
import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class        UpperCAmelCase_			( a__  ,       unittest.TestCase     ):


  '''simple docstring'''







  a__           =		BioGptTokenizer
  a__           =		False
  def 							_lowercase							( self			: Optional[int]    )			->	List[str]:







    """simple docstring"""






    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __magic_name__				     =       [
        'l',
        'o',
        'w',
        'e',
        'r',
        's',
        't',
        'i',
        'd',
        'n',
        'w</w>',
        'r</w>',
        't</w>',
        'lo',
        'low',
        'er</w>',
        'low</w>',
        'lowest</w>',
        'newer</w>',
        'wider</w>',
        '<unk>',
    ]
    __magic_name__				     =       dict(zip(SCREAMING_SNAKE_CASE_			,							range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
    __magic_name__				     =       ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

    __magic_name__				     =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""vocab_file"""]    )
    __magic_name__				     =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""merges_file"""]    )
    with open(self.vocab_file			,							"""w"""    ) as fp:
      fp.write(json.dumps(SCREAMING_SNAKE_CASE_    )    )
    with open(self.merges_file			,							"""w"""    ) as fp:
      fp.write("""\n""".join(SCREAMING_SNAKE_CASE_    )    )
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Optional[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       'lower newer'
    __magic_name__				     =       'lower newer'
    return input_text, output_text
  def 							_lowercase							( self			: Optional[int]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       BioGptTokenizer(self.vocab_file			,							self.merges_file    )

    __magic_name__				     =       'lower'
    __magic_name__				     =       ['low', 'er</w>']
    __magic_name__				     =       tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
    self.assertListEqual(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_    )

    __magic_name__				     =       tokens + ['<unk>']
    __magic_name__				     =       [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )			,							SCREAMING_SNAKE_CASE_    )





  @slow
  def 							_lowercase							( self			: Dict    )			->	str:







    """simple docstring"""






    __magic_name__				     =       BioGptTokenizer.from_pretrained("""microsoft/biogpt"""    )

    __magic_name__				     =       tokenizer.encode("""sequence builders"""			,							add_special_tokens=SCREAMING_SNAKE_CASE_    )
    __magic_name__				     =       tokenizer.encode("""multi-sequence build"""			,							add_special_tokens=SCREAMING_SNAKE_CASE_    )

    __magic_name__				     =       tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_    )
    __magic_name__				     =       tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_    )

    self.assertTrue(encoded_sentence == [2] + text    )
    self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )






import argparse

import torch

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


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


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 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.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



