





"""simple docstring"""
import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


lowerCAmelCase:	Optional[int]							      =importlib.metadata.version("numpy")
lowerCAmelCase:	str							      =".".join([str(x) for x in sys.version_info[:3]])






class lowerCamelCase__  (    __UpperCamelCase				):



   def    _UpperCAmelCase	(       self	) ->    Any:







      """simple docstring"""
      require_version_core("""numpy<1000.4.5"""	)
      require_version_core("""numpy<1000.4"""	)
      require_version_core("""numpy<1000"""	)

      # le
      require_version_core("""numpy<=1000.4.5"""	)
      require_version_core(f'''numpy<={numpy_ver}'''	)

      # eq
      require_version_core(f'''numpy=={numpy_ver}'''	)

      # ne
      require_version_core("""numpy!=1000.4.5"""	)

      # ge
      require_version_core("""numpy>=1.0"""	)
      require_version_core("""numpy>=1.0.0"""	)
      require_version_core(f'''numpy>={numpy_ver}'''	)

      # gt
      require_version_core("""numpy>1.0.0"""	)

      # mix
      require_version_core("""numpy>1.0.0,<1000"""	)

      # requirement w/o version
      require_version_core("""numpy"""	)

      # unmet requirements due to version conflict
      for req in ["numpy==1.0.0", "numpy>=1000.0.0", f'''numpy<{numpy_ver}''']:
         try:
            require_version_core(snake_case	)
         except ImportError as e:
            self.assertIn(f'''{req} is required'''      ,   str(snake_case	)	)
            self.assertIn("""but found"""      ,   str(snake_case	)	)

        # unmet requirements due to missing module
      for req in ["numpipypie>1", "numpipypie2"]:
         try:
            require_version_core(snake_case	)
         except importlib.metadata.PackageNotFoundError as e:
            self.assertIn(f'''The \'{req}\' distribution was not found and is required by this application'''      ,   str(snake_case	)	)
            self.assertIn("""Try: pip install transformers -U"""      ,   str(snake_case	)	)

        # bogus requirements formats:
        # 1. whole thing
      for req in ["numpy??1.0.0", "numpy1.0.0"]:
         try:
            require_version_core(snake_case	)
         except ValueError as e:
            self.assertIn("""requirement needs to be in the pip package format"""      ,   str(snake_case	)	)
        # 2. only operators
      for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
         try:
            require_version_core(snake_case	)
         except ValueError as e:
            self.assertIn("""need one of """      ,   str(snake_case	)	)





   def    _UpperCAmelCase	(       self	) ->    List[Any]:







      """simple docstring"""
      require_version("""python>=3.6.0"""	)

      # not matching requirements
      for req in ["python>9.9.9", "python<3.0.0"]:
         try:
            require_version_core(snake_case	)
         except ImportError as e:
            self.assertIn(f'''{req} is required'''      ,   str(snake_case	)	)
            self.assertIn(f'''but found python=={python_ver}'''      ,   str(snake_case	)	)









"""simple docstring"""
import argparse

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


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

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


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =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=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =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,
   )


