


import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # Initialise PyTorch model
 __lowerCamelCase		:							Optional[int]		 =						RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__						)
 print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__						)						)						)
 __lowerCamelCase		:							str		 =						RemBertModel(SCREAMING_SNAKE_CASE__						)

 # Load weights from tf checkpoint
 load_tf_weights_in_rembert(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 # Save pytorch-model
 print('Save PyTorch model to {}'.format(SCREAMING_SNAKE_CASE__						)						)
 torch.save(model.state_dict()      ,	SCREAMING_SNAKE_CASE__						)


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





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


lowercase_	   =     logging.get_logger(__name__)


@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]




      def __init__(    self:	Any						,				**a:	Optional[Any]    ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         __lowerCamelCase		:							str		 =						deprecated_arg[3:]
         setattr(self						,				a						,				not kwargs.pop(a    )    )
         logger.warning(
             F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
             F' {positive_arg}={kwargs[positive_arg]}'    )

       __lowerCamelCase		:							str		 =						kwargs.pop('torchscript'						,				self.torchscript    )
       __lowerCamelCase		:							int		 =						kwargs.pop('torch_xla_tpu_print_metrics'						,				self.torch_xla_tpu_print_metrics    )
       __lowerCamelCase		:							Dict		 =						kwargs.pop('fp16_opt_level'						,				self.fpaa_opt_level    )
       super().__init__(**a    )

      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Trace the models using torchscript"""}   )
      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
      __snake_case 	=       field(
          default="""O1""" ,				metadata={
              """help""": (
                  """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                  """See details at https://nvidia.github.io/apex/amp.html"""
              )
          } ,				)




      @cached_property
      def 					_snake_case     (    self:	Dict    ):
       requires_backends(self						,				['torch']    )
       logger.info('PyTorch: setting up devices'    )
       if not self.cuda:
        __lowerCamelCase		:							Dict		 =						torch.device('cpu'    )
        __lowerCamelCase		:							str		 =						0
       elif is_torch_tpu_available():
        __lowerCamelCase		:							Optional[int]		 =						xm.xla_device()
        __lowerCamelCase		:							Dict		 =						0
       else:
        __lowerCamelCase		:							Any		 =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
        __lowerCamelCase		:							Union[str, Any]		 =						torch.cuda.device_count()
       return device, n_gpu




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return is_torch_tpu_available() and self.tpu




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




      @property
      def 					_snake_case     (    self:	int    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[0]




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[1]




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       return self.n_gpu > 0

