





'''simple docstring'''




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 UpperCAmelCase				(		unittest.TestCase				):



    def        __lowerCamelCase    (	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=_snake_case  ,      )
     assert hasattr(self  ,      'env'       )



    def        __lowerCamelCase    (	self  ,      __A       ):
     __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=_snake_case  ,      instance_count=_snake_case  ,      instance_type=self.instance_type  ,      debugger_hook_config=_snake_case  ,      hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}  ,      metric_definitions=self.env.metric_definitions  ,      distribution=_snake_case  ,      py_version='py36'  ,      )



    def        __lowerCamelCase    (	self  ,      __A       ):
     TrainingJobAnalytics(_snake_case       ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'       )




    @parameterized.expand([(2,)]       )
    def        __lowerCamelCase    (	self  ,      __A       ):
     __UpperCAmelCase       = self.create_estimator(_snake_case       )

     # 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'  ,      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}  ,      _snake_case       )










from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 90 counterclockwise:\n''')
							print_matrix(rotate_aa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 180:\n''')
							print_matrix(rotate_aaa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 270 counterclockwise:\n''')
							print_matrix(rotate_aaa(matrix))




