
"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

A__      :	str          =    """▁"""

A__      :	Union[str, Any]          =    {"""vocab_file""": """sentencepiece.bpe.model"""}

A__      :	Optional[Any]          =    {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

A__      :	Dict          =    {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A								=							['input_ids', 'attention_mask']



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase="<s>" ,			__UpperCamelCase="</s>" ,			__UpperCamelCase="</s>" ,			__UpperCamelCase="<s>" ,			__UpperCamelCase="<unk>" ,			__UpperCamelCase="<pad>" ,			__UpperCamelCase="<mask>" ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__							: Any 							=						AddedToken(__UpperCamelCase ,			lstrip=__UpperCamelCase ,			rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase ,			__UpperCamelCase  ) else mask_token

          UpperCAmelCase__							: Union[str, Any] 							=						{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=__UpperCamelCase ,			eos_token=__UpperCamelCase ,			unk_token=__UpperCamelCase ,			sep_token=__UpperCamelCase ,			cls_token=__UpperCamelCase ,			pad_token=__UpperCamelCase ,			mask_token=__UpperCamelCase ,			sp_model_kwargs=self.sp_model_kwargs ,			**__UpperCamelCase ,			)

          UpperCAmelCase__							: int 							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
          self.sp_model.Load(str(__UpperCamelCase  )  )
          UpperCAmelCase__							: Tuple 							=						vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCAmelCase__							: int 							=						{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCAmelCase__							: Tuple 							=						1

          UpperCAmelCase__							: Optional[int] 							=						len(self.sp_model  ) + self.fairseq_offset
          UpperCAmelCase__							: str 							=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}



     def __getstate__(      self  )->					Any:
          UpperCAmelCase__							: Union[str, Any] 							=						self.__dict__.copy()
          UpperCAmelCase__							: List[str] 							=						None
          UpperCAmelCase__							: Union[str, Any] 							=						self.sp_model.serialized_model_proto()
          return state



     def __setstate__(      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: List[str] 							=						d

          # for backward compatibility
          if not hasattr(self ,			"sp_model_kwargs"  ):
               UpperCAmelCase__							: List[Any] 							=						{}

          UpperCAmelCase__							: Dict 							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:

          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase__							: Optional[int] 							=						[self.cls_token_id]
          UpperCAmelCase__							: Any 							=						[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = False  )->					List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCamelCase ,			token_ids_a=__UpperCamelCase ,			already_has_special_tokens=__UpperCamelCase  )

          if token_ids_a is None:
               return [1] + ([0] * len(__UpperCamelCase  )) + [1]
          return [1] + ([0] * len(__UpperCamelCase  )) + [1, 1] + ([0] * len(__UpperCamelCase  )) + [1]



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:
          UpperCAmelCase__							: List[Any] 							=						[self.sep_token_id]
          UpperCAmelCase__							: Dict 							=						[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]



     @property
     def  lowerCAmelCase__       (      self  )->					Dict:
          return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: List[str] 							=						{self.convert_ids_to_tokens(__UpperCamelCase  ): i for i in range(self.vocab_size  )}
          vocab.update(self.added_tokens_encoder  )
          return vocab



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          return self.sp_model.encode(__UpperCamelCase ,			out_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__							: str 							=						self.sp_model.PieceToId(__UpperCamelCase  )

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: List[Any] 							=						"".join(__UpperCamelCase  ).replace(__UpperCamelCase ,			" "  ).strip()
          return out_string



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Tuple[str]:
          if not os.path.isdir(__UpperCamelCase  ):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
               return
          UpperCAmelCase__							: List[str] 							=						os.path.join(
              __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

          if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCamelCase  ) and os.path.isfile(self.vocab_file  ):
               copyfile(self.vocab_file ,			__UpperCamelCase  )
          elif not os.path.isfile(self.vocab_file  ):
               with open(__UpperCamelCase ,			"wb"  ) as fi:
                    UpperCAmelCase__							: str 							=						self.sp_model.serialized_model_proto()
                    fi.write(__UpperCamelCase  )

          return (out_vocab_file,)





"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



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






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



