
'''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()
UpperCAmelCase__				:	Optional[int]					=						logging.get_logger(__name__)

UpperCAmelCase__				:	Any					=						'''Hello world! cécé herlolip'''







def 					A       ( UpperCamelCase_   :       Optional[int]  ,		UpperCamelCase_   :       List[str]  ,		UpperCamelCase_   :       List[Any] )   ->    Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__									=							FairseqRobertaModel.from_pretrained(snake_case__ )
			roberta.eval()  # disable dropout
			lowerCAmelCase__									=							roberta.model.encoder.sentence_encoder
			lowerCAmelCase__									=							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=5_14  ,		type_vocab_size=1  ,		layer_norm_eps=1E-5  ,		)
			if classification_head:
						lowerCAmelCase__									=							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
			print("Our BERT config:"  ,		snake_case__ )

			lowerCAmelCase__									=							RobertaForSequenceClassification(snake_case__ ) if classification_head else RobertaForMaskedLM(snake_case__ )
			model.eval()

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

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

						# self attention
						lowerCAmelCase__									=							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) )
						)

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

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

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

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

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

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

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

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


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







'''simple docstring'''

def 					A       ( )   ->    str:







			'''simple docstring'''



			lowerCAmelCase__									=							1

			while True:
						if (
						    sorted(str(UpperCamelCase_ ) )
						    == sorted(str(2 * i ) )
						    == sorted(str(3 * i ) )
						    == sorted(str(4 * i ) )
						    == sorted(str(5 * i ) )
						    == sorted(str(6 * i ) )
						):
									return i

						i += 1


if __name__ == "__main__":
		print(solution())





