
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()
__lowerCAmelCase				      =					logging.get_logger(__name__)

__lowerCAmelCase				      =					'Hello world! cécé herlolip'


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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   FairseqRobertaModel.from_pretrained(a  )
			roberta.eval()  # disable dropout
			SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.sentence_encoder
			SCREAMING_SNAKE_CASE    	=   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:
						SCREAMING_SNAKE_CASE    	=   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
			print('''Our BERT config:'''		,	a  )

			SCREAMING_SNAKE_CASE    	=   RobertaForSequenceClassification(a  ) if classification_head else RobertaForMaskedLM(a  )
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			SCREAMING_SNAKE_CASE    	=   roberta_sent_encoder.embed_tokens.weight
			SCREAMING_SNAKE_CASE    	=   roberta_sent_encoder.embed_positions.weight
			SCREAMING_SNAKE_CASE    	=   torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
			SCREAMING_SNAKE_CASE    	=   roberta_sent_encoder.emb_layer_norm.weight
			SCREAMING_SNAKE_CASE    	=   roberta_sent_encoder.emb_layer_norm.bias

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

						# self attention
						SCREAMING_SNAKE_CASE    	=   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)  )
						)

						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.q_proj.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.q_proj.bias
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.k_proj.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.k_proj.bias
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.v_proj.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.v_proj.bias

						# self-attention output
						SCREAMING_SNAKE_CASE    	=   layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.out_proj.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn.out_proj.bias
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn_layer_norm.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.self_attn_layer_norm.bias

						# intermediate
						SCREAMING_SNAKE_CASE    	=   layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						SCREAMING_SNAKE_CASE    	=   roberta_layer.fca.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.fca.bias

						# output
						SCREAMING_SNAKE_CASE    	=   layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						SCREAMING_SNAKE_CASE    	=   roberta_layer.fca.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.fca.bias
						SCREAMING_SNAKE_CASE    	=   roberta_layer.final_layer_norm.weight
						SCREAMING_SNAKE_CASE    	=   roberta_layer.final_layer_norm.bias
						# end of layer

			if classification_head:
						SCREAMING_SNAKE_CASE    	=   roberta.model.classification_heads['''mnli'''].dense.weight
						SCREAMING_SNAKE_CASE    	=   roberta.model.classification_heads['''mnli'''].dense.bias
						SCREAMING_SNAKE_CASE    	=   roberta.model.classification_heads['''mnli'''].out_proj.weight
						SCREAMING_SNAKE_CASE    	=   roberta.model.classification_heads['''mnli'''].out_proj.bias
			else:
						# LM Head
						SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.lm_head.dense.weight
						SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.lm_head.dense.bias
						SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.lm_head.layer_norm.weight
						SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.lm_head.layer_norm.bias
						SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.lm_head.weight
						SCREAMING_SNAKE_CASE    	=   roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			SCREAMING_SNAKE_CASE    	=   roberta.encode(a  ).unsqueeze(0  )  # batch of size 1

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

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


if __name__ == "__main__":
							__lowerCAmelCase				      =					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.'
							)
							__lowerCAmelCase				      =					parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)

from __future__ import annotations


def       a			(   a		,	a  )    ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', '''''', ''''''
			SCREAMING_SNAKE_CASE    	=   []

			for character in message_part:
						tmp.append(character_to_number[character]  )

			for each in tmp:
						one += each[0]
						two += each[1]
						three += each[2]

			return one + two + three


def       a			(   a		,	a  )    ->tuple[str, str, str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
			SCREAMING_SNAKE_CASE    	=   []

			for character in message_part:
						this_part += character_to_number[character]

			for digit in this_part:
						tmp += digit
						if len(a  ) == len(a  ):
									result.append(a  )
									SCREAMING_SNAKE_CASE    	=   ''''''

			return result[0], result[1], result[2]


def       a			(   a		,	a  )    ->tuple[str, str, dict[str, str], dict[str, str]]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   alphabet.replace(''' '''		,	''''''  ).upper()
			SCREAMING_SNAKE_CASE    	=   message.replace(''' '''		,	''''''  ).upper()

			# Check length and characters
			if len(a  ) != 27:
						raise KeyError('''Length of alphabet has to be 27.'''  )
			for each in message:
						if each not in alphabet:
									raise ValueError('''Each message character has to be included in alphabet!'''  )

    # Generate dictionares
			SCREAMING_SNAKE_CASE    	=   (
			    '''111''',
			    '''112''',
			    '''113''',
			    '''121''',
			    '''122''',
			    '''123''',
			    '''131''',
			    '''132''',
			    '''133''',
			    '''211''',
			    '''212''',
			    '''213''',
			    '''221''',
			    '''222''',
			    '''223''',
			    '''231''',
			    '''232''',
			    '''233''',
			    '''311''',
			    '''312''',
			    '''313''',
			    '''321''',
			    '''322''',
			    '''323''',
			    '''331''',
			    '''332''',
			    '''333''',
			)
			SCREAMING_SNAKE_CASE    	=   {}
			SCREAMING_SNAKE_CASE    	=   {}
			for letter, number in zip(a		,	a  ):
						SCREAMING_SNAKE_CASE    	=   number
						SCREAMING_SNAKE_CASE    	=   letter

			return message, alphabet, character_to_number, number_to_character


def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
			    a		,	a  )
			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''

			for i in range(0		,	len(a  ) + 1		,	a  ):
						encrypted_numeric += __encrypt_part(
						    message[i : i + period]		,	a  )

			for i in range(0		,	len(a  )		,	3  ):
						encrypted += number_to_character[encrypted_numeric[i : i + 3]]

			return encrypted


def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
			    a		,	a  )
			SCREAMING_SNAKE_CASE    	=   []
			SCREAMING_SNAKE_CASE    	=   ''''''

			for i in range(0		,	len(a  ) + 1		,	a  ):
						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __decrypt_part(message[i : i + period]		,	a  )

						for j in range(0		,	len(a  )  ):
									decrypted_numeric.append(a[j] + b[j] + c[j]  )

			for each in decrypted_numeric:
						decrypted += number_to_character[each]

			return decrypted


if __name__ == "__main__":
							__lowerCAmelCase				      =					'DEFEND THE EAST WALL OF THE CASTLE.'
							__lowerCAmelCase				      =					encrypt_message(msg, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
							__lowerCAmelCase				      =					decrypt_message(encrypted, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
							print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')