





'''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 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      Tuple			,     __lowercase							:      Optional[Any]       )		->       Union[str, Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   MobileBertConfig.from_json_file(__lowercase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _UpperCAmelCase     							=   MobileBertForPreTraining(__lowercase       )
     # Load weights from tf checkpoint
     _UpperCAmelCase     							=   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__":
  __SCREAMING_SNAKE_CASE     :Tuple       =				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.'''
  )
  __SCREAMING_SNAKE_CASE     :Optional[int]       =				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)










import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]     ):
					monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"      ,      set()     )







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					class 			a__    :






											def __init__(   self		,			_A			):




																"""simple docstring"""



																__lowerCAmelCase				   =       metric_id






					class 			a__    :
											_a							:    Optional[int]					=    [MetricMock(snake_case__      ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






											def 				__SCREAMING_SNAKE_CASE(   self			):




																"""simple docstring"""



																return self._metrics






					monkeypatch.setattr("datasets.inspect.huggingface_hub"      ,      HfhMock()     )







@pytest.mark.parametrize(
    "func, args"      ,      [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]     )
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					if "tmp_path" in args:
										__lowerCAmelCase				   =       tuple(arg if arg != "tmp_path" else tmp_path for arg in args     )
					with pytest.warns(SCREAMING_SNAKE_CASE_      ,      match="https://huggingface.co/docs/evaluate"     ):
										func(*SCREAMING_SNAKE_CASE_     )




