






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

_UpperCamelCase    :   int   			=						'Hello world! cécé herlolip'




def       snake_case  (A_				:str     ,      A_				:str     ,      A_				:bool				):







  '''simple docstring'''

  a					:      int    =       FairseqRobertaModel.from_pretrained(A_				)
  roberta.eval()  # disable dropout
  a					:      Union[str, Any]    =       roberta.model.encoder.sentence_encoder
  a					:      List[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:
    a					:      List[str]    =       roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'     ,      A_				)

  a					:      Optional[Any]    =       XLMRobertaXLForSequenceClassification(A_				) if classification_head else XLMRobertaXLForMaskedLM(A_				)
  model.eval()

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

  a					:      Optional[Any]    =       roberta_sent_encoder.layer_norm.weight
  a					:      int    =       roberta_sent_encoder.layer_norm.bias

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

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

    # self attention
    a					:      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)				)
    )

    a					:      List[Any]    =       roberta_layer.self_attn.q_proj.weight
    a					:      Optional[int]    =       roberta_layer.self_attn.q_proj.bias
    a					:      int    =       roberta_layer.self_attn.k_proj.weight
    a					:      Optional[int]    =       roberta_layer.self_attn.k_proj.bias
    a					:      str    =       roberta_layer.self_attn.v_proj.weight
    a					:      str    =       roberta_layer.self_attn.v_proj.bias

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

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

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

    # output
    a					:      BertOutput    =       layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    a					:      Union[str, Any]    =       roberta_layer.fca.weight
    a					:      Tuple    =       roberta_layer.fca.bias
    # end of layer

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

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

  a					:      Union[str, Any]    =       model(A_				)[0]
  if classification_head:
    a					:      List[str]    =       roberta.model.classification_heads['mnli'](roberta.extract_features(A_				)				)
  else:
    a					:      List[Any]    =       roberta.model(A_				)[0]
  print(our_output.shape     ,      their_output.shape				)
  a					:      Optional[Any]    =       torch.max(torch.abs(our_output - their_output				)				).item()
  print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
  a					:      Union[str, Any]    =       torch.allclose(A_     ,      A_     ,      atol=1E-3				)
  print('Do both models output the same tensors?'     ,      '🔥' if success else '💩'				)
  if not success:
    raise Exception('Something went wRoNg'				)

  pathlib.Path(A_				).mkdir(parents=A_     ,      exist_ok=A_				)
  print(f'''Saving model to {pytorch_dump_folder_path}'''				)
  model.save_pretrained(A_				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[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.'
   )
   _UpperCamelCase    :   List[Any]   			=						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 os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      a					:      Any    =       ['▁This', '▁is', '▁a', '▁t', 'est']
      a					:      List[Any]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['monolingual_vocab_file']      )
      with open(self.monolingual_vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        for token in vocab_tokens:
          fp.write(F'''{token} {vocab_tokens[token]}\n'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




