





"""simple docstring"""




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


UpperCamelCase_									=logging.get_logger(__name__)




@dataclass
class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   [
				    '''no_inference''',
				    '''no_cuda''',
				    '''no_tpu''',
				    '''no_speed''',
				    '''no_memory''',
				    '''no_env_print''',
				    '''no_multi_process''',
				]






				def __init__(			self		:   Any,			**lowerCAmelCase__		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																						_UpperCamelCase      : Optional[int]         =   deprecated_arg[3:]
																						_UpperCamelCase      : int         =   not kwargs.pop(lowerCAmelCase__							)
																						logger.warning(
																						    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																						    f""" {positive_arg}={kwargs[positive_arg]}"""							)
										_UpperCamelCase      : Optional[int]         =   kwargs.pop('''tpu_name''',			self.tpu_name							)
										_UpperCamelCase      : Optional[Any]         =   kwargs.pop('''device_idx''',			self.device_idx							)
										_UpperCamelCase      : Dict         =   kwargs.pop('''eager_mode''',			self.eager_mode							)
										_UpperCamelCase      : Tuple         =   kwargs.pop('''use_xla''',			self.use_xla							)
										super().__init__(**lowerCAmelCase__							)

				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={'''help''': '''Name of TPU'''}     ,    )
				UpperCamelCase								=   field(
				    default=0     ,    metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}     ,    )
				UpperCamelCase								=   field(default=_lowerCAmelCase     ,    metadata={'''help''': '''Benchmark models in eager model.'''}    )
				UpperCamelCase								=   field(
				    default=_lowerCAmelCase     ,    metadata={
				        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
				    }     ,    )






				@cached_property
				def     snake_case     (			self		:   List[Any]							)     ->				Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:




										'''simple docstring'''



										requires_backends(self,			['''tf''']							)
										_UpperCamelCase      : Union[str, Any]         =   None
										if self.tpu:
																try:
																						if self.tpu_name:
																												_UpperCamelCase      : List[Any]         =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name							)
																						else:
																												_UpperCamelCase      : Union[str, Any]         =   tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																						_UpperCamelCase      : Optional[int]         =   None
										return tpu






				@cached_property
				def     snake_case     (			self		:   Tuple							)     ->				Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:




										'''simple docstring'''



										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							)

																_UpperCamelCase      : str         =   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'''							)
																						_UpperCamelCase      : int         =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""							)
																else:
																						tf.config.set_visible_devices([],			'''GPU'''							)  # disable GPU
																						_UpperCamelCase      : Optional[Any]         =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""							)

										return strategy






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				bool:




										'''simple docstring'''



										requires_backends(self,			['''tf''']							)
										return self._setup_tpu is not None






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				"tf.distribute.Strategy":




										'''simple docstring'''



										requires_backends(self,			['''tf''']							)
										return self._setup_strategy






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Optional[int]:




										'''simple docstring'''



										requires_backends(self,			['''tf''']							)
										return tf.config.list_physical_devices('''GPU'''							)






				@property
				def     snake_case     (			self		:   int							)     ->				int:




										'''simple docstring'''



										requires_backends(self,			['''tf''']							)
										if self.cuda:
																return len(self.gpu_list							)
										return 0






				@property
				def     snake_case     (			self		:   Dict							)     ->				bool:




										'''simple docstring'''



										return self.n_gpu > 0










"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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(
						    """--mobilebert_config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained MobileBERT 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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



