
'''simple docstring'''







import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_      =   logging.get_logger(__name__)

lowerCAmelCase_      =   {
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_      =   {
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_      =   {'''Helsinki-NLP/opus-mt-en-de''': 5_1_2}
lowerCAmelCase_      =   {}







class 		snake_case_       (						a__						):



   """simple docstring"""





   __lowerCAmelCase	:							List[Any]								=VOCAB_FILES_NAMES
   __lowerCAmelCase	:							int								=PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase	:							str								=PRETRAINED_INIT_CONFIGURATION
   __lowerCAmelCase	:							Optional[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase	:							Dict								=['input_ids', 'attention_mask']
   __lowerCAmelCase	:							Optional[int]								=re.compile('''>>.+<<'''						)  # type: re.Pattern







   def __init__( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase=None			,   UpperCamelCase=None			,   UpperCamelCase=None			,   UpperCamelCase="<unk>"			,   UpperCamelCase="</s>"			,   UpperCamelCase="<pad>"			,   UpperCamelCase=5_12			,   UpperCamelCase = None			,   UpperCamelCase=False			,   **UpperCamelCase			,   ):
          lowerCamelCase__				   =      {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
              source_lang=_lowerCamelCase			,   target_lang=_lowerCamelCase			,   unk_token=_lowerCamelCase			,   eos_token=_lowerCamelCase			,   pad_token=_lowerCamelCase			,   model_max_length=_lowerCamelCase			,   sp_model_kwargs=self.sp_model_kwargs			,   target_vocab_file=_lowerCamelCase			,   separate_vocabs=_lowerCamelCase			,   **_lowerCamelCase			,   )
          assert Path(_lowerCamelCase).exists(), f"""cannot find spm source {source_spm}"""

          lowerCamelCase__				   =      separate_vocabs
          lowerCamelCase__				   =      load_json(_lowerCamelCase)
          if self.unk_token not in self.encoder:
                 raise KeyError("<unk> token must be in vocab")
          assert self.pad_token in self.encoder

          if separate_vocabs:
                 lowerCamelCase__				   =      load_json(_lowerCamelCase)
                 lowerCamelCase__				   =      {v: k for k, v in self.target_encoder.items()}
                 lowerCamelCase__				   =      []
          else:
                 lowerCamelCase__				   =      {v: k for k, v in self.encoder.items()}
                 lowerCamelCase__				   =      [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")]

          lowerCamelCase__				   =      source_lang
          lowerCamelCase__				   =      target_lang
          lowerCamelCase__				   =      [source_spm, target_spm]

          # load SentencePiece model for pre-processing
          lowerCamelCase__				   =      load_spm(_lowerCamelCase			,   self.sp_model_kwargs)
          lowerCamelCase__				   =      load_spm(_lowerCamelCase			,   self.sp_model_kwargs)
          lowerCamelCase__				   =      self.spm_source
          lowerCamelCase__				   =      self.encoder

          # Multilingual target side: default to using first supported language code.

          self._setup_normalizer()







   def     __UpperCAmelCase      ( self):
          try:
                 from sacremoses import MosesPunctNormalizer

                 lowerCamelCase__				   =      MosesPunctNormalizer(self.source_lang).normalize
          except (ImportError, FileNotFoundError):
                 warnings.warn("Recommended: pip install sacremoses.")
                 lowerCamelCase__				   =      lambda UpperCamelCase: x







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          return self.punc_normalizer(_lowerCamelCase) if x else ""







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          return self.current_encoder.get(_lowerCamelCase			,   self.current_encoder[self.unk_token])







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      self.language_code_re.match(_lowerCamelCase)
          lowerCamelCase__				   =      [match.group(0)] if match else []
          return code, self.language_code_re.sub(""			,   _lowerCamelCase)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      self.remove_language_code(_lowerCamelCase)
          lowerCamelCase__				   =      self.current_spm.encode(_lowerCamelCase			,   out_type=_lowerCamelCase)
          return code + pieces







