



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': 650, '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': 600, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ])
class 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     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      snake_case__   (     self,    __a=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      snake_case__   (     self,    __a):

														'''simple docstring'''

														TrainingJobAnalytics(__a).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv")







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					self.create_estimator()

														# run training
														estimator.fit()

														# result dataframe
														_lowerCAmelCase  :							Tuple				     =					TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

														# extract kpis
														_lowerCAmelCase  :							Optional[int]				     =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
														_lowerCAmelCase  :							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
														_lowerCAmelCase  :							Dict				     =					(
														    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},    __a)





def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations(_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item						) for item in array						)

							return count_of_possible_combinations(_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations_with_dp_array(
							    _lowerCamelCase       ,				_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														_lowerCAmelCase  :							Optional[int]				     =					sum(
														    count_of_possible_combinations_with_dp_array(target - item       ,				_lowerCamelCase						)
														    for item in array						)
														_lowerCAmelCase  :							Any				     =					answer
														return answer

							_lowerCAmelCase  :							List[Any]				     =					[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_lowerCamelCase       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					[0] * (target + 1)
							_lowerCAmelCase  :							List[str]				     =					1

							for i in range(1       ,				target + 1						):
														for j in range(_lowerCamelCase						):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						_snake_case	=						3
						_snake_case	=						5
						_snake_case	=						[1, 2, 5]
						print(combination_sum_iv(n, array, target))
