



'''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 					snake_case			(unittest.TestCase ):



       def 				_a   (					self   )	->				Tuple:
           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=UpperCAmelCase_  ,)
           assert hasattr(self  ,"env"   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           lowercase__									=    F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
           # distributed data settings
           lowercase__									=    {"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=UpperCAmelCase_  ,instance_count=UpperCAmelCase_  ,instance_type=self.instance_type  ,debugger_hook_config=UpperCAmelCase_  ,hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}  ,metric_definitions=self.env.metric_definitions  ,distribution=UpperCAmelCase_  ,py_version="py36"  ,)



       def 				_a   (					self  ,UpperCAmelCase_   )	->				str:
           TrainingJobAnalytics(UpperCAmelCase_   ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''   )







       @parameterized.expand([(2,)]   )
       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[Any]:
           # create estimator
           lowercase__									=    self.create_estimator(UpperCAmelCase_   )

           # run training
           estimator.fit()

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

           # extract kpis
           lowercase__									=    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]   )
           lowercase__									=    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]   )
           # get train time from SageMaker job, this includes starting, preprocessing, stopping
           lowercase__									=    (
               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}  ,UpperCAmelCase_   )









'''simple docstring'''





def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [list(range(1_000 - i  ,-1_000 - i  ,-1							)							) for i in range(1_000							)]


SCREAMING_SNAKE_CASE__          =  generate_large_matrix()
SCREAMING_SNAKE_CASE__          =  (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    assert all(row == sorted(_snake_case  ,reverse=_snake_case							) for row in grid							)
    assert all(list(_snake_case							) == sorted(_snake_case  ,reverse=_snake_case							) for col in zip(*_snake_case							)							)




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(_snake_case							) - 1

    # Edge cases such as no values or all numbers are negative.
    if not array or array[0] < 0:
        return 0

    while right + 1 > left:
        lowercase__									=    (left + right) // 2
        lowercase__									=    array[mid]

        # Num must be negative and the index must be greater than or equal to 0.
        if num < 0 and array[mid - 1] >= 0:
            return mid

        if num >= 0:
            lowercase__									=    mid + 1
        else:
            lowercase__									=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
    return len(_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(grid[0]							)

    for i in range(len(_snake_case							)							):
        lowercase__									=    find_negative_index(grid[i][:bound]							)
        total += bound
    return (len(_snake_case							) * len(grid[0]							)) - total




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    return len([number for row in grid for number in row if number < 0]							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    for row in grid:
        for i, number in enumerate(_snake_case							):
            if number < 0:
                total += len(_snake_case							) - i
                break
    return total




def 							lowerCamelCase	(					):





    '''simple docstring'''


    from timeit import timeit

    print("Running benchmarks"							)
    lowercase__									=    (
        "from __main__ import count_negatives_binary_search, "
        "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
    )
    for func in (
        "count_negatives_binary_search",  # took 0.7727 seconds
        "count_negatives_brute_force_with_break",  # took 4.6505 seconds
        "count_negatives_brute_force",  # took 12.8160 seconds
    ):
        lowercase__									=    timeit(f'''{func}(grid=grid)'''  ,setup=_snake_case  ,number=500							)
        print(f'''{func}() took {time:0.4f} seconds'''							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()




