


'''simple docstring'''

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 			UpperCAmelCase			(							lowerCAmelCase_):

 def 		lowercase_    (			self			:						List[Any],       a_			:						Dict,       a_			:						List[Any],       a_			:						Tuple,       a_			:						Optional[Any],       a_			:						Tuple       ):





    """simple docstring"""



    return True

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"       )
    UpperCamelCase__			    =    "Hugging Face大法好，谁用谁知道。"
    UpperCamelCase__			    =    "Hugging Face大法好,谁用谁知道。<unk>"
    UpperCamelCase__			    =    "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

    UpperCamelCase__			    =    tokenizer.tokenize(lowerCamelCase__       )
    self.assertListEqual(lowerCamelCase__,       lowerCamelCase__       )

    UpperCamelCase__			    =    tokens + [tokenizer.unk_token]

    UpperCamelCase__			    =    [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(lowerCamelCase__       ),       lowerCamelCase__       )

    UpperCamelCase__			    =    tokenizer.decode(lowerCamelCase__       )
    self.assertEqual(lowerCamelCase__,       lowerCamelCase__       )






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )					->  List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		TaConfig.from_json_file(_SCREAMING_SNAKE_CASE      )
      print(F"""Building PyTorch model from configuration: {config}"""      )
      SCREAMING_SNAKE_CASE						=		TaForConditionalGeneration(_SCREAMING_SNAKE_CASE      )

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

      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
      model.save_pretrained(_SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
      SCREAMING_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(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      SCREAMING_SNAKE_CASE_		  =			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


