





"""simple docstring"""




import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def        a_				(							_lowercase       ,						_lowercase   ):
						# Initialize PyTorch model
						_UpperCamelCase			:					Any     =    CanineConfig()
						_UpperCamelCase			:					str     =    CanineModel(_lowercase   )
						model.eval()

						print(F"""Building PyTorch model from configuration: {config}"""   )

						# Load weights from tf checkpoint
						load_tf_weights_in_canine(_lowercase       ,						_lowercase       ,						_lowercase   )

						# Save pytorch-model (weights and configuration)
						print(F"""Save PyTorch model to {pytorch_dump_path}"""   )
						model.save_pretrained(_lowercase   )

						# Save tokenizer files
						_UpperCamelCase			:					Dict     =    CanineTokenizer()
						print(F"""Save tokenizer files to {pytorch_dump_path}"""   )
						tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
						UpperCamelCase_           =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--tf_checkpoint_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to a folder where the PyTorch model will be placed.""",
						)
						UpperCamelCase_           =parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)









'''simple docstring'''


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

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


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_snake_case      :      int   							=		None


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


_snake_case      :      Union[str, Any]   							=		'▁'

_snake_case      :      Any   							=		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :      List[str]   							=		{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case      :      Union[str, Any]   							=		{
    'google/reformer-crime-and-punishment': 524288,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']
							lowercase_							=				ReformerTokenizer







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Any="<unk>"							,			lowerCAmelCase_       :  List[str]=[]							,			**lowerCAmelCase_       :  List[Any]							,			)			->							Dict:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_							,			tokenizer_file=lowerCAmelCase_							,			eos_token=lowerCAmelCase_							,			unk_token=lowerCAmelCase_							,			additional_special_tokens=lowerCAmelCase_							,			**lowerCAmelCase_							,			)

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







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = None					)			->							Tuple[str]:


														"""simple docstring"""


														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
														_a	     =				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,)

