






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
       import tensorflow as tf


__lowerCamelCase	:		int										=   logging.get_logger(__name__)







@dataclass
class  _lowercase   (   _A						):
      _a							:						int										=							[
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]
      def __init__(     self				,			**a   ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         snake_case__  :    List[str]							       =deprecated_arg[3:]
         snake_case__  :    Union[str, Any]							       =not kwargs.pop(UpperCAmelCase__   )
         logger.warning(
             F"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
             F" {positive_arg}={kwargs[positive_arg]}"   )
       snake_case__  :    Optional[int]							       =kwargs.pop("""tpu_name"""				,			self.tpu_name   )
       snake_case__  :    List[Any]							       =kwargs.pop("""device_idx"""				,			self.device_idx   )
       snake_case__  :    str							       =kwargs.pop("""eager_mode"""				,			self.eager_mode   )
       snake_case__  :    Optional[Any]							       =kwargs.pop("""use_xla"""				,			self.use_xla   )
       super().__init__(**UpperCAmelCase__   )

      _a							:						str										=							field(
          default=_A						,				metadata={'help': 'Name of TPU'}						,				)
      _a							:						int										=							field(
          default=0						,				metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,				)
      _a							:						bool										=							field(default=_A						,				metadata={'help': 'Benchmark models in eager model.'}						)
      _a							:						bool										=							field(
          default=_A						,				metadata={
              'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
          }						,				)
      @cached_property
      def 						lowercase__					(     self   ):
       requires_backends(self				,			["""tf"""]   )
       snake_case__  :    List[Any]							       =None
       if self.tpu:
        try:
         if self.tpu_name:
          snake_case__  :    Optional[int]							       =tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name   )
         else:
          snake_case__  :    Optional[Any]							       =tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
         snake_case__  :    str							       =None
       return tpu
      @cached_property
      def 						lowercase__					(     self   ):
       requires_backends(self				,			["""tf"""]   )
       if self.is_tpu:
        tf.config.experimental_connect_to_cluster(self._setup_tpu   )
        tf.tpu.experimental.initialize_tpu_system(self._setup_tpu   )

        snake_case__  :    Tuple							       =tf.distribute.TPUStrategy(self._setup_tpu   )
       else:
        # currently no multi gpu is allowed
        if self.is_gpu:
         # TODO: Currently only single GPU is supported
         tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,			"""GPU"""   )
         snake_case__  :    List[Any]							       =tf.distribute.OneDeviceStrategy(device=F"/gpu:{self.device_idx}"   )
        else:
         tf.config.set_visible_devices([]				,			"""GPU"""   )  # disable GPU
         snake_case__  :    Union[str, Any]							       =tf.distribute.OneDeviceStrategy(device=F"/cpu:{self.device_idx}"   )

       return strategy
      @property
      def 						lowercase__					(     self   ):
       requires_backends(self				,			["""tf"""]   )
       return self._setup_tpu is not None
      @property
      def 						lowercase__					(     self   ):
       requires_backends(self				,			["""tf"""]   )
       return self._setup_strategy
      @property
      def 						lowercase__					(     self   ):
       requires_backends(self				,			["""tf"""]   )
       return tf.config.list_physical_devices("""GPU"""   )
      @property
      def 						lowercase__					(     self   ):
       requires_backends(self				,			["""tf"""]   )
       if self.cuda:
        return len(self.gpu_list   )
       return 0



      @property
      def 						lowercase__					(     self   ):
       return self.n_gpu > 0













import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()







def 			A__							(      _a  :					int		,  _a  :					Any		,  _a  :					Union[str, Any]		,  _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    Any							       =BigBirdConfig.from_json_file(_a	)
 print(f"Building PyTorch model from configuration: {config}"	)

 if is_trivia_qa:
  snake_case__  :    str							       =BigBirdForQuestionAnswering(_a	)
 else:
  snake_case__  :    Optional[int]							       =BigBirdForPreTraining(_a	)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_a		,  _a		,  is_trivia_qa=_a	)

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


if __name__ == "__main__":
       __lowerCamelCase	:		Union[str, Any]										=   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(
           """--big_bird_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."""
       )
       parser.add_argument(
           """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
       )
       __lowerCamelCase	:		Tuple										=   parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
       )





