






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()
A        =			logging.get_logger(__name__)

A        =			'Hello world! cécé herlolip'



def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		bool      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   FairseqRobertaModel.from_pretrained(lowerCAmelCase__      )
				roberta.eval()  # disable dropout
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta.model.encoder.sentence_encoder
				SCREAMING_SNAKE_CASE_   :	Any													=   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:
								SCREAMING_SNAKE_CASE_   :	Any													=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our RoBERTa config:'    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   XLMRobertaXLForSequenceClassification(lowerCAmelCase__      ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__      )
				model.eval()

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

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   roberta_sent_encoder.layer_norm.weight
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_sent_encoder.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]

								SCREAMING_SNAKE_CASE_   :	RobertaAttention													=   layer.attention
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_layer.self_attn_layer_norm.weight
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_layer.self_attn_layer_norm.bias

								# 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[int]													=   roberta_layer.self_attn.q_proj.bias
								SCREAMING_SNAKE_CASE_   :	Any													=   roberta_layer.self_attn.k_proj.weight
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta_layer.self_attn.k_proj.bias
								SCREAMING_SNAKE_CASE_   :	Any													=   roberta_layer.self_attn.v_proj.weight
								SCREAMING_SNAKE_CASE_   :	int													=   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_   :	Optional[Any]													=   roberta_layer.self_attn.out_proj.weight
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								SCREAMING_SNAKE_CASE_   :	str													=   roberta_layer.final_layer_norm.weight
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_layer.final_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[Any]													=   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_   :	Dict													=   roberta_layer.fca.weight
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta_layer.fca.bias
								# end of layer

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

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

				SCREAMING_SNAKE_CASE_   :	Any													=   model(lowerCAmelCase__      )[0]
				if classification_head:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCAmelCase__      )      )
				else:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta.model(lowerCAmelCase__      )[0]
				print(our_output.shape    ,    their_output.shape      )
				SCREAMING_SNAKE_CASE_   :	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(lowerCAmelCase__    ,    lowerCAmelCase__    ,    atol=1E-3      )
				print('Do both models output the same tensors?'    ,    '🔥' if success else '💩'      )
				if not success:
								raise Exception('Something went wRoNg'      )

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


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







from __future__ import annotations

from collections import Counter
from random import random


class 							__a							:





		'''simple docstring'''



		def __init__(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   {}


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						if nodea not in self.connections:
										self.add_node(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   probability


		def   __snake_case			(						self       ):
						return list(self.connections       )






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   random()

						for dest in self.connections[node]:
										current_probability += self.connections[node][dest]
										if current_probability > random_value:
														return dest
						return ""






def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		list[tuple[str, str, float]]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[str]													=   Counter(graph.get_nodes()      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   start

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Dict													=   graph.transition(lowerCAmelCase__      )
								visited[node] += 1

				return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()