from __future__ import annotations

from collections import deque





class      A							:


   def __init__(self     :						Dict       ,  __UpperCAmelCase     :						list[str]		)						->				Optional[int]:
         """simple docstring"""



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

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


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						str		)						->				int | None:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						str		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  0
         for character in keyword:
               UpperCAmelCase__								=  self.find_next_state(__UpperCAmelCase       ,  __UpperCAmelCase		)
               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		)
                     UpperCAmelCase__								=  len(self.adlist		) - 1
               else:
                     UpperCAmelCase__								=  next_state
         self.adlist[current_state]["output"].append(__UpperCAmelCase		)


   def 			lowercase_      (self     :						Optional[int]		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  deque()
         for node in self.adlist[0]["next_states"]:
               q.append(__UpperCAmelCase		)
               UpperCAmelCase__								=  0
         while q:
               UpperCAmelCase__								=  q.popleft()
               for child in self.adlist[r]["next_states"]:
                     q.append(__UpperCAmelCase		)
                     UpperCAmelCase__								=  self.adlist[r]["fail_state"]
                     while (
                         self.find_next_state(__UpperCAmelCase       ,  self.adlist[child]["value"]		) is None
                         and state != 0
                     ):
                           UpperCAmelCase__								=  self.adlist[state]["fail_state"]
                     UpperCAmelCase__								=  self.find_next_state(
                         __UpperCAmelCase       ,  self.adlist[child]["value"]		)
                     if self.adlist[child]["fail_state"] is None:
                           UpperCAmelCase__								=  0
                     UpperCAmelCase__								=  (
                         self.adlist[child]["output"]
                         + self.adlist[self.adlist[child]["fail_state"]]["output"]
                     )





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				dict[str, list[int]]:
         """simple docstring"""



         UpperCAmelCase__								=  {}  # returns a dict with keywords and list of its occurrences
         UpperCAmelCase__								=  0
         for i in range(len(__UpperCAmelCase		)		):
               while (
                   self.find_next_state(__UpperCAmelCase       ,  string[i]		) is None
                   and current_state != 0
               ):
                     UpperCAmelCase__								=  self.adlist[current_state]["fail_state"]
               UpperCAmelCase__								=  self.find_next_state(__UpperCAmelCase       ,  string[i]		)
               if next_state is None:
                     UpperCAmelCase__								=  0
               else:
                     UpperCAmelCase__								=  next_state
                     for key in self.adlist[current_state]["output"]:
                           if key not in result:
                                 UpperCAmelCase__								=  []
                           result[key].append(i - len(__UpperCAmelCase		) + 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_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      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """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=__UpperCAmelCase       ,  )
         assert hasattr(self       ,  "env"		)


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]=1		)						->				Dict:
         """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=__UpperCAmelCase       ,  instance_type=self.instance_type       ,  debugger_hook_config=__UpperCAmelCase       ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}       ,  metric_definitions=self.env.metric_definitions       ,  py_version="py36"       ,  )


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



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





   def 			lowercase_      (self     :						Any		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.create_estimator()

         # run training
         estimator.fit()

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

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



