



import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     OpenAIGPTTokenizer
   SCREAMING_SNAKE_CASE__							=     OpenAIGPTTokenizerFast
   SCREAMING_SNAKE_CASE__							=     True
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _lowerCAmelCase 			=  [
             '''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>''',
         ]
         _lowerCAmelCase 			=  dict(zip(_lowerCAmelCase       ,     range(len(_lowerCAmelCase			)			)			)			)
         _lowerCAmelCase 			=  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

         _lowerCAmelCase 			=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''vocab_file''']			)
         _lowerCAmelCase 			=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''merges_file''']			)
         with open(self.vocab_file       ,     '''w'''			) as fp:
               fp.write(json.dumps(_lowerCAmelCase			)			)
         with open(self.merges_file       ,     '''w'''			) as fp:
               fp.write('''\n'''.join(_lowerCAmelCase			)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         return "lower newer", "lower newer"





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  OpenAIGPTTokenizer(self.vocab_file       ,     self.merges_file			)

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

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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase=15			):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                     _lowerCAmelCase 			=  self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase       ,     **_lowerCAmelCase			)

                     # Simple input
                     _lowerCAmelCase 			=  '''This is a simple input'''
                     _lowerCAmelCase 			=  ['''This is a simple input 1''', '''This is a simple input 2''']
                     _lowerCAmelCase 			=  ('''This is a simple input''', '''This is a pair''')
                     _lowerCAmelCase 			=  [
                         ('''This is a simple input 1''', '''This is a simple input 2'''),
                         ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                     ]

                     # Simple input tests
                     self.assertRaises(_lowerCAmelCase       ,     tokenizer_r.encode       ,     _lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     padding='''max_length'''			)

                     # Simple input
                     self.assertRaises(_lowerCAmelCase       ,     tokenizer_r.encode_plus       ,     _lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     padding='''max_length'''			)

                     # Simple input
                     self.assertRaises(
                         _lowerCAmelCase       ,     tokenizer_r.batch_encode_plus       ,     _lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     padding='''max_length'''       ,     )

                     # Pair input
                     self.assertRaises(_lowerCAmelCase       ,     tokenizer_r.encode       ,     _lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     padding='''max_length'''			)

                     # Pair input
                     self.assertRaises(_lowerCAmelCase       ,     tokenizer_r.encode_plus       ,     _lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     padding='''max_length'''			)

                     # Pair input
                     self.assertRaises(
                         _lowerCAmelCase       ,     tokenizer_r.batch_encode_plus       ,     _lowerCAmelCase       ,     max_length=_lowerCAmelCase       ,     padding='''max_length'''       ,     )





   def 	__lowerCAmelCase							(	self			):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 		UpperCAmelCase						(						snake_case_				):
   pass




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      # Initialise PyTorch model
      _lowerCAmelCase 			=  AlbertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
      print(f'''Building PyTorch model from configuration: {config}'''				)
      _lowerCAmelCase 			=  AlbertForPreTraining(_SCREAMING_SNAKE_CASE				)

      # Load weights from tf checkpoint
      load_tf_weights_in_albert(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      # Save pytorch-model
      print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
      torch.save(model.state_dict()     ,				_SCREAMING_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(
        "--albert_config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained ALBERT 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.albert_config_file, args.pytorch_dump_path)