

import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Any    ):



     '''simple docstring'''





     lowercase__					:     Union[str, Any]   = RoFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_    )
     print(f"""Building PyTorch model from configuration: {config}"""    )
     lowercase__					:     Union[str, Any]   = RoFormerForMaskedLM(SCREAMING_SNAKE_CASE_    )

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(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_			,  _use_new_zipfile_serialization=SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       snake_case_    						=				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(
           '''--bert_config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained BERT 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.'''
       )
       snake_case_    						=				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
         return True





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
         lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
         lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
         lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

         lowercase__					:     Any   = tokenizer.tokenize(a)
         self.assertListEqual(a		,     a)

         lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]

         lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)

         lowercase__					:     Optional[Any]   = tokenizer.decode(a)
         self.assertEqual(a		,     a)

