


'''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 					lowerCAmelCase_     (    UpperCAmelCase  ):






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[int]:
            return True






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

            _A				=       tokenizer.tokenize(_UpperCamelCase      )
            self.assertListEqual(_UpperCamelCase ,    _UpperCamelCase      )

            _A				=       tokens + [tokenizer.unk_token]

            _A				=       [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(_UpperCamelCase      ) ,    _UpperCamelCase      )

            _A				=       tokenizer.decode(_UpperCamelCase      )
            self.assertEqual(_UpperCamelCase ,    _UpperCamelCase      )









'''simple docstring'''
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         __UpperCamelCase       ,						__UpperCamelCase       ,						model_class='bert'       ,						is_encoder_named_decoder=__UpperCamelCase       ,						is_encoder=__UpperCamelCase       ,						)

     # Save pytorch-model
     print(F'Save PyTorch model and config to {pytorch_dump_path}'		)
     model.save_pretrained(__UpperCamelCase		)


if __name__ == "__main__":
    lowerCAmelCase  =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_named_decoder""",
        action="""store_true""",
        help="""If decoder has to be renamed to encoder in PyTorch model.""",
    )
    parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
    parser.add_argument("""--vocab_size""", default=50_358, type=int, help="""Vocab size of model""")
    lowerCAmelCase  =				parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_hub_path,
        args.pytorch_dump_path,
        args.is_encoder_named_decoder,
        args.vocab_size,
        is_encoder=args.is_encoder,
    )





