



'''simple docstring'''





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
lowercase_			=  logging.get_logger(__name__)

lowercase_			=  "Hello world! cécé herlolip"




def lowerCAmelCase  (__A  ,     __A  ,     __A):



     """simple docstring"""



     _a  ,   _a     =  os.path.split(__A)
     _a     =  DataaVecTextModel.from_pretrained(
         __A  ,     checkpoint_file=__A)
     dataavec.eval()  # disable dropout
     _a     =  dataavec.models[0]
     _a     =  dataavec_model.encoder.sentence_encoder
     _a     =  DataaVecTextConfig(
         vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,     hidden_size=dataavec_model.args.encoder_embed_dim  ,     num_hidden_layers=dataavec_model.args.encoder_layers  ,     num_attention_heads=dataavec_model.args.encoder_attention_heads  ,     intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,     max_position_embeddings=514  ,     type_vocab_size=1  ,     layer_norm_eps=1e-5  ,     )
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''  ,     __A)

     _a     =  DataaVecTextForSequenceClassification(__A) if classification_head else DataaVecTextForMaskedLM(__A)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     _a     =  dataavec_sent_encoder.embed_tokens.weight
     _a     =  dataavec_sent_encoder.embed_positions.weight
     _a     =  torch.zeros_like(
         model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
     _a     =  dataavec_sent_encoder.layernorm_embedding.weight
     _a     =  dataavec_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers):
          # Encoder: start of layer
          _a     =  model.dataavec_text.encoder.layer[i]
          _a     =  dataavec_sent_encoder.layers[i]

          # self attention
          _a     =  layer.attention.self
          assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )
          assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
              (config.hidden_size, config.hidden_size)), (
              "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
              F''' {torch.Size((config.hidden_size, config.hidden_size))}'''
          )

          _a     =  dataavec_layer.self_attn.q_proj.weight
          _a     =  dataavec_layer.self_attn.q_proj.bias
          _a     =  dataavec_layer.self_attn.k_proj.weight
          _a     =  dataavec_layer.self_attn.k_proj.bias
          _a     =  dataavec_layer.self_attn.v_proj.weight
          _a     =  dataavec_layer.self_attn.v_proj.bias

          # self-attention output
          _a     =  layer.attention.output
          assert (
              self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
          ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
          _a     =  dataavec_layer.self_attn.out_proj.weight
          _a     =  dataavec_layer.self_attn.out_proj.bias
          _a     =  dataavec_layer.self_attn_layer_norm.weight
          _a     =  dataavec_layer.self_attn_layer_norm.bias

          # intermediate
          _a     =  layer.intermediate
          assert (
              intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias

          # output
          _a     =  layer.output
          assert (
              bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
          ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
          _a     =  dataavec_layer.fca.weight
          _a     =  dataavec_layer.fca.bias
          _a     =  dataavec_layer.final_layer_norm.weight
          _a     =  dataavec_layer.final_layer_norm.bias
          # end of layer

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

     # Let's check that we get the same results.
     _a     =  dataavec.encode(__A).unsqueeze(0)  # batch of size 1

     _a     =  model(__A)[0]
     if classification_head:
          _a     =  dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(__A))
     else:
          _a     =  dataavec_model(__A)[0]
     print(our_output.shape  ,     their_output.shape)
     _a     =  torch.max(torch.abs(our_output - their_output)).item()
     print(F'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
     _a     =  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__":
  lowercase_			=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--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."
  )
  lowercase_			=  parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )








'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbartaa import MBartaaTokenizer
else:
  lowercase_			=  None


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase_			=  {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_			=  {
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

# fmt: off
lowercase_			=  ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	List[str]										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	int										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase   :	Optional[int]										=	PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase   :	Any										=	['input_ids', 'attention_mask']
 __lowerCamelCase   :	Optional[Any]										=	MBartaaTokenizer

 __lowerCamelCase   :	List[int]										=	[]
 __lowerCamelCase   :	List[int]										=	[]



 def __init__(self			,       A=None			,       A=None			,       A=None			,       A=None			,       A="</s>"			,       A="</s>"			,       A="<s>"			,       A="<unk>"			,       A="<pad>"			,       A="<mask>"			,       **A			,       )   ->					List[str]:


      """simple docstring"""



      _a     =  AddedToken(A			,       lstrip=A			,       rstrip=A	) if isinstance(A			,       A	) else mask_token

      _a     =  kwargs.get('''additional_special_tokens'''			,       []	)
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          A			,       src_lang=A			,       tgt_lang=A			,       tokenizer_file=A			,       eos_token=A			,       sep_token=A			,       cls_token=A			,       unk_token=A			,       pad_token=A			,       mask_token=A			,       **A			,       )

      _a     =  vocab_file
      _a     =  False if not self.vocab_file else True

      _a     =  {
          lang_code: self.convert_tokens_to_ids(A	) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a     =  src_lang if src_lang is not None else '''en_XX'''
      _a     =  tgt_lang
      _a     =  self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang	)



 @property
 def  a__  (self	)   ->					str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang	)



 def  a__  (self			,       A			,       A = None	)   ->					List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def  a__  (self			,       A			,       A = "en_XX"			,       A = None			,       A = "ro_RO"			,       **A			,       )   ->					BatchEncoding:


      """simple docstring"""



      _a     =  src_lang
      _a     =  tgt_lang
      return super().prepare_seqaseq_batch(A			,       A			,       **A	)



 def  a__  (self	)   ->					Union[str, Any]:


      """simple docstring"""



      return self.set_src_lang_special_tokens(self.src_lang	)



 def  a__  (self	)   ->					Union[str, Any]:


      """simple docstring"""



      return self.set_tgt_lang_special_tokens(self.tgt_lang	)



 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  processors.TemplateProcessing(
          single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str			,       pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str			,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,       self.prefix_tokens + self.suffix_tokens	)	)			,       )



 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  processors.TemplateProcessing(
          single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str			,       pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str			,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,       self.prefix_tokens + self.suffix_tokens	)	)			,       )



 def  a__  (self			,       A			,       A			,       A			,       A			,       **A	)   ->					Optional[int]:


      """simple docstring"""



      if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''	)
      _a     =  src_lang
      _a     =  self(A			,       add_special_tokens=A			,       return_tensors=A			,       **A	)
      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  tgt_lang_id
      return inputs



 def  a__  (self			,       A			,       A = None	)   ->					Tuple[str]:


      """simple docstring"""



      if not self.can_save_slow_tokenizer:
           raise ValueError(
               '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
               '''tokenizer.'''	)

      if not os.path.isdir(A	):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
           return
      _a     =  os.path.join(
          A			,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

      if os.path.abspath(self.vocab_file	) != os.path.abspath(A	):
           copyfile(self.vocab_file			,       A	)

      return (out_vocab_file,)



