



'''simple docstring'''
import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
snake_case_				:			int						 =						logging.get_logger(__name__)

snake_case_				:			Union[str, Any]						 =						'Hello world! cécé herlolip'






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						_UpperCamelCase       ,    _UpperCamelCase      :	Any				       =     os.path.split(UpperCAmelCase_							)
						_UpperCamelCase      :	Optional[int]				       =     DataaVecTextModel.from_pretrained(
						    UpperCAmelCase_						,						checkpoint_file=UpperCAmelCase_							)
						dataavec.eval()  # disable dropout
						_UpperCamelCase      :	int				       =     dataavec.models[0]
						_UpperCamelCase      :	Dict				       =     dataavec_model.encoder.sentence_encoder
						_UpperCamelCase      :	Dict				       =     DataaVecTextConfig(
						    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings						,						hidden_size=dataavec_model.args.encoder_embed_dim						,						num_hidden_layers=dataavec_model.args.encoder_layers						,						num_attention_heads=dataavec_model.args.encoder_attention_heads						,						intermediate_size=dataavec_model.args.encoder_ffn_embed_dim						,						max_position_embeddings=5_1_4						,						type_vocab_size=1						,						layer_norm_eps=1E-5						,						)
						if classification_head:
												_UpperCamelCase      :	int				       =     dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'						,						UpperCAmelCase_							)

						_UpperCamelCase      :	Optional[int]				       =     DataaVecTextForSequenceClassification(UpperCAmelCase_							) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase_							)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						_UpperCamelCase      :	List[str]				       =     dataavec_sent_encoder.embed_tokens.weight
						_UpperCamelCase      :	Union[str, Any]				       =     dataavec_sent_encoder.embed_positions.weight
						_UpperCamelCase      :	int				       =     torch.zeros_like(
						    model.dataavec_text.embeddings.token_type_embeddings.weight							)  # just zero them out b/c data2vec doesn't use them.
						_UpperCamelCase      :	Optional[int]				       =     dataavec_sent_encoder.layernorm_embedding.weight
						_UpperCamelCase      :	List[Any]				       =     dataavec_sent_encoder.layernorm_embedding.bias

						for i in range(config.num_hidden_layers							):
												# Encoder: start of layer
												_UpperCamelCase      :	BertLayer				       =     model.dataavec_text.encoder.layer[i]
												_UpperCamelCase      :	TransformerSentenceEncoderLayer				       =     dataavec_sent_encoder.layers[i]

												# self attention
												_UpperCamelCase      :	BertSelfAttention				       =     layer.attention.self
												assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
												    (config.hidden_size, config.hidden_size)							), (
												    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
												    f' {torch.Size((config.hidden_size, config.hidden_size)							)}'
												)
												assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
												    (config.hidden_size, config.hidden_size)							), (
												    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
												    f' {torch.Size((config.hidden_size, config.hidden_size)							)}'
												)
												assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
												    (config.hidden_size, config.hidden_size)							), (
												    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
												    f' {torch.Size((config.hidden_size, config.hidden_size)							)}'
												)

												_UpperCamelCase      :	List[Any]				       =     dataavec_layer.self_attn.q_proj.weight
												_UpperCamelCase      :	List[str]				       =     dataavec_layer.self_attn.q_proj.bias
												_UpperCamelCase      :	Dict				       =     dataavec_layer.self_attn.k_proj.weight
												_UpperCamelCase      :	Dict				       =     dataavec_layer.self_attn.k_proj.bias
												_UpperCamelCase      :	Optional[Any]				       =     dataavec_layer.self_attn.v_proj.weight
												_UpperCamelCase      :	Optional[int]				       =     dataavec_layer.self_attn.v_proj.bias

												# self-attention output
												_UpperCamelCase      :	BertSelfOutput				       =     layer.attention.output
												assert (
												    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
												), f'Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'
												_UpperCamelCase      :	Optional[int]				       =     dataavec_layer.self_attn.out_proj.weight
												_UpperCamelCase      :	int				       =     dataavec_layer.self_attn.out_proj.bias
												_UpperCamelCase      :	List[str]				       =     dataavec_layer.self_attn_layer_norm.weight
												_UpperCamelCase      :	Optional[Any]				       =     dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												_UpperCamelCase      :	BertIntermediate				       =     layer.intermediate
												assert (
												    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
												), f'Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'
												_UpperCamelCase      :	Optional[Any]				       =     dataavec_layer.fca.weight
												_UpperCamelCase      :	int				       =     dataavec_layer.fca.bias

