



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

_UpperCamelCase											=						'Hello world! cécé herlolip'

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       :       str           = FairseqRobertaModel.from_pretrained(_lowerCAmelCase       )
  roberta.eval()  # disable dropout
  __lowerCamelCase       :       Optional[int]           = roberta.model.encoder.sentence_encoder
  __lowerCamelCase       :       Dict           = 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:
    __lowerCamelCase       :       Union[str, Any]           = roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Dict           = XLMRobertaXLForSequenceClassification(_lowerCAmelCase       ) if classification_head else XLMRobertaXLForMaskedLM(_lowerCAmelCase       )
  model.eval()

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

  __lowerCamelCase       :       Any           = roberta_sent_encoder.layer_norm.weight
  __lowerCamelCase       :       Union[str, Any]           = roberta_sent_encoder.layer_norm.bias

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

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

    # self attention
    __lowerCamelCase       :       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)       )
    )

    __lowerCamelCase       :       Tuple           = roberta_layer.self_attn.q_proj.weight
    __lowerCamelCase       :       str           = roberta_layer.self_attn.q_proj.bias
    __lowerCamelCase       :       Any           = roberta_layer.self_attn.k_proj.weight
    __lowerCamelCase       :       Tuple           = roberta_layer.self_attn.k_proj.bias
    __lowerCamelCase       :       Dict           = roberta_layer.self_attn.v_proj.weight
    __lowerCamelCase       :       Optional[Any]           = roberta_layer.self_attn.v_proj.bias

    # self-attention output
    __lowerCamelCase       :       BertSelfOutput           = layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    __lowerCamelCase       :       Dict           = roberta_layer.self_attn.out_proj.weight
    __lowerCamelCase       :       Any           = roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    __lowerCamelCase       :       List[str]           = roberta_layer.final_layer_norm.weight
    __lowerCamelCase       :       Dict           = roberta_layer.final_layer_norm.bias

    # intermediate
    __lowerCamelCase       :       BertIntermediate           = layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    __lowerCamelCase       :       Dict           = roberta_layer.fca.weight
    __lowerCamelCase       :       Tuple           = roberta_layer.fca.bias

    # output
    __lowerCamelCase       :       BertOutput           = layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    __lowerCamelCase       :       Dict           = roberta_layer.fca.weight
    __lowerCamelCase       :       int           = roberta_layer.fca.bias
    # end of layer

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

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

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











'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






