



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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
			raise Exception("requires fairseq >= 1.0.0a")

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

SCREAMING_SNAKE_CASE     				=						"Hello world! cécé herlolip"







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
		UpperCAmelCase_						=				FairseqRobertaModel.from_pretrained(__SCREAMING_SNAKE_CASE )
		roberta.eval()  # disable dropout
		UpperCAmelCase_						=				roberta.model.encoder.sentence_encoder
		UpperCAmelCase_						=				XLMRobertaConfig(
		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings			,     hidden_size=roberta.cfg.model.encoder_embed_dim			,     num_hidden_layers=roberta.cfg.model.encoder_layers			,     num_attention_heads=roberta.cfg.model.encoder_attention_heads			,     intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim			,     max_position_embeddings=514			,     type_vocab_size=1			,     layer_norm_eps=1E-5			,     )
		if classification_head:
				UpperCAmelCase_						=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

		print("Our RoBERTa config:"			,     __SCREAMING_SNAKE_CASE )

		UpperCAmelCase_						=				XLMRobertaXLForSequenceClassification(__SCREAMING_SNAKE_CASE ) if classification_head else XLMRobertaXLForMaskedLM(__SCREAMING_SNAKE_CASE )
		model.eval()

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

		UpperCAmelCase_						=				roberta_sent_encoder.layer_norm.weight
		UpperCAmelCase_						=				roberta_sent_encoder.layer_norm.bias

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

				UpperCAmelCase_						=				layer.attention
				UpperCAmelCase_						=				roberta_layer.self_attn_layer_norm.weight
				UpperCAmelCase_						=				roberta_layer.self_attn_layer_norm.bias

				# self attention
				UpperCAmelCase_						=				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) )
				)

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

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

				# this one is final layer norm
				UpperCAmelCase_						=				roberta_layer.final_layer_norm.weight
				UpperCAmelCase_						=				roberta_layer.final_layer_norm.bias

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

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

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

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

		UpperCAmelCase_						=				model(__SCREAMING_SNAKE_CASE )[0]
		if classification_head:
				UpperCAmelCase_						=				roberta.model.classification_heads["mnli"](roberta.extract_features(__SCREAMING_SNAKE_CASE ) )
		else:
				UpperCAmelCase_						=				roberta.model(__SCREAMING_SNAKE_CASE )[0]
		print(our_output.shape			,     their_output.shape )
		UpperCAmelCase_						=				torch.max(torch.abs(our_output - their_output ) ).item()
		print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
		UpperCAmelCase_						=				torch.allclose(__SCREAMING_SNAKE_CASE			,     __SCREAMING_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(__SCREAMING_SNAKE_CASE ).mkdir(parents=__SCREAMING_SNAKE_CASE			,     exist_ok=__SCREAMING_SNAKE_CASE )
		print(f'''Saving model to {pytorch_dump_folder_path}''' )
		model.save_pretrained(__SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)






import heapq as hq
import math
from collections.abc import Iterator



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				str(id_     )
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				{}  # {vertex:distance}



					def __lt__(			self    ,	lowerCAmelCase     ):
							return self.key < other.key



					def __repr__(			self     ):
							return self.id



					def    A__		(			self    ,	lowerCAmelCase     ):
							self.neighbors.append(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				weight







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		# add the neighbors:
		graph[a - 1].add_neighbor(graph[b - 1] )
		graph[b - 1].add_neighbor(graph[a - 1] )
		# add the edges:
		graph[a - 1].add_edge(graph[b - 1]			,     __SCREAMING_SNAKE_CASE )
		graph[b - 1].add_edge(graph[a - 1]			,     __SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							list:
		UpperCAmelCase_						=				[]
		for u in graph:
				UpperCAmelCase_						=				math.inf
				UpperCAmelCase_						=				None
		UpperCAmelCase_						=				0
		UpperCAmelCase_						=				graph[:]
		while q:
				UpperCAmelCase_						=				min(__SCREAMING_SNAKE_CASE )
				q.remove(__SCREAMING_SNAKE_CASE )
				for v in u.neighbors:
						if (v in q) and (u.edges[v.id] < v.key):
								UpperCAmelCase_						=				u
								UpperCAmelCase_						=				u.edges[v.id]
		for i in range(1			,     len(__SCREAMING_SNAKE_CASE ) ):
				a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
		return a







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Iterator[tuple]:
		for u in graph:
				UpperCAmelCase_						=				math.inf
				UpperCAmelCase_						=				None
		UpperCAmelCase_						=				0

		UpperCAmelCase_						=				list(__SCREAMING_SNAKE_CASE )
		hq.heapify(__SCREAMING_SNAKE_CASE )

		while h:
				UpperCAmelCase_						=				hq.heappop(__SCREAMING_SNAKE_CASE )
				for v in u.neighbors:
						if (v in h) and (u.edges[v.id] < v.key):
								UpperCAmelCase_						=				u
								UpperCAmelCase_						=				u.edges[v.id]
								hq.heapify(__SCREAMING_SNAKE_CASE )

		for i in range(1			,     len(__SCREAMING_SNAKE_CASE ) ):
				yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)







def snake_case__  (			)		->							None:
		pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

