



'''simple docstring'''




from __future__ import annotations

from collections import deque






class 						_UpperCamelCase  :


			'''simple docstring'''

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		list[dict]           =		[]
					self.adlist.append(
					    {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}						)

					for keyword in keywords:
							self.add_keyword(lowerCAmelCase__						)
					self.set_fail_transitions()

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : int							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					for state in self.adlist[current_state]["next_states"]:
							if char == self.adlist[state]["value"]:
									return state
					return None

			def UpperCamelCase__					(							self     : int							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		0
					for character in keyword:
							__SCREAMING_SNAKE_CASE       :		str           =		self.find_next_state(lowerCAmelCase__							,     lowerCAmelCase__						)
							if next_state is None:
									self.adlist.append(
									    {
									        """value""": character,
									        """next_states""": [],
									        """fail_state""": 0,
									        """output""": [],
									    }						)
									self.adlist[current_state]["next_states"].append(len(self.adlist						) - 1						)
									__SCREAMING_SNAKE_CASE       :		int           =		len(self.adlist						) - 1
							else:
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		next_state
					self.adlist[current_state]["output"].append(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		deque           =		deque()
					for node in self.adlist[0]["next_states"]:
							q.append(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		List[str]           =		0
					while q:
							__SCREAMING_SNAKE_CASE       :		str           =		q.popleft()
							for child in self.adlist[r]["next_states"]:
									q.append(lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.adlist[r]["fail_state"]
									while (
									    self.find_next_state(lowerCAmelCase__							,     self.adlist[child]["""value"""]						) is None
									    and state != 0
									):
											__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.adlist[state]["fail_state"]
									__SCREAMING_SNAKE_CASE       :		Dict           =		self.find_next_state(
									    lowerCAmelCase__							,     self.adlist[child]["""value"""]						)
									if self.adlist[child]["fail_state"] is None:
											__SCREAMING_SNAKE_CASE       :		Tuple           =		0
									__SCREAMING_SNAKE_CASE       :		Tuple           =		(
									    self.adlist[child]["output"]
									    + self.adlist[self.adlist[child]["fail_state"]]["output"]
									)



			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		dict           =		{}  # returns a dict with keywords and list of its occurrences
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		0
					for i in range(len(lowerCAmelCase__						)						):
							while (
							    self.find_next_state(lowerCAmelCase__							,     string[i]						) is None
							    and current_state != 0
							):
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.adlist[current_state]["fail_state"]
							__SCREAMING_SNAKE_CASE       :		List[str]           =		self.find_next_state(lowerCAmelCase__							,     string[i]						)
							if next_state is None:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		0
							else:
									__SCREAMING_SNAKE_CASE       :		Dict           =		next_state
									for key in self.adlist[current_state]["output"]:
											if key not in result:
													__SCREAMING_SNAKE_CASE       :		Any           =		[]
											result[key].append(i - len(lowerCAmelCase__						) + 1						)
					return result


if __name__ == "__main__":
						import doctest

						doctest.testmod()

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  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:List[Any]						)		-> 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=a     ,   )
								assert hasattr(self     ,   "env"						)







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







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Dict						)		-> Optional[int]:
								TrainingJobAnalytics(a						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)







	@parameterized.expand([(2,)]						)
	def    _lowerCamelCase				(					self		:Dict     ,   a		:Tuple						)		-> List[Any]:
								# create estimator
								__UpperCamelCase	:   int			   =					self.create_estimator(a						)

								# run training
								estimator.fit()

								# result dataframe
								__UpperCamelCase	:   Optional[int]			   =					TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

								# extract kpis
								__UpperCamelCase	:   Optional[Any]			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
								__UpperCamelCase	:   Tuple			   =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								__UpperCamelCase	:   int			   =					(
								    Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"     ,   9_9_9_9_9_9						)
								)

								# 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						)