




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:
						UpperCamelCase__ :   Dict						=							None

UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Dict						=							{
    '''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'''
        ),
    },
}

UpperCamelCase__ :   Dict						=							{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

UpperCamelCase__ :   Any						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[str]						=     VOCAB_FILES_NAMES
				__a     :       int						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     BarthezTokenizer






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

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):

							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : 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]






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							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(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

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

							return (out_vocab_file,)









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        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FunnelConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FunnelBaseModel(lowerCamelCase_  ) if base_model else FunnelModel(lowerCamelCase_  )

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

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


if __name__ == "__main__":
						UpperCamelCase__ :   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.'''
						)
						UpperCamelCase__ :   Tuple						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)



