



"""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


lowerCAmelCase__        =				['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase__        =				{'''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()
lowerCAmelCase__        =				logging.get_logger(__name__)

lowerCAmelCase__        =				''' Hello world! cécé herlolip'''

lowerCAmelCase__        =				[
    ('''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        snake_case_  (      A_					:		str						):


       '''simple docstring'''





       _lowerCamelCase						:				Optional[Any]    			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
       ]
       for k in ignore_keys:
              state_dict.pop(A_,				A_						)






def        snake_case_  (      A_					:		str,				A_					:		Optional[Any],				A_					:		Tuple						):


       '''simple docstring'''





       _lowerCamelCase						:				str    			= dct.pop(A_						)
       _lowerCamelCase						:				List[Any]    			= val






def        snake_case_  (      A_					:		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Tuple    			= torch.load(A_,				map_location='''cpu'''						)
       _lowerCamelCase						:				Optional[int]    			= torch.hub.load('''pytorch/fairseq''',				'''bart.large.cnn'''						).eval()
       hub_interface.model.load_state_dict(sd['''model''']						)
       return hub_interface






def        snake_case_  (      A_					:		str						):


       '''simple docstring'''





       _lowerCamelCase			,    _lowerCamelCase						:				Union[str, Any]    			= emb.weight.shape
       _lowerCamelCase						:				Any    			= nn.Linear(A_,				A_,				bias=A_						)
       _lowerCamelCase						:				Optional[Any]    			= emb.weight.data
       return lin_layer






@torch.no_grad()
def        snake_case_  (      A_					:		Optional[Any],				A_					:		List[str],				A_					:		Union[str, Any]=None						):


       '''simple docstring'''





       if not os.path.exists(A_						):
              _lowerCamelCase						:				List[Any]    			= torch.hub.load('''pytorch/fairseq''',				A_						).eval()
       else:
              _lowerCamelCase						:				Optional[int]    			= load_xsum_checkpoint(A_						)

       bart.model.upgrade_state_dict(bart.model.state_dict()						)
       if hf_checkpoint_name is None:
              _lowerCamelCase						:				int    			= checkpoint_path.replace('''.''',				'''-'''						)
       _lowerCamelCase						:				List[str]    			= BartConfig.from_pretrained(A_						)
       _lowerCamelCase						:				Any    			= bart.encode(A_						).unsqueeze(0						)
       _lowerCamelCase						:				Any    			= BartTokenizer.from_pretrained(A_						).encode(A_,				return_tensors='''pt'''						).unsqueeze(0						)
       if not torch.eq(A_,				A_						).all():
              raise ValueError(
                  F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''						)

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


if __name__ == "__main__":
   lowerCAmelCase__        =				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'''
   )
   lowerCAmelCase__        =				parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







'''simple docstring'''





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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "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"
        ),
    },
}

lowercase_		       =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Any          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       str          =       ['input_ids', 'attention_mask']
 __lowerCamelCase    :       Union[str, Any]          =       RobertaTokenizer



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


      """simple docstring"""



      super().__init__(
          A ,   A ,   tokenizer_file=A ,   errors=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   cls_token=A ,   unk_token=A ,   pad_token=A ,   mask_token=A ,   add_prefix_space=A ,   trim_offsets=A ,   **A ,   )

      _a      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
           _a      =  getattr(A ,   pre_tok_state.pop('''type'''       )       )
           _a      =  add_prefix_space
           _a      =  pre_tok_class(**A       )

      _a      =  add_prefix_space

      _a      =  '''post_processor'''
      _a      =  getattr(self.backend_tokenizer ,   A ,   A       )
      if tokenizer_component_instance:
           _a      =  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:
                _a      =  tuple(state['''sep''']       )
           if "cls" in state:
                _a      =  tuple(state['''cls''']       )

           _a      =  False

           if state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
                _a      =  add_prefix_space
                _a      =  True

           if state.get('''trim_offsets''' ,   A       ) != trim_offsets:
                _a      =  trim_offsets
                _a      =  True

           if changes_to_apply:
                _a      =  getattr(A ,   state.pop('''type'''       )       )
                _a      =  component_class(**A       )
                setattr(self.backend_tokenizer ,   A ,   A       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      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 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



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



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )
      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(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )

      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(*A ,   **A       )



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


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )



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


      """simple docstring"""



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


      """simple docstring"""



      _a      =  [self.sep_token_id]
      _a      =  [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]



