


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_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 16_00, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 16_00, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]				)
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Tuple   ):
       # configuration for running training on smdistributed Model Parallel
       snake_case__						= {
           '''enabled''': True,
           '''processes_per_host''': 8,
       }
       snake_case__						= {
           '''enabled''': True,
           '''parameters''': {
               '''microbatches''': 4,
               '''placement_strategy''': '''spread''',
               '''pipeline''': '''interleaved''',
               '''optimize''': '''speed''',
               '''partitions''': 4,
               '''ddp''': True,
           },
       }

       snake_case__						= {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

       snake_case__						= '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
       # 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=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""     ,		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,
               '''max_steps''': 5_00,
           }     ,		metric_definitions=self.env.metric_definitions     ,		distribution=_a     ,		py_version='''py36'''     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:int   ):
       TrainingJobAnalytics(_a   ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""   )







     @parameterized.expand([(1,)]   )
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Optional[Any]   ):
       # create estimator
       snake_case__						= self.create_estimator(_a   )

       # run training
       estimator.fit()

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

       # extract kpis
       snake_case__						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']   )
       snake_case__						= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']   )
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       snake_case__						= (
           Session().describe_training_job(estimator.latest_training_job.name   ).get('''TrainingTimeInSeconds'''     ,		99_99_99   )
       )

       # 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   )





from copy import deepcopy
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:int     ,		_a:list[int] | None = None     ,		_a:int | None = None   ):

       if arr is None and size is not None:
         snake_case__						= size
         snake_case__						= [0] * size
       elif arr is not None:
         self.init(_a   )
       else:
         raise ValueError('''Either arr or size must be specified'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:list[int]   ):
       snake_case__						= len(_a   )
       snake_case__						= deepcopy(_a   )
       for i in range(1     ,		self.size   ):
         snake_case__						= self.next_(_a   )
         if j < self.size:
           self.tree[j] += self.tree[i]

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.tree[:]
       for i in range(self.size - 1     ,		0     ,		-1   ):
         snake_case__						= self.next_(_a   )
         if j < self.size:
           arr[j] -= arr[i]
       return arr

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return index + (index & (-index))

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return index - (index & (-index))

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int     ,		_a:int   ):
       if index == 0:
         self.tree[0] += value
         return
       while index < self.size:
         self.tree[index] += value
         snake_case__						= self.next_(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:int   ):
       self.add(_a     ,		value - self.get(_a   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int   ):
       if right == 0:
         return 0
       snake_case__						= self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
         result += self.tree[right]
         snake_case__						= self.prev(_a   )
       return result

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:int   ):
       return self.prefix(_a   ) - self.prefix(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int   ):
       return self.query(_a     ,		index + 1   )







     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int   ):
       value -= self.tree[0]
       if value < 0:
         return -1

       snake_case__						= 1  # Largest power of 2 <= size
       while j * 2 < self.size:
         j *= 2

       snake_case__						= 0

       while j > 0:
         if i + j < self.size and self.tree[i + j] <= value:
           value -= self.tree[i + j]
           i += j
         j //= 2
       return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()

