






'''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 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]       )      ->      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=__SCREAMING_SNAKE_CASE	,    )
							assert hasattr(self	,    '''env'''       )






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






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Any:
							TrainingJobAnalytics(__SCREAMING_SNAKE_CASE       ).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'       )







			@parameterized.expand([(2,)]       )
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      str:
							# create estimator
							a_		:	Dict									=   self.create_estimator(__SCREAMING_SNAKE_CASE       )

							# run training
							estimator.fit()

							# result dataframe
							a_		:	Union[str, Any]									=   TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

							# extract kpis
							a_		:	Optional[int]									=   list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']       )
							a_		:	Tuple									=   list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']       )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							a_		:	List[str]									=   (
							    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}	,    __SCREAMING_SNAKE_CASE       )












'''simple docstring'''







from __future__ import annotations

from typing import TypedDict





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					42
			snake_case__	=					42





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )

				return [s[i:] + s[:i] for i in range(len(__A   )   )]





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )
				if not s:
								raise ValueError('''The parameter s must not be empty.'''   )

				a_		:	str									=   all_rotations(__A   )
				rotations.sort()  # sort the list of rotations in alphabetically order
				# make a string composed of the last char of each rotation
				a_		:	BWTTransformDict									=   {
				    "bwt_string": "".join([word[-1] for word in rotations]   ),
				    "idx_original_string": rotations.index(__A   ),
				}
				return response





def 		_UpperCAmelCase (				__A				: str    ,		__A				: int   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter bwt_string type must be str.'''   )
				if not bwt_string:
								raise ValueError('''The parameter bwt_string must not be empty.'''   )
				try:
								a_		:	Union[str, Any]									=   int(__A   )
				except ValueError:
								raise TypeError(
								    '''The parameter idx_original_string type must be int or passive'''
								    ''' of cast to int.'''   )
				if idx_original_string < 0:
								raise ValueError('''The parameter idx_original_string must not be lower than 0.'''   )
				if idx_original_string >= len(__A   ):
								raise ValueError(
								    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''   )

				a_		:	Union[str, Any]									=   [''''''] * len(__A   )
				for _ in range(len(__A   )   ):
								for i in range(len(__A   )   ):
												a_		:	int									=   bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


if __name__ == "__main__":
		__lowerCAmelCase									=   'Provide a string that I will generate its BWT transform: '
		__lowerCAmelCase									=   input(entry_msg).strip()
		__lowerCAmelCase									=   bwt_transform(s)
		print(
		    F"""Burrows Wheeler transform for string '{s}' results """
		    F"""in '{result['bwt_string']}'"""
		)
		__lowerCAmelCase									=   reverse_bwt(result['bwt_string'], result['idx_original_string'])
		print(
		    F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
		    F"""we get original string '{original_string}'"""
		)




