'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







		'''simple docstring'''






		lowerCamelCase_		:Union[str, Any]						   =		1


		@register_to_config
		def __init__(			self				,		snake_case_ = 1_0_0_0				,		snake_case_ = None						):




							'''simple docstring'''

							self.set_timesteps(snake_case_						)

							# standard deviation of the initial noise distribution
							UpperCAmelCase_							:       Union[str, Any]       =				1.0

							# For now we only support F-PNDM, i.e. the runge-kutta method
							# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
							# mainly at formula (9), (12), (13) and the Algorithm 2.
							UpperCAmelCase_							:       int       =				4

							# running values
							UpperCAmelCase_							:       str       =				[]


		def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = None						):




							'''simple docstring'''

							UpperCAmelCase_							:       Union[str, Any]       =				num_inference_steps
							UpperCAmelCase_							:       int       =				torch.linspace(1				,		0				,		num_inference_steps + 1						)[:-1]
							UpperCAmelCase_							:       Tuple       =				torch.cat([steps, torch.tensor([0.0]						)]						)

							if self.config.trained_betas is not None:
												UpperCAmelCase_							:       Optional[int]       =				torch.tensor(self.config.trained_betas				,		dtype=torch.floataa						)
							else:
												UpperCAmelCase_							:       Tuple       =				torch.sin(steps * math.pi / 2						) ** 2

							UpperCAmelCase_							:       Dict       =				(1.0 - self.betas**2) ** 0.5

							UpperCAmelCase_							:       str       =				(torch.atana(self.betas				,		self.alphas						) / math.pi * 2)[:-1]
							UpperCAmelCase_							:       str       =				timesteps.to(snake_case_						)

							UpperCAmelCase_							:       Any       =				[]


		def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = True				,		):




							'''simple docstring'''

							if self.num_inference_steps is None:
												raise ValueError(
												    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

							UpperCAmelCase_							:       Any       =				(self.timesteps == timestep).nonzero().item()
							UpperCAmelCase_							:       Optional[Any]       =				timestep_index + 1

							UpperCAmelCase_							:       Dict       =				sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
							self.ets.append(snake_case_						)

							if len(self.ets						) == 1:
												UpperCAmelCase_							:       Tuple       =				self.ets[-1]
							elif len(self.ets						) == 2:
												UpperCAmelCase_							:       Any       =				(3 * self.ets[-1] - self.ets[-2]) / 2
							elif len(self.ets						) == 3:
												UpperCAmelCase_							:       List[str]       =				(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
							else:
												UpperCAmelCase_							:       Union[str, Any]       =				(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

							UpperCAmelCase_							:       Union[str, Any]       =				self._get_prev_sample(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						)

							if not return_dict:
												return (prev_sample,)

							return SchedulerOutput(prev_sample=snake_case_						)


		def _UpperCamelCase					(			self				,		snake_case_				,		*snake_case_				,		**snake_case_						):




							'''simple docstring'''

							return sample


		def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




							'''simple docstring'''

							UpperCAmelCase_							:       int       =				self.alphas[timestep_index]
							UpperCAmelCase_							:       Union[str, Any]       =				self.betas[timestep_index]

							UpperCAmelCase_							:       Any       =				self.alphas[prev_timestep_index]
							UpperCAmelCase_							:       Dict       =				self.betas[prev_timestep_index]

							UpperCAmelCase_							:       List[Any]       =				(sample - sigma * ets) / max(snake_case_				,		1E-8						)
							UpperCAmelCase_							:       Tuple       =				next_alpha * pred + ets * next_sigma

							return prev_sample






		def __len__(			self						):




							'''simple docstring'''

							return self.config.num_train_timesteps







'''simple docstring'''


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 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			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=snake_case_				,		)
       assert hasattr(self				,		'env'						)


  def _UpperCamelCase					(			self				,		snake_case_=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=snake_case_				,		instance_type=self.instance_type				,		debugger_hook_config=snake_case_				,		hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}				,		metric_definitions=self.env.metric_definitions				,		py_version='py36'				,		)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       TrainingJobAnalytics(snake_case_						).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCAmelCase_							:       int       =				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

       # extract kpis
       UpperCAmelCase_							:       Dict       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
       UpperCAmelCase_							:       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
       UpperCAmelCase_							:       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}				,		snake_case_						)






