


'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
		from sagemaker import Session, TrainingJobAnalytics
		from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'      ,						'False'   )   ) is not True      ,						reason='Skipping test because should only be run when releasing minor transformers version'      ,						)
@pytest.mark.usefixtures('sm_env'   )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_50, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]   )
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if self.framework == "pytorch":
												subprocess.run(
												    F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()       ,  encoding='''utf-8'''       ,  check=__A       ,  )
							assert hasattr(self       ,  '''env'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=1				):

							'''simple docstring'''


							return HuggingFace(
							    entry_point=self.script       ,  source_dir=self.env.test_path       ,  role=self.env.role       ,  image_uri=self.env.image_uri       ,  base_job_name=F'''{self.env.base_job_name}-single'''       ,  instance_count=__A       ,  instance_type=self.instance_type       ,  debugger_hook_config=__A       ,  hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  py_version='''py36'''       ,  )




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							TrainingJobAnalytics(__A				).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					self.create_estimator()

							# run training
							estimator.fit()

							# result dataframe
							UpperCamelCase__  :Optional[Any]				   =					TrainingJobAnalytics(estimator.latest_training_job.name				).dataframe()

							# extract kpis
							UpperCamelCase__  :List[str]				   =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']				)
							UpperCamelCase__  :Union[str, Any]				   =					list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']				)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							UpperCamelCase__  :List[str]				   =					(
							    Session().describe_training_job(estimator.latest_training_job.name				).get('''TrainingTimeInSeconds'''       ,  999999				)
							)

							# assert kpis
							assert train_runtime <= self.results["train_runtime"]
							assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy				)
							assert all(t <= self.results['''eval_loss'''] for t in eval_loss				)

							# dump tests result into json file to share in PR
							with open(F'''{estimator.latest_training_job.name}.json'''       ,  '''w'''				) as outfile:
												json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}       ,  __A				)


import argparse

import torch

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


logging.set_verbosity_info()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Union[str, Any]			,a__    :   Optional[int]       )					->				List[Any]:
	# Initialise PyTorch model
	__A :			Dict		    =			MobileBertConfig.from_json_file(a__       )
	print(f"""Building PyTorch model from configuration: {config}"""       )
	__A :			Tuple		    =			MobileBertForPreTraining(a__       )
	# Load weights from tf checkpoint
	__A :			Dict		    =			load_tf_weights_in_mobilebert(a__			,a__			,a__       )
	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
	torch.save(model.state_dict()			,a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					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(
			    '''--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_							:					Tuple		 =   parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





