





import mpmath  # for roots of unity
import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__=None   ,				a__=None):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		list(poly_a or [0])[:]
             _lowerCamelCase				:  Union[str, Any]						=		list(poly_b or [0])[:]

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

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

             # Add 0 to make lengths equal a power of 2
             _lowerCamelCase				:  Dict						=		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				:  int						=		complex(mpmath.root(x=1   ,				n=self.c_max_length   ,				k=1))

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


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
             # Corner case
             if len(a__) <= 1:
                    return dft[0]
             #
             _lowerCamelCase				:  Optional[Any]						=		self.c_max_length // 2
             while next_ncol > 0:
                    _lowerCamelCase				:  Optional[Any]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  List[str]						=		self.root**next_ncol

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


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.__dft('''A''')
             _lowerCamelCase				:  Optional[Any]						=		self.__dft('''B''')
             _lowerCamelCase				:  List[str]						=		[[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				:  str						=		2
             while next_ncol <= self.c_max_length:
                    _lowerCamelCase				:  Optional[int]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  Dict						=		self.root ** (next_ncol // 2)
                    _lowerCamelCase				:  List[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[int]						=		[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):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		'''A = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]))
             _lowerCamelCase				:  List[str]						=		'''B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]))
             _lowerCamelCase				:  Tuple						=		'''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()








import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]	)
class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def 					__snake_case      (		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=a__   ,				)
             assert hasattr(self   ,				'''env''')


      def 					__snake_case      (		self   ,				a__=1):



             """simple docstring"""





             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}-single"""   ,				instance_count=a__   ,				instance_type=self.instance_type   ,				debugger_hook_config=a__   ,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}   ,				metric_definitions=self.env.metric_definitions   ,				py_version='''py36'''   ,				)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             TrainingJobAnalytics(a__).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.create_estimator()

             # run training
             estimator.fit()

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

             # extract kpis
             _lowerCamelCase				:  Tuple						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
             _lowerCamelCase				:  Dict						=		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
             # get train time from SageMaker job, this includes starting, preprocessing, stopping
             _lowerCamelCase				:  Optional[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}   ,				a__)

