
'''simple docstring'''


import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def 			__snake_case  (     _UpperCAmelCase       :							List[str],   _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							str,   _UpperCAmelCase       :							int):
   UpperCamelCase	  =	FunnelConfig.from_json_file(_UpperCAmelCase)
   print(f'Building PyTorch model from configuration: {config}')
   UpperCamelCase	  =	FunnelBaseModel(_UpperCAmelCase) if base_model else FunnelModel(_UpperCAmelCase)

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(_UpperCAmelCase,   _UpperCAmelCase,   _UpperCAmelCase)

   # Save pytorch-model
   print(f'Save PyTorch model to {pytorch_dump_path}')
   torch.save(model.state_dict(),   _UpperCAmelCase)


if __name__ == "__main__":
   snake_case_			:	Optional[Any] 	=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--base_model', action='store_true', help='Whether you want just the base model (no decoder) or not.'
   )
   snake_case_			:	List[Any] 	=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
   )







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

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


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

_snake_case      :							Union[str, Any]     	=    logging.get_logger(__name__)

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) else mask_token

										super().__init__(
										    lowerCAmelCase__		,    tokenizer_file=lowerCAmelCase__		,    bos_token=lowerCAmelCase__		,    eos_token=lowerCAmelCase__		,    unk_token=lowerCAmelCase__		,    sep_token=lowerCAmelCase__		,    cls_token=lowerCAmelCase__		,    pad_token=lowerCAmelCase__		,    mask_token=lowerCAmelCase__		,    **lowerCAmelCase__		,    )

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





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

										if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowercase         =      [self.cls_token_id]
										lowercase         =      [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [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]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										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(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      os.path.join(
										    lowerCAmelCase__		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

										return (out_vocab_file,)
