


'''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': 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 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   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=lowerCamelCase			,     )
          assert hasattr(self			,     "env"		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          TrainingJobAnalytics(lowerCamelCase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)


     @parameterized.expand([(1,)]		)
     def lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.create_estimator(lowerCamelCase		)

          # run training
          estimator.fit()

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

          # extract kpis
          __A	:       Any      =							list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
          __A	:       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
          __A	:       Union[str, 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}			,     lowerCamelCase		)





'''simple docstring'''


# Function to print upper half of diamond (pyramid)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(0       ,					SCREAMING_SNAKE_CASE							):
          for _ in range(0       ,					n - i - 1							):  # printing spaces
               print(" "       ,					end=""							)
          for _ in range(0       ,					i + 1							):  # printing stars
               print("* "       ,					end=""							)
          print()


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):
          for _ in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):  # printing stars
               print("* "       ,					end=""							)
          print()
          for _ in range(n - i + 1       ,					0       ,					-1							):  # printing spaces
               print(" "       ,					end=""							)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if n <= 0:
          print("       ...       ....        nothing printing :("							)
          return
     floyd(SCREAMING_SNAKE_CASE							)  # upper half
     reverse_floyd(SCREAMING_SNAKE_CASE							)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      _UpperCamelCase 							=  1
      while K:
            _UpperCamelCase 							=  int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            _UpperCamelCase 							=  int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")