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          return self.decoder.get(_lowerCamelCase			,   self.unk_token)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   **UpperCamelCase):
          return super().batch_decode(_lowerCamelCase			,   **_lowerCamelCase)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   **UpperCamelCase):
          return super().decode(_lowerCamelCase			,   **_lowerCamelCase)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          lowerCamelCase__				   =      []
          lowerCamelCase__				   =      ''''''
          for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        out_string += sp_model.decode_pieces(_lowerCamelCase) + token + " "
                        lowerCamelCase__				   =      []
                 else:
                        current_sub_tokens.append(_lowerCamelCase)
          out_string += sp_model.decode_pieces(_lowerCamelCase)
          return out_string.strip()







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase=None):
          if token_ids_a is None:
                 return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.spm_source
          lowerCamelCase__				   =      self.encoder







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.spm_target
          if self.separate_vocabs:
                 lowerCamelCase__				   =      self.target_encoder







   @property
   def     __UpperCAmelCase      ( self):
          return len(self.encoder)







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase = None):
          if not os.path.isdir(_lowerCamelCase):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                 return
          lowerCamelCase__				   =      []

          if self.separate_vocabs:
                 lowerCamelCase__				   =      os.path.join(
                     _lowerCamelCase			,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]			,   )
                 lowerCamelCase__				   =      os.path.join(
                     _lowerCamelCase			,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]			,   )
                 save_json(self.encoder			,   _lowerCamelCase)
                 save_json(self.target_encoder			,   _lowerCamelCase)
                 saved_files.append(_lowerCamelCase)
                 saved_files.append(_lowerCamelCase)
          else:
                 lowerCamelCase__				   =      os.path.join(
                     _lowerCamelCase			,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"])
                 save_json(self.encoder			,   _lowerCamelCase)
                 saved_files.append(_lowerCamelCase)

          for spm_save_filename, spm_orig_path, spm_model in zip(
              [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]			,   self.spm_files			,   [self.spm_source, self.spm_target]			,   ):
                 lowerCamelCase__				   =      os.path.join(
                     _lowerCamelCase			,   (filename_prefix + "-" if filename_prefix else "") + spm_save_filename)
                 if os.path.abspath(_lowerCamelCase) != os.path.abspath(_lowerCamelCase) and os.path.isfile(_lowerCamelCase):
                        copyfile(_lowerCamelCase			,   _lowerCamelCase)
                        saved_files.append(_lowerCamelCase)
                 elif not os.path.isfile(_lowerCamelCase):
                        with open(_lowerCamelCase			,   "wb") as fi:
                               lowerCamelCase__				   =      spm_model.serialized_model_proto()
                               fi.write(_lowerCamelCase)
                        saved_files.append(_lowerCamelCase)

          return tuple(_lowerCamelCase)







   def     __UpperCAmelCase      ( self):
          return self.get_src_vocab()







   def     __UpperCAmelCase      ( self):
          return dict(self.encoder			,   **self.added_tokens_encoder)







   def     __UpperCAmelCase      ( self):
          return dict(self.target_encoder			,   **self.added_tokens_decoder)







   def __getstate__( self):
          lowerCamelCase__				   =      self.__dict__.copy()
          state.update(
              {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]})
          return state







   def __setstate__( self			,   UpperCamelCase):
          lowerCamelCase__				   =      d

          # for backward compatibility
          if not hasattr(self			,   "sp_model_kwargs"):
                 lowerCamelCase__				   =      {}

          lowerCamelCase__				   =      (load_spm(_lowerCamelCase			,   self.sp_model_kwargs) for f in self.spm_files)
          lowerCamelCase__				   =      self.spm_source
          self._setup_normalizer()







   def     __UpperCAmelCase      ( self			,   *UpperCamelCase			,   **UpperCamelCase):
          return 1







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      set(self.all_special_ids)  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special
          return [1 if x in all_special_ids else 0 for x in seq]







