





"""simple docstring"""





from __future__ import annotations

from collections import deque




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Optional[int]      ,    _A							:							List[Any]):



											"""simple docstring"""







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

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





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							List[str]      ,    _A							:							Optional[int]):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    0
											for character in keyword:
																		_SCREAMING_SNAKE_CASE		:						int			  =    self.find_next_state(A__      ,    A__)
																		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		:						List[Any]			  =    len(self.adlist) - 1
																		else:
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    next_state
											self.adlist[current_state]["output"].append(A__)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple):



											"""simple docstring"""







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


if __name__ == "__main__":
			import doctest

			doctest.testmod()



"""simple docstring"""





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge",
            "results": {"train_runtime": 6_50, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_00, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											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							:							Union[str, Any]      ,    _A							:							str=1):



											"""simple docstring"""







											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=f"""{self.env.base_job_name}-single"""      ,    instance_count=_A      ,    instance_type=self.instance_type      ,    debugger_hook_config=_A      ,    hyperparameters={**self.env.hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,    metric_definitions=self.env.metric_definitions      ,    py_version="""py36"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											TrainingJobAnalytics(_A).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""")





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.create_estimator()

											# run training
											estimator.fit()

											# result dataframe
											_SCREAMING_SNAKE_CASE		:						Any			  =    TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

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


