




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""": 1_6_0_0, """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""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]     )
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      List[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=UpperCamelCase__     ,  )
           assert hasattr(self     ,  "env"     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Dict:
           # configuration for running training on smdistributed Model Parallel
           lowerCamelCase   :      Any			 =					{
               "enabled": True,
               "processes_per_host": 8,
           }
           lowerCamelCase   :      Union[str, Any]			 =					{
               "enabled": True,
               "parameters": {
                   "microbatches": 4,
                   "placement_strategy": "spread",
                   "pipeline": "interleaved",
                   "optimize": "speed",
                   "partitions": 4,
                   "ddp": True,
               },
           }

           lowerCamelCase   :      List[Any]			 =					{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

           lowerCamelCase   :      Tuple			 =					"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=UpperCamelCase__     ,  instance_type=self.instance_type     ,  debugger_hook_config=UpperCamelCase__     ,  hyperparameters={
                   **self.env.hyperparameters,
                   "model_name_or_path": self.model_name_or_path,
                   "max_steps": 500,
               }     ,  metric_definitions=self.env.metric_definitions     ,  distribution=UpperCamelCase__     ,  py_version="py36"     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[Any]:
           TrainingJobAnalytics(UpperCamelCase__     ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''     )







    @parameterized.expand([(1,)]     )
    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           # create estimator
           lowerCamelCase   :      Optional[Any]			 =					self.create_estimator(UpperCamelCase__     )

           # run training
           estimator.fit()

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

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










def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					current_set.copy()
  for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					row[0]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      if magnitude == 0:
        A_      :				Union[str, Any]		     =					column
        continue
      A_      :				Dict		     =					column / magnitude
    # Subtract to cancel term
  A_      :				Union[str, Any]		     =					current_set[0]
  A_      :				Tuple		     =					[first_row]
  A_      :				int		     =					current_set[1::]
  for row in current_set:
    A_      :				Tuple		     =					[]
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(SCREAMING_SNAKE_CASE       )
      continue
    for column_index in range(len(SCREAMING_SNAKE_CASE       )       ):
      temp_row.append(first_row[column_index] - row[column_index]       )
    final_set.append(SCREAMING_SNAKE_CASE       )
  # Create next recursion iteration set
  if len(final_set[0]       ) != 3:
    A_      :				Optional[Any]		     =					final_set[0]
    A_      :				Any		     =					[]
    A_      :				str		     =					[]
    for row in final_set[1::]:
      current_first_column.append(row[0]       )
      next_iteration.append(row[1::]       )
    A_      :				Optional[Any]		     =					simplify(SCREAMING_SNAKE_CASE       )
    for i in range(len(SCREAMING_SNAKE_CASE       )       ):
      resultant[i].insert(0 ,			current_first_column[i]       )
    resultant.insert(0 ,			SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					resultant
  return final_set


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if len(SCREAMING_SNAKE_CASE       ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  A_      :				str		     =					len(SCREAMING_SNAKE_CASE       ) + 1
  if any(len(SCREAMING_SNAKE_CASE       ) != _length for item in equations       ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  for row in equations:
    if any(not isinstance(SCREAMING_SNAKE_CASE ,			(int, float)       ) for column in row       ):
      raise ValueError('''solve_simultaneous() requires lists of integers'''       )
  if len(SCREAMING_SNAKE_CASE       ) == 1:
    return [equations[0][-1] / equations[0][0]]
  A_      :				Dict		     =					equations.copy()
  if any(0 in row for row in data_set       ):
    A_      :				Tuple		     =					data_set.copy()
    A_      :				Optional[Any]		     =					[]
    for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
      if 0 not in row:
        A_      :				str		     =					data_set.pop(SCREAMING_SNAKE_CASE       )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation'''       )
    data_set.insert(0 ,			SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					data_set.copy()
  A_      :				Dict		     =					simplify(SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					simplified[::-1]
  A_      :				list		     =					[]
  for row in simplified:
    A_      :				Union[str, Any]		     =					row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0       )
        continue
      solutions.append(current_solution / row[-2]       )
      continue
    A_      :				Optional[Any]		     =					row.copy()[: len(SCREAMING_SNAKE_CASE       ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0       )
    if len(SCREAMING_SNAKE_CASE       ) == 0:
      solutions.append(0       )
      continue
    A_      :				int		     =					temp_row[1::]
    A_      :				int		     =					temp_row[::-1]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      current_solution -= column * solutions[column_index]
    solutions.append(SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					[]
  for item in solutions:
    final.append(float(round(SCREAMING_SNAKE_CASE ,			5       )       )       )
  return final[::-1]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     UpperCamelCase								=	[
         [2, 1, 1, 1, 1, 4],
         [1, 2, 1, 1, 1, 5],
         [1, 1, 2, 1, 1, 6],
         [1, 1, 1, 2, 1, 7],
         [1, 1, 1, 1, 2, 8],
     ]
     print(solve_simultaneous(eq))
     print(solve_simultaneous([[4, 2]]))