												# output
												_UpperCamelCase      :	BertOutput				       =     layer.output
												assert (
												    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
												), f'Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'
												_UpperCamelCase      :	List[str]				       =     dataavec_layer.fca.weight
												_UpperCamelCase      :	Any				       =     dataavec_layer.fca.bias
												_UpperCamelCase      :	List[str]				       =     dataavec_layer.final_layer_norm.weight
												_UpperCamelCase      :	List[str]				       =     dataavec_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												_UpperCamelCase      :	int				       =     dataavec.model.classification_heads['mnli'].dense.weight
												_UpperCamelCase      :	Tuple				       =     dataavec.model.classification_heads['mnli'].dense.bias
												_UpperCamelCase      :	Optional[int]				       =     dataavec.model.classification_heads['mnli'].out_proj.weight
												_UpperCamelCase      :	Optional[Any]				       =     dataavec.model.classification_heads['mnli'].out_proj.bias
						else:
												# LM Head
												_UpperCamelCase      :	Tuple				       =     dataavec_model.encoder.lm_head.dense.weight
												_UpperCamelCase      :	str				       =     dataavec_model.encoder.lm_head.dense.bias
												_UpperCamelCase      :	Tuple				       =     dataavec_model.encoder.lm_head.layer_norm.weight
												_UpperCamelCase      :	Tuple				       =     dataavec_model.encoder.lm_head.layer_norm.bias
												_UpperCamelCase      :	Optional[Any]				       =     dataavec_model.encoder.lm_head.weight
												_UpperCamelCase      :	Dict				       =     dataavec_model.encoder.lm_head.bias

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

						_UpperCamelCase      :	str				       =     model(UpperCAmelCase_							)[0]
						if classification_head:
												_UpperCamelCase      :	Optional[Any]				       =     dataavec.model.classification_heads['mnli'](dataavec.extract_features(UpperCAmelCase_							)							)
						else:
												_UpperCamelCase      :	List[str]				       =     dataavec_model(UpperCAmelCase_							)[0]
						print(our_output.shape						,						their_output.shape							)
						_UpperCamelCase      :	Union[str, Any]				       =     torch.max(torch.abs(our_output - their_output							)							).item()
						print(f'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
						_UpperCamelCase      :	Tuple				       =     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__":
					snake_case_				:			Union[str, Any]						 =						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--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.'
					)
					snake_case_				:			Dict						 =						parser.parse_args()
					convert_dataavec_checkpoint_to_pytorch(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)







'''simple docstring'''
from __future__ import annotations

from queue import Queue






class 							lowercase__			:





	def __init__(				self      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	dict[int, list[int]]				       =     {}





	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							for i in self.vertices:
													print(lowerCamelCase__				,' : '				,' -> '.join([str(lowerCamelCase__				) for j in self.vertices[i]]				)				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							if from_vertex in self.vertices:
													self.vertices[from_vertex].append(lowerCamelCase__				)
							else:
													_UpperCamelCase      :	Any				       =     [to_vertex]





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     set()

							# create a first in first out queue to store all the vertices for BFS
							_UpperCamelCase      :	Queue				       =     Queue()

							# mark the source node as visited and enqueue it
							visited.add(lowerCamelCase__				)
							queue.put(lowerCamelCase__				)

							while not queue.empty():
													_UpperCamelCase      :	Optional[Any]				       =     queue.get()

													# loop through all adjacent vertex and enqueue it if not yet visited
													for adjacent_vertex in self.vertices[vertex]:
																			if adjacent_vertex not in visited:
																									queue.put(lowerCamelCase__				)
																									visited.add(lowerCamelCase__				)
							return visited


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)

					snake_case_				:			int						 =						Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					# 0  :  1 -> 2
					# 1  :  2
					# 2  :  0 -> 3
					# 3  :  3

					assert sorted(g.bfs(2)) == [0, 1, 2, 3]