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase = None			,   UpperCamelCase = False):
          if already_has_special_tokens:
                 return self._special_token_mask(_lowerCamelCase)
          elif token_ids_a is None:
                 return self._special_token_mask(_lowerCamelCase) + [1]
          else:
                 return self._special_token_mask(token_ids_a + token_ids_a) + [1]







def   lowerCAmelCase(  a__    :  Dict      , a__    :  List[Any]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      sentencepiece.SentencePieceProcessor(**a__      )
       spm.Load(a__      )
       return spm







def   lowerCAmelCase(  a__    :  Any      , a__    :  Optional[Any]      ):







       '''simple docstring'''






       with open(a__      , "w"      ) as f:
              json.dump(a__      , a__      , indent=2      )







def   lowerCAmelCase(  a__    :  Any      ):







       '''simple docstring'''






       with open(a__      , "r"      ) as f:
              return json.load(a__      )



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()
a__				:      Dict    		=     logging.get_logger(__name__)

a__				:      str    		=     '''Hello world! cécé herlolip'''







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE				:				List[str]				      = os.path.split(a__   )
    SCREAMING_SNAKE_CASE				:				Any				      = DataaVecTextModel.from_pretrained(
        a__ ,						checkpoint_file=a__   )
    dataavec.eval()  # disable dropout
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec.models[0]
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_model.encoder.sentence_encoder
    SCREAMING_SNAKE_CASE				:				List[str]				      = 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:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our BERT config:''' ,						a__   )

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = DataaVecTextForSequenceClassification(a__   ) if classification_head else DataaVecTextForMaskedLM(a__   )
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec_sent_encoder.embed_tokens.weight
    SCREAMING_SNAKE_CASE				:				int				      = dataavec_sent_encoder.embed_positions.weight
    SCREAMING_SNAKE_CASE				:				int				      = torch.zeros_like(
        model.dataavec_text.embeddings.token_type_embeddings.weight   )  # just zero them out b/c data2vec doesn't use them.
    SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_sent_encoder.layernorm_embedding.weight
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_sent_encoder.layernorm_embedding.bias

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

        # self attention
        SCREAMING_SNAKE_CASE				:				BertSelfAttention				      = 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)   )}"""
        )

        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.q_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.q_proj.bias
        SCREAMING_SNAKE_CASE				:				str				      = dataavec_layer.self_attn.k_proj.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.self_attn.k_proj.bias
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.v_proj.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.self_attn.v_proj.bias

        # self-attention output
        SCREAMING_SNAKE_CASE				:				BertSelfOutput				      = 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}"""
        SCREAMING_SNAKE_CASE				:				List[Any]				      = dataavec_layer.self_attn.out_proj.weight
        SCREAMING_SNAKE_CASE				:				Dict				      = dataavec_layer.self_attn.out_proj.bias
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.self_attn_layer_norm.bias

        # intermediate
        SCREAMING_SNAKE_CASE				:				BertIntermediate				      = 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}"""
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = dataavec_layer.fca.bias

        # output
        SCREAMING_SNAKE_CASE				:				BertOutput				      = 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}"""
        SCREAMING_SNAKE_CASE				:				int				      = dataavec_layer.fca.weight
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.fca.bias
        SCREAMING_SNAKE_CASE				:				Any				      = dataavec_layer.final_layer_norm.weight
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = dataavec_layer.final_layer_norm.bias
        # end of layer

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

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

    SCREAMING_SNAKE_CASE				:				List[str]				      = model(a__   )[0]
    if classification_head:
        SCREAMING_SNAKE_CASE				:				Tuple				      = dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(a__   )   )
    else:
        SCREAMING_SNAKE_CASE				:				List[str]				      = dataavec_model(a__   )[0]
    print(our_output.shape ,						their_output.shape   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.max(torch.abs(our_output - their_output   )   ).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
    SCREAMING_SNAKE_CASE				:				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__":
      a__				:      str    		=     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.'''
      )
      a__				:      int    		=     parser.parse_args()
      convert_dataavec_checkpoint_to_pytorch(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )
