






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""": 1600, """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""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]    )
class   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     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=_SCREAMING_SNAKE_CASE      ,							)
            assert hasattr(self      ,							"""env"""    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     int										=    {
                """enabled""": True,
                """processes_per_host""": 8,
            }
            a_    :     Union[str, Any]										=    {
                """enabled""": True,
                """parameters""": {
                    """microbatches""": 4,
                    """placement_strategy""": """spread""",
                    """pipeline""": """interleaved""",
                    """optimize""": """speed""",
                    """partitions""": 4,
                    """ddp""": True,
                },
            }

            a_    :     List[str]										=    {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

            a_    :     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=_SCREAMING_SNAKE_CASE      ,							instance_type=self.instance_type      ,							debugger_hook_config=_SCREAMING_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=_SCREAMING_SNAKE_CASE      ,							py_version="""py36"""      ,							)

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

       @parameterized.expand([(1,)]    )
       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     str										=    self.create_estimator(_SCREAMING_SNAKE_CASE    )

            # run training
            estimator.fit()

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

            # extract kpis
            a_    :     str										=    list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]    )
            a_    :     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_    :     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}      ,							_SCREAMING_SNAKE_CASE    )







import numpy as np

__lowerCamelCase        =     [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]


class   a__	:

       def __init__(   self      :     Union[str, Any]    ):
            a_    :     List[Any]										=    np.array(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     str    ):
            a_			,			a_    :     Optional[int]										=    np.where(letter == self.SQUARE    )
            a_    :     Union[str, Any]										=    np.concatenate([indexa + 1, indexa + 1]    )
            return indexes

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     int    ):
            a_    :     str										=    self.SQUARE[indexa - 1, indexa - 1]
            return letter

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     str    ):
            a_    :     List[str]										=    message.lower()
            a_    :     str										=    message.replace(""" """      ,							""""""    )
            a_    :     Union[str, Any]										=    message.replace("""j"""      ,							"""i"""    )

            a_    :     Optional[Any]										=    np.empty((2, len(lowerCamelCase_    ))    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     int										=    self.letter_to_numbers(message[letter_index]    )

                 a_    :     str										=    numbers[0]
                 a_    :     int										=    numbers[1]

            a_    :     int										=    first_step.reshape(2 * len(lowerCamelCase_    )    )
            a_    :     Optional[Any]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Optional[Any]										=    int(second_step[numbers_index * 2]    )
                 a_    :     Optional[Any]										=    int(second_step[(numbers_index * 2) + 1]    )
                 a_    :     Optional[Any]										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     List[str]										=    encoded_message + letter

            return encoded_message

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     str    ):
            a_    :     Optional[Any]										=    message.lower()
            message.replace(""" """      ,							""""""    )
            a_    :     int										=    np.empty(2 * len(lowerCamelCase_    )    )
            for letter_index in range(len(lowerCamelCase_    )    ):
                 a_    :     str										=    self.letter_to_numbers(message[letter_index]    )
                 a_    :     Optional[int]										=    numbers[0]
                 a_    :     Optional[Any]										=    numbers[1]

            a_    :     Tuple										=    first_step.reshape((2, len(lowerCamelCase_    ))    )
            a_    :     Optional[int]										=    """"""
            for numbers_index in range(len(lowerCamelCase_    )    ):
                 a_    :     Dict										=    int(second_step[0, numbers_index]    )
                 a_    :     Tuple										=    int(second_step[1, numbers_index]    )
                 a_    :     Tuple										=    self.numbers_to_letter(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    decoded_message + letter

            return decoded_message



