




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_fnet import FNetTokenizer
else:
		lowercase__							:	List[Any]											=		None

lowercase__							:	Tuple											=		logging.get_logger(__name__)
lowercase__							:	Dict											=		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase__							:	int											=		{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

lowercase__							:	int											=		{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowercase__							:	str											=		"▁"
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  VOCAB_FILES_NAMES
						lowerCAmelCase_							=  PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_							=  ['''input_ids''', '''token_type_ids''']
						lowerCAmelCase_							=  FNetTokenizer






						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	str=None			,      __lowercase				:	Optional[int]=None			,      __lowercase				:	Union[str, Any]=False			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	Dict="<unk>"			,      __lowercase				:	int="[SEP]"			,      __lowercase				:	int="<pad>"			,      __lowercase				:	Optional[Any]="[CLS]"			,      __lowercase				:	Union[str, Any]="[MASK]"			,      **__lowercase				:	str			,      ):






							"""simple docstring"""






							snake_case_						       =	(
							    AddedToken(__lowercase			,      lstrip=__lowercase			,      rstrip=__lowercase			,      normalized=__lowercase		)
							    if isinstance(__lowercase			,      __lowercase		)
							    else mask_token
							)

							super().__init__(
							    __lowercase			,      tokenizer_file=__lowercase			,      do_lower_case=__lowercase			,      remove_space=__lowercase			,      keep_accents=__lowercase			,      unk_token=__lowercase			,      sep_token=__lowercase			,      pad_token=__lowercase			,      cls_token=__lowercase			,      mask_token=__lowercase			,      **__lowercase			,      )

							snake_case_						       =	do_lower_case
							snake_case_						       =	remove_space
							snake_case_						       =	keep_accents
							snake_case_						       =	vocab_file
							snake_case_						       =	False if not self.vocab_file else True






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[int]			,      __lowercase				:	Optional[List[int]] = None		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	List[int]			,      __lowercase				:	Optional[List[int]] = None		):






							"""simple docstring"""






							snake_case_						       =	[self.sep_token_id]
							snake_case_						       =	[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		) * [0] + len(token_ids_a + sep		) * [1]






						def 			snake_case__		(		self				:	str			,      __lowercase				:	str			,      __lowercase				:	Optional[str] = None		):






							"""simple docstring"""






							if not os.path.isdir(__lowercase		):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
								return
							snake_case_						       =	os.path.join(
							    __lowercase			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

							return (out_vocab_file,)






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT model. \n"
		        "This specifies the model architecture."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
