



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""": 6_5_0, """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""": 6_0_0, """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""": 6_0_0, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ])
class 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  Union[str, Any]  ):
							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=__lowercase,   )
							assert hasattr(self,   "env"  )




				def 							A__ (  self   :  List[Any],   __lowercase   :  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=__lowercase,   instance_count=__lowercase,   instance_type=self.instance_type,   debugger_hook_config=__lowercase,   hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path},   metric_definitions=self.env.metric_definitions,   distribution=__lowercase,   py_version="py36",   )




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Optional[Any]  ):
							TrainingJobAnalytics(__lowercase  ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''  )




				@parameterized.expand([(2,)]  )
				def 							A__ (  self   :  Tuple,   __lowercase   :  List[Any]  ):
							# create estimator
							lowercase__   					=			self.create_estimator(__lowercase  )

							# 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",   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},   __lowercase  )









from __future__ import annotations

from typing import TypedDict
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   str
				UpperCamelCase__			:   int



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						raise TypeError("The parameter s type must be str."					)
			if not s:
						raise ValueError("The parameter s must not be empty."					)

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			if not isinstance(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
						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:
						lowercase__   					=			int(SCREAMING_SNAKE_CASE_					)
			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(SCREAMING_SNAKE_CASE_					):
						raise ValueError(
						    "The parameter idx_original_string must be lower than" " len(bwt_string)."					)

			lowercase__   					=			[""] * len(SCREAMING_SNAKE_CASE_					)
			for _ in range(len(SCREAMING_SNAKE_CASE_					)					):
						for i in range(len(SCREAMING_SNAKE_CASE_					)					):
									lowercase__   					=			bwt_string[i] + ordered_rotations[i]
						ordered_rotations.sort()
			return ordered_rotations[idx_original_string]


if __name__ == "__main__":
							lowercase_						=       """Provide a string that I will generate its BWT transform: """
							lowercase_						=       input(entry_msg).strip()
							lowercase_						=       bwt_transform(s)
							print(
							    F'Burrows Wheeler transform for string \'{s}\' results '
							    F'in \'{result["bwt_string"]}\''
							)
							lowercase_						=       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}\''
							)




