




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


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

_UpperCAmelCase		:  Union[str, Any]	=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_UpperCAmelCase		:  Dict	=   {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

_UpperCAmelCase		:  List[Any]	=   {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}




class       __lowerCAmelCase				(							A_):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a           =  ['''input_ids''', '''attention_mask''']
       _a           =  RobertaTokenizer






       def __init__(      self:    Tuple				,    _lowerCAmelCase:    Optional[Any]=None				,    _lowerCAmelCase:    str=None				,    _lowerCAmelCase:    List[Any]=None				,    _lowerCAmelCase:    Optional[int]="replace"				,    _lowerCAmelCase:    Tuple="<s>"				,    _lowerCAmelCase:    Optional[Any]="</s>"				,    _lowerCAmelCase:    str="</s>"				,    _lowerCAmelCase:    Dict="<s>"				,    _lowerCAmelCase:    str="<unk>"				,    _lowerCAmelCase:    Optional[Any]="<pad>"				,    _lowerCAmelCase:    Dict="<mask>"				,    _lowerCAmelCase:    Optional[int]=False				,    _lowerCAmelCase:    Optional[int]=True				,    **_lowerCAmelCase:    Any				,    ):
         super().__init__(
             _lowerCamelCase				,    _lowerCamelCase				,    tokenizer_file=_lowerCamelCase				,    errors=_lowerCamelCase				,    bos_token=_lowerCamelCase				,    eos_token=_lowerCamelCase				,    sep_token=_lowerCamelCase				,    cls_token=_lowerCamelCase				,    unk_token=_lowerCamelCase				,    pad_token=_lowerCamelCase				,    mask_token=_lowerCamelCase				,    add_prefix_space=_lowerCamelCase				,    trim_offsets=_lowerCamelCase				,    **_lowerCamelCase				,    )

         lowercase			:List[str]							=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get("add_prefix_space"				,    _lowerCamelCase ) != add_prefix_space:
           lowercase			:List[str]							=  getattr(_lowerCamelCase				,    pre_tok_state.pop("type" ) )
           lowercase			:int							=  add_prefix_space
           lowercase			:Optional[Any]							=  pre_tok_class(**_lowerCamelCase )

         lowercase			:List[str]							=  add_prefix_space

         lowercase			:Tuple							=  "post_processor"
         lowercase			:List[Any]							=  getattr(self.backend_tokenizer				,    _lowerCamelCase				,    _lowerCamelCase )
         if tokenizer_component_instance:
           lowercase			:Dict							=  json.loads(tokenizer_component_instance.__getstate__() )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             lowercase			:Optional[int]							=  tuple(state["sep"] )
           if "cls" in state:
             lowercase			:Dict							=  tuple(state["cls"] )

           lowercase			:str							=  False

           if state.get("add_prefix_space"				,    _lowerCamelCase ) != add_prefix_space:
             lowercase			:List[Any]							=  add_prefix_space
             lowercase			:Optional[Any]							=  True

           if state.get("trim_offsets"				,    _lowerCamelCase ) != trim_offsets:
             lowercase			:str							=  trim_offsets
             lowercase			:Tuple							=  True

           if changes_to_apply:
             lowercase			:Dict							=  getattr(_lowerCamelCase				,    state.pop("type" ) )
             lowercase			:List[str]							=  component_class(**_lowerCamelCase )
             setattr(self.backend_tokenizer				,    _lowerCamelCase				,    _lowerCamelCase )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         if self._mask_token is None:
           if self.verbose:
             logger.error("Using mask_token, but it is not set yet." )
           return None
         return str(self._mask_token )






       @mask_token.setter
       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    Tuple ):
         lowercase			:int							=  AddedToken(_lowerCamelCase				,    lstrip=_lowerCamelCase				,    rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase				,    _lowerCamelCase ) else value
         lowercase			:List[str]							=  value






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    *_lowerCAmelCase:    str				,    **_lowerCAmelCase:    List[str] ):
         lowercase			:List[str]							=  kwargs.get("is_split_into_words"				,    _lowerCamelCase )
         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*_lowerCamelCase				,    **_lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    *_lowerCAmelCase:    Optional[Any]				,    **_lowerCAmelCase:    Dict ):
         lowercase			:Any							=  kwargs.get("is_split_into_words"				,    _lowerCamelCase )

         assert self.add_prefix_space or not is_split_into_words, (
             F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*_lowerCamelCase				,    **_lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):
         lowercase			:Any							=  self._tokenizer.model.save(_lowerCamelCase				,    name=_lowerCamelCase )
         return tuple(_lowerCamelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    str=None ):
         lowercase			:Dict							=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):
         lowercase			:Dict							=  [self.sep_token_id]
         lowercase			:Union[str, Any]							=  [self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]





"""simple docstring"""



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase__          = ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase__          = {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = ' Hello world! cécé herlolip'

UpperCAmelCase__          = [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase ,					__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

  bart.model.upgrade_state_dict(bart.model.state_dict()		)
  if hf_checkpoint_name is None:
    _snake_case					     =       checkpoint_path.replace('''.''' ,					'''-'''		)
  _snake_case					     =       BartConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       bart.encode(__lowerCamelCase		).unsqueeze(0		)
  _snake_case					     =       BartTokenizer.from_pretrained(__lowerCamelCase		).encode(__lowerCamelCase ,					return_tensors='''pt'''		).unsqueeze(0		)
  if not torch.eq(__lowerCamelCase ,					__lowerCamelCase		).all():
    raise ValueError(
        f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

  if checkpoint_path == "bart.large.mnli":
    _snake_case					     =       bart.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''model.decoder.embed_tokens.weight''']
    for src, dest in mnli_rename_keys:
      rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       BartForSequenceClassification(__lowerCamelCase		).eval()
    model.load_state_dict(__lowerCamelCase		)
    _snake_case					     =       bart.predict('''mnli''' ,					__lowerCamelCase ,					return_logits=__lowerCamelCase		)
    _snake_case					     =       model(__lowerCamelCase		)[0]  # logits
  else:  # no classification heads to worry about
    _snake_case					     =       bart.model.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''decoder.embed_tokens.weight''']
    _snake_case					     =       bart.extract_features(__lowerCamelCase		)
    if hf_checkpoint_name == "facebook/bart-large":
      _snake_case					     =       BartModel(__lowerCamelCase		).eval()
      model.load_state_dict(__lowerCamelCase		)
      _snake_case					     =       model(__lowerCamelCase		).model[0]
    else:
      _snake_case					     =       BartForConditionalGeneration(__lowerCamelCase		).eval()  # an existing summarization ckpt
      model.model.load_state_dict(__lowerCamelCase		)
      if hasattr(__lowerCamelCase ,					'''lm_head'''		):
        _snake_case					     =       make_linear_from_emb(model.model.shared		)
      _snake_case					     =       model.model(__lowerCamelCase		)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase__          = parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



