


"""simple docstring"""





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_600, '''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_600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]	)
class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    List[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=UpperCamelCase_    , )
           assert hasattr(self    , '''env''' )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    List[Any] ):
           # configuration for running training on smdistributed Model Parallel
           lowerCAmelCase    :		List[Any]				=      {
               '''enabled''': True,
               '''processes_per_host''': 8,
           }
           lowerCAmelCase    :		Optional[int]				=      {
               '''enabled''': True,
               '''parameters''': {
                   '''microbatches''': 4,
                   '''placement_strategy''': '''spread''',
                   '''pipeline''': '''interleaved''',
                   '''optimize''': '''speed''',
                   '''partitions''': 4,
                   '''ddp''': True,
               },
           }

           lowerCAmelCase    :		str				=      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

           lowerCAmelCase    :		Any				=      '''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''': 5_0_0,
               }    , metric_definitions=self.env.metric_definitions    , distribution=UpperCamelCase_    , py_version='''py36'''    , )






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    str ):
           TrainingJobAnalytics(UpperCamelCase_ ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' )






     @parameterized.expand([(1,)] )
     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Union[str, Any] ):
           # create estimator
           lowerCAmelCase    :		Any				=      self.create_estimator(UpperCamelCase_ )

           # run training
           estimator.fit()

           # result dataframe
           lowerCAmelCase    :		Tuple				=      TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()

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






"""simple docstring"""





from __future__ import annotations

from decimal import Decimal

from numpy import array


def    _snake_case  (  _snake_case :	list[list[float]]	):
      lowerCAmelCase    :		str				=      Decimal

      # Check if the provided matrix has 2 rows and 2 columns
      # since this implementation only works for 2x2 matrices
      if len(_snake_case	) == 2 and len(matrix[0]	) == 2 and len(matrix[1]	) == 2:
            # Calculate the determinant of the matrix
            lowerCAmelCase    :		int				=      float(
                d(matrix[0][0]	) * d(matrix[1][1]	) - d(matrix[1][0]	) * d(matrix[0][1]	)	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creates a copy of the matrix with swapped positions of the elements
            lowerCAmelCase    :		Optional[int]				=      [[0.0, 0.0], [0.0, 0.0]]
            lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      matrix[1][1], matrix[0][0]
            lowerCAmelCase,					lowerCAmelCase    :		Union[str, Any]				=      -matrix[1][0], -matrix[0][1]

            # Calculate the inverse of the matrix
            return [
                [(float(d(_snake_case	)	) / determinant) or 0.0 for n in row] for row in swapped_matrix
            ]
      elif (
          len(_snake_case	) == 3
          and len(matrix[0]	) == 3
          and len(matrix[1]	) == 3
          and len(matrix[2]	) == 3
      ):
            # Calculate the determinant of the matrix using Sarrus rule
            lowerCAmelCase    :		int				=      float(
                (
                    (d(matrix[0][0]	) * d(matrix[1][1]	) * d(matrix[2][2]	))
                    + (d(matrix[0][1]	) * d(matrix[1][2]	) * d(matrix[2][0]	))
                    + (d(matrix[0][2]	) * d(matrix[1][0]	) * d(matrix[2][1]	))
                )
                - (
                    (d(matrix[0][2]	) * d(matrix[1][1]	) * d(matrix[2][0]	))
                    + (d(matrix[0][1]	) * d(matrix[1][0]	) * d(matrix[2][2]	))
                    + (d(matrix[0][0]	) * d(matrix[1][2]	) * d(matrix[2][1]	))
                )	)
            if determinant == 0:
                  raise ValueError('''This matrix has no inverse.'''	)

            # Creating cofactor matrix
            lowerCAmelCase    :		Dict				=      [
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
                [d(0.0	), d(0.0	), d(0.0	)],
            ]
            lowerCAmelCase    :		List[str]				=      (d(matrix[1][1]	) * d(matrix[2][2]	)) - (
                d(matrix[1][2]	) * d(matrix[2][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[1][0]	) * d(matrix[2][2]	)) - (d(matrix[1][2]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		str				=      (d(matrix[1][0]	) * d(matrix[2][1]	)) - (
                d(matrix[1][1]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Any				=      -(
                (d(matrix[0][1]	) * d(matrix[2][2]	)) - (d(matrix[0][2]	) * d(matrix[2][1]	))
            )
            lowerCAmelCase    :		Any				=      (d(matrix[0][0]	) * d(matrix[2][2]	)) - (
                d(matrix[0][2]	) * d(matrix[2][0]	)
            )
            lowerCAmelCase    :		Optional[int]				=      -(
                (d(matrix[0][0]	) * d(matrix[2][1]	)) - (d(matrix[0][1]	) * d(matrix[2][0]	))
            )
            lowerCAmelCase    :		Optional[int]				=      (d(matrix[0][1]	) * d(matrix[1][2]	)) - (
                d(matrix[0][2]	) * d(matrix[1][1]	)
            )
            lowerCAmelCase    :		Dict				=      -(
                (d(matrix[0][0]	) * d(matrix[1][2]	)) - (d(matrix[0][2]	) * d(matrix[1][0]	))
            )
            lowerCAmelCase    :		List[Any]				=      (d(matrix[0][0]	) * d(matrix[1][1]	)) - (
                d(matrix[0][1]	) * d(matrix[1][0]	)
            )

            # Transpose the cofactor matrix (Adjoint matrix)
            lowerCAmelCase    :		str				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        lowerCAmelCase    :		Optional[Any]				=      cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
            lowerCAmelCase    :		Tuple				=      array(_snake_case	)
            for i in range(3	):
                  for j in range(3	):
                        inverse_matrix[i][j] /= d(_snake_case	)

        # Calculate the inverse of the matrix
            return [[float(d(_snake_case	)	) or 0.0 for n in row] for row in inverse_matrix]
      raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''	)


