



"""simple docstring"""

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()
A_	:						str				=			logging.get_logger(__name__)

A_	:						Optional[Any]				=			'Hello world! cécé herlolip'
def    __snake_case       (  __A			:	str       ,		__A			:	str       ,		__A			:	bool    )    ->					Dict:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FairseqRobertaModel.from_pretrained(UpperCamelCase__    )
				roberta.eval()  # disable dropout
				SCREAMING_SNAKE_CASE	:       Dict 							=    roberta.model.encoder.sentence_encoder
				SCREAMING_SNAKE_CASE	:       int 							=    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	:       List[Any] 							=    roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
				print('Our BERT config:'       ,		UpperCamelCase__    )

				SCREAMING_SNAKE_CASE	:       Dict 							=    RobertaForSequenceClassification(UpperCamelCase__    ) if classification_head else RobertaForMaskedLM(UpperCamelCase__    )
				model.eval()

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

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

								# self attention
								SCREAMING_SNAKE_CASE	:       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)    )
								)

								SCREAMING_SNAKE_CASE	:       List[str] 							=    roberta_layer.self_attn.q_proj.weight
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    roberta_layer.self_attn.q_proj.bias
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    roberta_layer.self_attn.k_proj.weight
								SCREAMING_SNAKE_CASE	:       Tuple 							=    roberta_layer.self_attn.k_proj.bias
								SCREAMING_SNAKE_CASE	:       List[str] 							=    roberta_layer.self_attn.v_proj.weight
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    roberta_layer.self_attn.v_proj.bias

								# self-attention output
								SCREAMING_SNAKE_CASE	:       BertSelfOutput 							=    layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								SCREAMING_SNAKE_CASE	:       Tuple 							=    roberta_layer.self_attn.out_proj.weight
								SCREAMING_SNAKE_CASE	:       List[str] 							=    roberta_layer.self_attn.out_proj.bias
								SCREAMING_SNAKE_CASE	:       Dict 							=    roberta_layer.self_attn_layer_norm.weight
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    roberta_layer.self_attn_layer_norm.bias

								# intermediate
								SCREAMING_SNAKE_CASE	:       BertIntermediate 							=    layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								SCREAMING_SNAKE_CASE	:       Tuple 							=    roberta_layer.fca.weight
								SCREAMING_SNAKE_CASE	:       List[str] 							=    roberta_layer.fca.bias

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

				if classification_head:
								SCREAMING_SNAKE_CASE	:       int 							=    roberta.model.classification_heads["mnli"].dense.weight
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    roberta.model.classification_heads["mnli"].dense.bias
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    roberta.model.classification_heads["mnli"].out_proj.weight
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    roberta.model.classification_heads["mnli"].out_proj.bias
				else:
								# LM Head
								SCREAMING_SNAKE_CASE	:       Any 							=    roberta.model.encoder.lm_head.dense.weight
								SCREAMING_SNAKE_CASE	:       Dict 							=    roberta.model.encoder.lm_head.dense.bias
								SCREAMING_SNAKE_CASE	:       List[str] 							=    roberta.model.encoder.lm_head.layer_norm.weight
								SCREAMING_SNAKE_CASE	:       List[str] 							=    roberta.model.encoder.lm_head.layer_norm.bias
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    roberta.model.encoder.lm_head.weight
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    roberta.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
					A_	:						Optional[int]				=			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.'
					)
					A_	:						Optional[Any]				=			parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)










"""simple docstring"""

def    __snake_case       (  __A			:	str       ,		__A			:	str    )    ->					int:






				'''simple docstring'''
				# The longer word should come first
				if len(__A    ) < len(__A    ):
								return levenshtein_distance(__A       ,		__A    )

				if len(__A    ) == 0:
								return len(__A    )

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    list(range(len(__A    ) + 1    )    )

				for i, ca in enumerate(__A    ):
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [i + 1]

								for j, ca in enumerate(__A    ):
												# Calculate insertions, deletions and substitutions
												SCREAMING_SNAKE_CASE	:       str 							=    previous_row[j + 1] + 1
												SCREAMING_SNAKE_CASE	:       List[str] 							=    current_row[j] + 1
												SCREAMING_SNAKE_CASE	:       Any 							=    previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(__A       ,		__A       ,		__A    )    )

								# Store the previous row
								SCREAMING_SNAKE_CASE	:       Tuple 							=    current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
							A_	:						Optional[int]				=			input('Enter the first word:\n').strip()
							A_	:						Optional[Any]				=			input('Enter the second word:\n').strip()

							A_	:						str				=			levenshtein_distance(first_word, second_word)
							print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')





