


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				:  Tuple      					=							logging.get_logger(__name__)

__UpperCamelCase				:  Dict      					=							'Hello world! cécé herlolip'
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   FairseqRobertaModel.from_pretrained(lowerCAmelCase_       )
   roberta.eval()  # disable dropout
   UpperCAmelCase_							:      Optional[int]						=   roberta.model.encoder.sentence_encoder
   UpperCAmelCase_							:      Optional[Any]						=   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=5_1_4			,       type_vocab_size=1			,       layer_norm_eps=1e-5			,       )
   if classification_head:
      UpperCAmelCase_							:      Dict						=   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

   UpperCAmelCase_							:      List[str]						=   XLMRobertaXLForSequenceClassification(lowerCAmelCase_       ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase_       )
   model.eval()

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

   UpperCAmelCase_							:      List[Any]						=   roberta_sent_encoder.layer_norm.weight
   UpperCAmelCase_							:      List[Any]						=   roberta_sent_encoder.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]

      UpperCAmelCase_							:      RobertaAttention						=   layer.attention
      UpperCAmelCase_							:      Optional[Any]						=   roberta_layer.self_attn_layer_norm.weight
      UpperCAmelCase_							:      List[str]						=   roberta_layer.self_attn_layer_norm.bias

      # 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_							:      Optional[Any]						=   roberta_layer.self_attn.q_proj.weight
      UpperCAmelCase_							:      int						=   roberta_layer.self_attn.q_proj.bias
      UpperCAmelCase_							:      Optional[int]						=   roberta_layer.self_attn.k_proj.weight
      UpperCAmelCase_							:      Any						=   roberta_layer.self_attn.k_proj.bias
      UpperCAmelCase_							:      List[Any]						=   roberta_layer.self_attn.v_proj.weight
      UpperCAmelCase_							:      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_							:      Optional[Any]						=   roberta_layer.self_attn.out_proj.weight
      UpperCAmelCase_							:      Union[str, Any]						=   roberta_layer.self_attn.out_proj.bias

      # this one is final layer norm
      UpperCAmelCase_							:      str						=   roberta_layer.final_layer_norm.weight
      UpperCAmelCase_							:      Optional[Any]						=   roberta_layer.final_layer_norm.bias

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

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

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

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

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



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_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.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)