"""simple docstring"""




from math import ceil





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]	)       ->      int:
  _lowerCAmelCase						:       Dict  			=     list(range(0			,_lowerCamelCase	)	)

  _lowerCAmelCase						:       Tuple  			=     [item for sublist in list(device_map.values()	) for item in sublist]

  # Duplicate check
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  for i in device_map_blocks:
    if device_map_blocks.count(_lowerCamelCase	) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(_lowerCamelCase	)
    # Missing blocks
  _lowerCAmelCase						:       int  			=     [i for i in blocks if i not in device_map_blocks]
  _lowerCAmelCase						:       List[Any]  			=     [i for i in device_map_blocks if i not in blocks]

  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
        """ These attention blocks were specified more than once: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """There are attention blocks for this model that are not specified in the device_map. Add these attention """
        """blocks to a device on the device_map: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
        + str(_lowerCamelCase	)	)






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(range(_lowerCamelCase	)	)
  _lowerCAmelCase						:       Optional[Any]  			=     int(ceil(n_layers / len(_lowerCamelCase	)	)	)
  _lowerCAmelCase						:       Optional[int]  			=     [layers[i : i + n_blocks] for i in range(0			,_lowerCamelCase			,_lowerCamelCase	)]

  return dict(zip(_lowerCamelCase			,_lowerCamelCase	)	)






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': 6_5_0, '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': 6_0_0, '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': 6_0_0, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ])
class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:List[Any]						)		-> Any:
								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		:Any     ,   a		:Optional[Any]						)		-> Dict:
								__UpperCamelCase	:   str			   =					f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
								# distributed data settings
								__UpperCamelCase	:   Optional[int]			   =					{"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=a     ,   instance_count=a     ,   instance_type=self.instance_type     ,   debugger_hook_config=a     ,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}     ,   metric_definitions=self.env.metric_definitions     ,   distribution=a     ,   py_version="py36"     ,   )







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict						)		-> Optional[int]:
								TrainingJobAnalytics(a						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)







	@parameterized.expand([(2,)]						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Tuple						)		-> List[Any]:
								# create estimator
								__UpperCamelCase	:   int			   =					self.create_estimator(a						)

								# run training
								estimator.fit()

								# result dataframe
								__UpperCamelCase	:   Optional[int]			   =					TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

								# extract kpis
								__UpperCamelCase	:   Optional[Any]			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
								__UpperCamelCase	:   Tuple			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								__UpperCamelCase	:   int			   =					(
								    Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"     ,   9_9_9_9_9_9						)
								)

								# 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						)