
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      A__							(     unittest.TestCase	):






				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''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					:      Optional[Any]    ,     a					:      int=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					:      Dict    ,     a					:      Dict   ):






							'''simple docstring'''







							TrainingJobAnalytics(a   ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.create_estimator()

							# run training
							estimator.fit()

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

							# extract kpis
							lowerCAmelCase__      :      Dict         =		list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']   )
							lowerCAmelCase__      :      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__      :      Optional[int]         =		(
							    Session().describe_training_job(estimator.latest_training_job.name   ).get('TrainingTimeInSeconds'    ,     999_999   )
							)

							# 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   )

class      A__							:






				def __init__(						self					:      Optional[Any]    ,     a					:      list   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		set_counts
							lowerCAmelCase__      :      str         =		max(a   )
							lowerCAmelCase__      :      Any         =		len(a   )
							lowerCAmelCase__      :      List[str]         =		[1] * num_sets
							lowerCAmelCase__      :      Dict         =		list(range(a   )   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.get_parent(a   )
							lowerCAmelCase__      :      Tuple         =		self.get_parent(a   )

							if src_parent == dst_parent:
										return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
										self.set_counts[dst_parent] += self.set_counts[src_parent]
										lowerCAmelCase__      :      Tuple         =		0
										lowerCAmelCase__      :      str         =		dst_parent
										if self.ranks[dst_parent] == self.ranks[src_parent]:
													self.ranks[dst_parent] += 1
										lowerCAmelCase__      :      List[Any]         =		self.set_counts[dst_parent]
							else:
										self.set_counts[src_parent] += self.set_counts[dst_parent]
										lowerCAmelCase__      :      Optional[int]         =		0
										lowerCAmelCase__      :      Tuple         =		src_parent
										lowerCAmelCase__      :      Optional[int]         =		self.set_counts[src_parent]

							lowerCAmelCase__      :      Optional[Any]         =		max(self.max_set    ,     a   )
							return True






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      int   ):






							'''simple docstring'''







							if self.parents[disj_set] == disj_set:
										return disj_set
							lowerCAmelCase__      :      Tuple         =		self.get_parent(self.parents[disj_set]   )
							return self.parents[disj_set]