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''': 1_6_0_0, '''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''': 1_6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]    )
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  List[Any]							):



    """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=_lowercase		,  )
    assert hasattr(self		,  '''env'''							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= {
        '''enabled''': True,
        '''processes_per_host''': 8,
    }
    _UpperCamelCase:     List[Any]							= {
        '''enabled''': True,
        '''parameters''': {
            '''microbatches''': 4,
            '''placement_strategy''': '''spread''',
            '''pipeline''': '''interleaved''',
            '''optimize''': '''speed''',
            '''partitions''': 4,
            '''ddp''': True,
        },
    }

    _UpperCamelCase:     Optional[int]							= {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

    _UpperCamelCase:     str							= '''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=_lowercase		,  instance_type=self.instance_type		,  debugger_hook_config=_lowercase		,  hyperparameters={
            **self.env.hyperparameters,
            '''model_name_or_path''': self.model_name_or_path,
            '''max_steps''': 500,
        }		,  metric_definitions=self.env.metric_definitions		,  distribution=_lowercase		,  py_version='''py36'''		,  )



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  Any							):



    """simple docstring"""



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



   @parameterized.expand([(1,)]							)
   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.create_estimator(_lowercase							)

    # run training
    estimator.fit()

    # result dataframe
    _UpperCamelCase:     Any							= TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

    # extract kpis
    _UpperCamelCase:     Dict							= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']							)
    _UpperCamelCase:     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
    _UpperCamelCase:     Any							= (
        Session().describe_training_job(estimator.latest_training_job.name							).get('''TrainingTimeInSeconds'''		,  999_999							)
    )

    # 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}		,  _lowercase							)
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def       lowerCAmelCase_     (		lowercase:							ndarray		)    ->				float:







 '''simple docstring'''
 return np.dot(lowercase  ,					lowercase		)




class  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  int		,  *,
       _lowercase					:  float = np.inf		,  _lowercase					:  str = "linear"		,  _lowercase					:  float = 0.0		,  ):



    """simple docstring"""



    _UpperCamelCase:     int							= regularization
    _UpperCamelCase:     Optional[int]							= gamma
    if kernel == "linear":
     _UpperCamelCase:     Optional[Any]							= self.__linear
    elif kernel == "rbf":
     if self.gamma == 0:
      raise ValueError('''rbf kernel requires gamma'''							)
     if not isinstance(self.gamma		,  (float, int)							):
      raise ValueError('''gamma must be float or int'''							)
     if not self.gamma > 0:
      raise ValueError('''gamma must be > 0'''							)
     _UpperCamelCase:     Optional[Any]							= self.__rbf
     # in the future, there could be a default value like in sklearn
     # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
     # previously it was 1/(n_features)
    else:
     _UpperCamelCase:     Optional[int]							= f"""Unknown kernel: {kernel}"""
     raise ValueError(_lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.dot(_lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  ndarray		,  _lowercase					:  ndarray							):



    """simple docstring"""



    return np.exp(-(self.gamma * norm_squared(vectora - vectora							))							)



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  list[ndarray]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= observations
    _UpperCamelCase:     Optional[int]							= classes

    # using Wolfe's Dual to calculate w.
    # Primal problem: minimize 1/2*norm_squared(w)
    #   constraint: yn(w . xn + b) >= 1
    #
    # With l a vector
    # Dual problem: maximize sum_n(ln) -
    #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
    #   constraint: self.C >= ln >= 0
    #           and sum_n(ln*yn) = 0
    # Then we get w using w = sum_n(ln*yn*xn)
    # At the end we can get b ~= mean(yn - w . xn)
    #
    # Since we use kernels, we only need l_star to calculate b
    # and to classify observations

    ((_UpperCamelCase)	,							):     Any							= np.shape(_lowercase							)

    def to_minimize(_lowercase					:  ndarray							) -> float:
     _UpperCamelCase:     Optional[int]							= 0
     ((_UpperCamelCase)	,							):     str							= np.shape(_lowercase							)
     for i in range(_lowercase							):
      for j in range(_lowercase							):
       s += (
           candidate[i]
           * candidate[j]
           * classes[i]
           * classes[j]
           * self.kernel(observations[i]		,  observations[j]							)
       )
     return 1 / 2 * s - sum(_lowercase							)

    _UpperCamelCase:     Optional[Any]							= LinearConstraint(_lowercase		,  0		,  0							)
    _UpperCamelCase:     Optional[int]							= Bounds(0		,  self.regularization							)

    _UpperCamelCase:     Dict							= minimize(
        _lowercase		,  np.ones(_lowercase							)		,  bounds=_lowercase		,  constraints=[ly_contraint]							).x
    _UpperCamelCase:     Union[str, Any]							= l_star

    # calculating mean offset of separation plane to points
    _UpperCamelCase:     List[str]							= 0
    for i in range(_lowercase							):
     for j in range(_lowercase							):
      s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
          observations[i]		,  observations[j]							)
    _UpperCamelCase:     str							= s / n



   def   lowerCAmelCase						(	self					:  Optional[Any]		,  _lowercase					:  ndarray							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= sum(
        self.optimum[n]
        * self.classes[n]
        * self.kernel(self.observations[n]		,  _lowercase							)
        for n in range(len(self.classes							)							)							)
    return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
  import doctest

  doctest.testmod()