






"""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.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 6_50, "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": 6_00, "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": 6_00, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]							)
class     __lowerCAmelCase (      unittest.TestCase							):







  '''simple docstring'''


  def        UpperCamelCase__ (   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        UpperCamelCase__ (   self:		str					,      UpperCamelCase_:		List[Any]       ):
     UpperCamelCase_			     =f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
     # distributed data settings
     UpperCamelCase_			     ={"""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=UpperCamelCase_					,      instance_count=UpperCamelCase_					,      instance_type=self.instance_type					,      debugger_hook_config=UpperCamelCase_					,      hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}					,      metric_definitions=self.env.metric_definitions					,      distribution=UpperCamelCase_					,      py_version="py36"					,      )
  def        UpperCamelCase__ (   self:		int					,      UpperCamelCase_:		List[Any]       ):
     TrainingJobAnalytics(UpperCamelCase_       ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""       )





  @parameterized.expand([(2,)]       )
  def        UpperCamelCase__ (   self:		Any					,      UpperCamelCase_:		str       ):
     # create estimator
     UpperCamelCase_			     =self.create_estimator(UpperCamelCase_       )

     # run training
     estimator.fit()

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

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




"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      # Input as list
      _lowerCamelCase	:					Any   =		list(poly_a or [0])[:]
      _lowerCamelCase	:					Optional[Any]   =		list(poly_b or [0])[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
         self.polyA.pop()
      _lowerCamelCase	:					int   =		len(self.polyA)

      while self.polyB[-1] == 0:
         self.polyB.pop()
      _lowerCamelCase	:					Union[str, Any]   =		len(self.polyB)

      # Add 0 to make lengths equal a power of 2
      _lowerCamelCase	:					List[Any]   =		int(
          2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

      while len(self.polyA) < self.c_max_length:
         self.polyA.append(0)
      while len(self.polyB) < self.c_max_length:
         self.polyB.append(0)
      # A complex root used for the fourier transform
      _lowerCamelCase	:					Optional[Any]   =		complex(mpmath.root(x=1      ,   n=self.c_max_length      ,   k=1))

      # The product
      _lowerCamelCase	:					int   =		self.__multiply()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
      # Corner case
      if len(SCREAMING_SNAKE_CASE) <= 1:
         return dft[0]
      #
      _lowerCamelCase	:					str   =		self.c_max_length // 2
      while next_ncol > 0:
         _lowerCamelCase	:					Dict   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					Tuple   =		self.root**next_ncol

         # First half of next step
         _lowerCamelCase	:					int   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
            current_root *= root
         # Second half of next step
         _lowerCamelCase	:					Optional[int]   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
            current_root *= root
         # Update
         _lowerCamelCase	:					Union[str, Any]   =		new_dft
         _lowerCamelCase	:					List[str]   =		next_ncol // 2
      return dft[0]





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		self.__dft("""A""")
      _lowerCamelCase	:					List[str]   =		self.__dft("""B""")
      _lowerCamelCase	:					List[Any]   =		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
      del dft_a
      del dft_b

      # Corner Case
      if len(inverce_c[0]) <= 1:
         return inverce_c[0]
      # Inverse DFT
      _lowerCamelCase	:					List[str]   =		2
      while next_ncol <= self.c_max_length:
         _lowerCamelCase	:					Any   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					List[Any]   =		self.root ** (next_ncol // 2)
         _lowerCamelCase	:					str   =		1
         # First half of next step
         for j in range(self.c_max_length // next_ncol):
            for i in range(next_ncol // 2):
               # Even positions
               new_inverse_c[i].append(
                   (
                       inverce_c[i][j]
                       + inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / 2)
               # Odd positions
               new_inverse_c[i + next_ncol // 2].append(
                   (
                       inverce_c[i][j]
                       - inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / (2 * current_root))
            current_root *= root
         # Update
         _lowerCamelCase	:					Any   =		new_inverse_c
         next_ncol *= 2
      # Unpack
      _lowerCamelCase	:					Optional[Any]   =		[round(x[0].real      ,   8) + round(x[0].imag      ,   8) * 1j for x in inverce_c]

      # Remove leading 0's
      while inverce_c[-1] == 0:
         inverce_c.pop()
      return inverce_c





   def __str__(							self)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""A = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
      _lowerCamelCase	:					List[Any]   =		"""B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
      _lowerCamelCase	:					int   =		"""A*B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.product))

      return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()

