]
		for block in encrypted_message.split(","    ):
				encrypted_blocks.append(int(_lowercase    )    )

		return decrypt_message(_lowercase ,     _lowercase ,     (n, d) ,     _lowercase    )



def    lowerCamelCase_   (  )							->	None:
		__A							:   int	  =				"encrypted_file.txt"
		__A							:   int	  =				input(r"Encrypt\Decrypt [e\d]: "    )

		if response.lower().startswith("e"    ):
				__A							:   str	  =				"encrypt"
		elif response.lower().startswith("d"    ):
				__A							:   str	  =				"decrypt"

		if mode == "encrypt":
				if not os.path.exists("rsa_pubkey.txt"    ):
						rkg.make_key_files("rsa" ,     1_024    )

				__A							:   Any	  =				input("\nEnter message: "    )
				__A							:   Any	  =				"rsa_pubkey.txt"
				print(F"Encrypting and writing to {filename}..."    )
				__A							:   List[str]	  =				encrypt_and_write_to_file(_lowercase ,     _lowercase ,     _lowercase    )

				print("\nEncrypted text:"    )
				print(_lowercase    )

		elif mode == "decrypt":
				__A							:   Dict	  =				"rsa_privkey.txt"
				print(F"Reading from {filename} and decrypting..."    )
				__A							:   Dict	  =				read_from_file_and_decrypt(_lowercase ,     _lowercase    )
				print("writing decryption to rsa_decryption.txt..."    )
				with open("rsa_decryption.txt" ,     "w"    ) as dec:
						dec.write(_lowercase    )

				print("\nDecryption:"    )
				print(_lowercase    )


if __name__ == "__main__":
					main()





