

'''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()
_lowerCAmelCase    			=						logging.get_logger(__name__)

_lowerCAmelCase    			=						'''Hello world! cécé herlolip'''




def 						UpperCamelCase       ( a		,						a		,						a )      ->	str:






     '''simple docstring'''
     __magic_name__   ,  __magic_name__						   =				os.path.split(_UpperCAmelCase )
     __magic_name__						   =				DataaVecTextModel.from_pretrained(
         _UpperCAmelCase		,						checkpoint_file=_UpperCAmelCase )
     dataavec.eval()  # disable dropout
     __magic_name__						   =				dataavec.models[0]
     __magic_name__						   =				dataavec_model.encoder.sentence_encoder
     __magic_name__						   =				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=514		,						type_vocab_size=1		,						layer_norm_eps=1e-5		,						)
     if classification_head:
          __magic_name__						   =				dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''		,						_UpperCAmelCase )

     __magic_name__						   =				DataaVecTextForSequenceClassification(_UpperCAmelCase ) if classification_head else DataaVecTextForMaskedLM(_UpperCAmelCase )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __magic_name__						   =				dataavec_sent_encoder.embed_tokens.weight
     __magic_name__						   =				dataavec_sent_encoder.embed_positions.weight
     __magic_name__						   =				torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight )  # just zero them out b/c data2vec doesn't use them.
     __magic_name__						   =				dataavec_sent_encoder.layernorm_embedding.weight
     __magic_name__						   =				dataavec_sent_encoder.layernorm_embedding.bias

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

          # self attention
          __magic_name__						   =				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) )}'''
          )

          __magic_name__						   =				dataavec_layer.self_attn.q_proj.weight
          __magic_name__						   =				dataavec_layer.self_attn.q_proj.bias
          __magic_name__						   =				dataavec_layer.self_attn.k_proj.weight
          __magic_name__						   =				dataavec_layer.self_attn.k_proj.bias
          __magic_name__						   =				dataavec_layer.self_attn.v_proj.weight
          __magic_name__						   =				dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          __magic_name__						   =				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}'''
          __magic_name__						   =				dataavec_layer.self_attn.out_proj.weight
          __magic_name__						   =				dataavec_layer.self_attn.out_proj.bias
          __magic_name__						   =				dataavec_layer.self_attn_layer_norm.weight
          __magic_name__						   =				dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          __magic_name__						   =				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}'''
          __magic_name__						   =				dataavec_layer.fca.weight
          __magic_name__						   =				dataavec_layer.fca.bias

          # output
          __magic_name__						   =				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}'''
          __magic_name__						   =				dataavec_layer.fca.weight
          __magic_name__						   =				dataavec_layer.fca.bias
          __magic_name__						   =				dataavec_layer.final_layer_norm.weight
          __magic_name__						   =				dataavec_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          __magic_name__						   =				dataavec.model.classification_heads['''mnli'''].dense.weight
          __magic_name__						   =				dataavec.model.classification_heads['''mnli'''].dense.bias
          __magic_name__						   =				dataavec.model.classification_heads['''mnli'''].out_proj.weight
          __magic_name__						   =				dataavec.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          __magic_name__						   =				dataavec_model.encoder.lm_head.dense.weight
          __magic_name__						   =				dataavec_model.encoder.lm_head.dense.bias
          __magic_name__						   =				dataavec_model.encoder.lm_head.layer_norm.weight
          __magic_name__						   =				dataavec_model.encoder.lm_head.layer_norm.bias
          __magic_name__						   =				dataavec_model.encoder.lm_head.weight
          __magic_name__						   =				dataavec_model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __magic_name__						   =				dataavec.encode(_UpperCAmelCase ).unsqueeze(0 )  # batch of size 1

     __magic_name__						   =				model(_UpperCAmelCase )[0]
     if classification_head:
          __magic_name__						   =				dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(_UpperCAmelCase ) )
     else:
          __magic_name__						   =				dataavec_model(_UpperCAmelCase )[0]
     print(our_output.shape		,						their_output.shape )
     __magic_name__						   =				torch.max(torch.abs(our_output - their_output ) ).item()
     print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
     __magic_name__						   =				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__":
      _lowerCAmelCase    			=						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."
      )
      _lowerCAmelCase    			=						parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )













def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     int:
				print("""\nVertex Distance""" )
				for i in range(_UpperCAmelCase ):
								if dist[i] != float("""inf""" ):
												print(_UpperCAmelCase	,					"""\t"""	,					int(dist[i] )	,					end="""\t""" )
								else:
												print(_UpperCAmelCase	,					"""\t"""	,					"""INF"""	,					end="""\t""" )
								print()





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Dict:
				lowerCamelCase		       =float("""inf""" )
				lowerCamelCase		       =-1
				for i in range(_UpperCAmelCase ):
								if (not vset[i]) and mdist[i] < min_val:
												lowerCamelCase		       =i
												lowerCamelCase		       =mdist[i]
				return min_ind





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )  ->     Optional[int]:
				lowerCamelCase		       =[float("""inf""" ) for _ in range(_UpperCAmelCase )]
				lowerCamelCase		       =[False for _ in range(_UpperCAmelCase )]
				lowerCamelCase		       =0.0

				for _ in range(v - 1 ):
								lowerCamelCase		       =min_dist(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )
								lowerCamelCase		       =True

								for i in range(_UpperCAmelCase ):
												if (
												    (not vset[i])
												    and graph[u][i] != float("""inf""" )
												    and mdist[u] + graph[u][i] < mdist[i]
												):
																lowerCamelCase		       =mdist[u] + graph[u][i]

				print_dist(_UpperCAmelCase	,					_UpperCAmelCase )


if __name__ == "__main__":
							UpperCAmelCase__  :	Tuple     =int(input('''Enter number of vertices: ''').strip())
							UpperCAmelCase__  :	str     =int(input('''Enter number of edges: ''').strip())

							UpperCAmelCase__  :	str     =[[float('''inf''') for i in range(V)] for j in range(V)]

							for i in range(V):
														UpperCAmelCase__  :	Dict     =0.0

							for i in range(E):
														print('''\nEdge ''', i + 1)
														UpperCAmelCase__  :	int     =int(input('''Enter source:''').strip())
														UpperCAmelCase__  :	str     =int(input('''Enter destination:''').strip())
														UpperCAmelCase__  :	List[Any]     =float(input('''Enter weight:''').strip())
														UpperCAmelCase__  :	str     =weight

							UpperCAmelCase__  :	Tuple     =int(input('''\nEnter shortest path source:''').strip())
							dijkstra(graph, V, gsrc)





