

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        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''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=snake_case_ ,   )
             assert hasattr(self ,   '''env'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    ={
                 '''enabled''': True,
                 '''processes_per_host''': 8,
             }
             __A    ={
                 '''enabled''': True,
                 '''parameters''': {
                     '''microbatches''': 4,
                     '''placement_strategy''': '''spread''',
                     '''pipeline''': '''interleaved''',
                     '''optimize''': '''speed''',
                     '''partitions''': 4,
                     '''ddp''': True,
                 },
             }

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

             __A    ='''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=snake_case_ ,   instance_type=self.instance_type ,   debugger_hook_config=snake_case_ ,   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=snake_case_ ,   py_version='''py36''' ,   )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             TrainingJobAnalytics(snake_case_     ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''     )


       @parameterized.expand([(1,)]     )
       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.create_estimator(snake_case_     )

             # run training
             estimator.fit()

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

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






from __future__ import annotations
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = order

								# a_{0} ... a_{k}
								__lowercase			       = [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								__lowercase			       = [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								__lowercase			       = [0.0] * self.order
								# y[n-1] ... y[n-k]
								__lowercase			       = [0.0] * self.order






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								if len(snake_case_    ) < self.order:
											__lowercase			       = [1.0, *a_coeffs]

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected a_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected b_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								__lowercase			       = a_coeffs
								__lowercase			       = b_coeffs






					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								__lowercase			       = 0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1			,							self.order + 1    ):
											result += (
											    self.b_coeffs[i] * self.input_history[i - 1]
											    - self.a_coeffs[i] * self.output_history[i - 1]
											)

								__lowercase			       = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								__lowercase			       = self.input_history[:-1]
								__lowercase			       = self.output_history[:-1]

								__lowercase			       = sample
								__lowercase			       = result

								return result


