





'''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()
snake_case_  :							List[str]            =	logging.get_logger(__name__)

snake_case_  :							Optional[Any]            =	"Hello world! cécé herlolip"





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      bool )			->						List[str]:
						UpperCAmelCase_      :   Dict      =	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
						roberta.eval()  # disable dropout
						UpperCAmelCase_      :   Optional[Any]      =	roberta.model.encoder.sentence_encoder
						UpperCAmelCase_      :   Optional[int]      =	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=514,      type_vocab_size=1,      layer_norm_eps=1E-5,      )
						if classification_head:
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our BERT config:''',      SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Optional[Any]      =	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__ )
						model.eval()

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

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

												# self attention
												UpperCAmelCase_      :   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) )
												)

												UpperCAmelCase_      :   List[str]      =	roberta_layer.self_attn.q_proj.weight
												UpperCAmelCase_      :   Union[str, Any]      =	roberta_layer.self_attn.q_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.k_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.k_proj.bias
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.v_proj.weight
												UpperCAmelCase_      :   Optional[Any]      =	roberta_layer.self_attn.v_proj.bias

												# self-attention output
												UpperCAmelCase_      :   BertSelfOutput      =	layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.out_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.out_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn_layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.self_attn_layer_norm.bias

												# intermediate
												UpperCAmelCase_      :   BertIntermediate      =	layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.fca.weight
												UpperCAmelCase_      :   List[str]      =	roberta_layer.fca.bias

												# output
												UpperCAmelCase_      :   BertOutput      =	layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												UpperCAmelCase_      :   str      =	roberta_layer.fca.weight
												UpperCAmelCase_      :   str      =	roberta_layer.fca.bias
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.final_layer_norm.weight
												UpperCAmelCase_      :   str      =	roberta_layer.final_layer_norm.bias
												# end of layer

						if classification_head:
												UpperCAmelCase_      :   Tuple      =	roberta.model.classification_heads['''mnli'''].dense.weight
												UpperCAmelCase_      :   Tuple      =	roberta.model.classification_heads['''mnli'''].dense.bias
												UpperCAmelCase_      :   str      =	roberta.model.classification_heads['''mnli'''].out_proj.weight
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.bias
						else:
												# LM Head
												UpperCAmelCase_      :   Optional[int]      =	roberta.model.encoder.lm_head.dense.weight
												UpperCAmelCase_      :   Any      =	roberta.model.encoder.lm_head.dense.bias
												UpperCAmelCase_      :   str      =	roberta.model.encoder.lm_head.layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta.model.encoder.lm_head.layer_norm.bias
												UpperCAmelCase_      :   List[Any]      =	roberta.model.encoder.lm_head.weight
												UpperCAmelCase_      :   Tuple      =	roberta.model.encoder.lm_head.bias

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

						UpperCAmelCase_      :   Optional[int]      =	model(SCREAMING_SNAKE_CASE__ )[0]
						if classification_head:
												UpperCAmelCase_      :   List[Any]      =	roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE__ ) )
						else:
												UpperCAmelCase_      :   Tuple      =	roberta.model(SCREAMING_SNAKE_CASE__ )[0]
						print(our_output.shape,      their_output.shape )
						UpperCAmelCase_      :   Tuple      =	torch.max(torch.abs(our_output - their_output ) ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}""" )  # ~ 1e-7
						UpperCAmelCase_      :   List[Any]      =	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__":
					snake_case_  :							Any            =	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."
					)
					snake_case_  :							Dict            =	parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





