



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, 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.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]  )
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					def    A__		(			self     ):
							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=lowerCAmelCase    ,	)
							assert hasattr(self    ,	"env"     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
							# distributed data settings
							UpperCAmelCase_						=				{"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

							# creates estimator
							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=lowerCAmelCase    ,	instance_count=lowerCAmelCase    ,	instance_type=self.instance_type    ,	debugger_hook_config=lowerCAmelCase    ,	hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}    ,	metric_definitions=self.env.metric_definitions    ,	distribution=lowerCAmelCase    ,	py_version="py36"    ,	)



					def    A__		(			self    ,	lowerCAmelCase     ):
							TrainingJobAnalytics(lowerCAmelCase     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )



					@parameterized.expand([(2,)]     )
					def    A__		(			self    ,	lowerCAmelCase     ):
							# create estimator
							UpperCAmelCase_						=				self.create_estimator(lowerCAmelCase     )

							# run training
							estimator.fit()

							# result dataframe
							UpperCAmelCase_						=				TrainingJobAnalytics(estimator.latest_training_job.name     ).dataframe()

							# extract kpis
							UpperCAmelCase_						=				list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]     )
							UpperCAmelCase_						=				list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]     )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							UpperCAmelCase_						=				(
							    Session().describe_training_job(estimator.latest_training_job.name     ).get("TrainingTimeInSeconds"    ,	99_9999     )
							)

							# 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}    ,	lowerCAmelCase     )






import argparse

import torch

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


logging.set_verbosity_info()







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

