


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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]    )
class   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     Union[str, 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=lowerCamelCase_      ,							)
            assert hasattr(self      ,							"""env"""    )

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Union[str, Any]    ):
            a_    :     List[str]										=    F'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
            # distributed data settings
            a_    :     str										=    {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

            # 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=lowerCamelCase_      ,							instance_count=lowerCamelCase_      ,							instance_type=self.instance_type      ,							debugger_hook_config=lowerCamelCase_      ,							hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,							metric_definitions=self.env.metric_definitions      ,							distribution=lowerCamelCase_      ,							py_version="""py36"""      ,							)

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Optional[int]    ):
            TrainingJobAnalytics(lowerCamelCase_    ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''    )

       @parameterized.expand([(2,)]    )
       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     List[Any]    ):
            # create estimator
            a_    :     Optional[int]										=    self.create_estimator(lowerCamelCase_    )

            # run training
            estimator.fit()

            # result dataframe
            a_    :     Optional[Any]										=    TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

            # extract kpis
            a_    :     Optional[int]										=    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]    )
            a_    :     Optional[Any]										=    list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]    )
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            a_    :     str										=    (
                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}      ,							lowerCamelCase_    )







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def  _a			(						__UpperCamelCase ):
     a_    :     int										=    int(number**0.5 )
     return number == sq * sq



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     int										=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     a_    :     int										=    x_den * y_den * z_den
     a_    :     int										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
     top //= hcf
     bottom //= hcf
     return top, bottom



def  _a			(						__UpperCamelCase = 3_5 ):
     a_    :     set										=    set()
     a_    :     int
     a_    :     Fraction										=    Fraction(0 )
     a_    :     tuple[int, int]

     for x_num in range(1  ,  order + 1 ):
          for x_den in range(x_num + 1  ,  order + 1 ):
               for y_num in range(1  ,  order + 1 ):
                    for y_den in range(y_num + 1  ,  order + 1 ):
                         # n=1
                         a_    :     int										=    x_num * y_den + x_den * y_num
                         a_    :     Optional[int]										=    x_den * y_den
                         a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Optional[int]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

                         # n=2
                         a_    :     List[Any]										=    (
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         a_    :     str										=    x_den * x_den * y_den * y_den
                         if is_sq(__UpperCamelCase ) and is_sq(__UpperCamelCase ):
                              a_    :     Optional[Any]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Optional[int]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   a_    :     str										=    add_three(
                                       __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                                   unique_s.add(__UpperCamelCase )

                    # n=-1
                         a_    :     Union[str, Any]										=    x_num * y_num
                         a_    :     Tuple										=    x_den * y_num + x_num * y_den
                         a_    :     List[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Union[str, Any]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

                         # n=2
                         a_    :     Union[str, Any]										=    x_num * x_num * y_num * y_num
                         a_    :     Dict										=    (
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(__UpperCamelCase ) and is_sq(__UpperCamelCase ):
                              a_    :     Dict										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Union[str, Any]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Dict										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   a_    :     List[Any]										=    add_three(
                                       __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                                   unique_s.add(__UpperCamelCase )

     for num, den in unique_s:
          total += Fraction(__UpperCamelCase  ,  __UpperCamelCase )

     return total.denominator + total.numerator


if __name__ == "__main__":
       print(f'''{solution() = }''')



