



"""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 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()
snake_case   					=				logging.get_logger(__name__)

snake_case   					=				'''Hello world! cécé herlolip'''




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				str:
  _snake_case   	=      FairseqRobertaModel.from_pretrained(lowerCAmelCase_	)
  roberta.eval()  # disable dropout
  _snake_case   	=      roberta.model.encoder.sentence_encoder
  _snake_case   	=      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:
    _snake_case   	=      roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

  print('''Our RoBERTa config:'''  ,						lowerCAmelCase_	)

  _snake_case   	=      XLMRobertaXLForSequenceClassification(lowerCAmelCase_	) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase_	)
  model.eval()

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

  _snake_case   	=      roberta_sent_encoder.layer_norm.weight
  _snake_case   	=      roberta_sent_encoder.layer_norm.bias

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

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

    # self attention
    _snake_case   	=      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)	)
    )

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

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

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

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

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

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

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

  _snake_case   	=      model(lowerCAmelCase_	)[0]
  if classification_head:
    _snake_case   	=      roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCAmelCase_	)	)
  else:
    _snake_case   	=      roberta.model(lowerCAmelCase_	)[0]
  print(our_output.shape  ,						their_output.shape	)
  _snake_case   	=      torch.max(torch.abs(our_output - their_output	)	).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
  _snake_case   	=      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__":
     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.'''
     )
     snake_case   					=				parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )










"""simple docstring"""






from ...configuration_utils import PretrainedConfig



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''bert-generation'''



   def __init__(       self    :	Tuple							,       __lowerCamelCase    :	Optional[int]=5_0_3_5_8							,       __lowerCamelCase    :	List[str]=1_0_2_4							,       __lowerCamelCase    :	Optional[Any]=2_4							,       __lowerCamelCase    :	Any=1_6							,       __lowerCamelCase    :	Union[str, Any]=4_0_9_6							,       __lowerCamelCase    :	Optional[Any]="gelu"							,       __lowerCamelCase    :	str=0.1							,       __lowerCamelCase    :	List[str]=0.1							,       __lowerCamelCase    :	int=5_1_2							,       __lowerCamelCase    :	Dict=0.0_2							,       __lowerCamelCase    :	Tuple=1E-12							,       __lowerCamelCase    :	Any=0							,       __lowerCamelCase    :	Union[str, Any]=2							,       __lowerCamelCase    :	List[Any]=1							,       __lowerCamelCase    :	Optional[Any]="absolute"							,       __lowerCamelCase    :	str=True							,       **__lowerCamelCase    :	List[Any]							,       ):



     """simple docstring"""
     super().__init__(pad_token_id=__lowerCamelCase							,       bos_token_id=__lowerCamelCase							,       eos_token_id=__lowerCamelCase							,       **__lowerCamelCase				)

     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      hidden_act
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      initializer_range
     _snake_case   	=      layer_norm_eps
     _snake_case   	=      position_embedding_type
     _snake_case   	=      use_cache





