"""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     UpperCamelCase__(			__A		,						__A ):
						lowerCAmelCase__  :						Union[str, Any]													=		1





						@register_to_config
						def __init__(       self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = None     )					->				Tuple:
													# set `betas`, `alphas`, `timesteps`
													self.set_timesteps(__UpperCAmelCase     )

													# standard deviation of the initial noise distribution
													A__	     =							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.
													A__	     =							4

													# running values
													A__	     =							[]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				Union[str, Any]:
													A__	     =							num_inference_steps
													A__	     =							torch.linspace(1		,0		,num_inference_steps + 1     )[:-1]
													A__	     =							torch.cat([steps, torch.tensor([0.0]     )]     )

													if self.config.trained_betas is not None:
																				A__	     =							torch.tensor(self.config.trained_betas		,dtype=torch.floataa     )
													else:
																				A__	     =							torch.sin(steps * math.pi / 2     ) ** 2

													A__	     =							(1.0 - self.betas**2) ** 0.5

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

													A__	     =							[]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = True		,)					->				Union[SchedulerOutput, Tuple]:
													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'     )

													A__	     =							(self.timesteps == timestep).nonzero().item()
													A__	     =							timestep_index + 1

													A__	     =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
													self.ets.append(__UpperCAmelCase     )

													if len(self.ets     ) == 1:
																				A__	     =							self.ets[-1]
													elif len(self.ets     ) == 2:
																				A__	     =							(3 * self.ets[-1] - self.ets[-2]) / 2
													elif len(self.ets     ) == 3:
																				A__	     =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
													else:
																				A__	     =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

													A__	     =							self._get_prev_sample(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )

													if not return_dict:
																				return (prev_sample,)

													return SchedulerOutput(prev_sample=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				torch.FloatTensor:
													return sample





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Any:
													A__	     =							self.alphas[timestep_index]
													A__	     =							self.betas[timestep_index]

													A__	     =							self.alphas[prev_timestep_index]
													A__	     =							self.betas[prev_timestep_index]

													A__	     =							(sample - sigma * ets) / max(__UpperCAmelCase		,1e-8     )
													A__	     =							next_alpha * pred + ets * next_sigma

													return prev_sample





						def __len__(       self     )					->				Optional[Any]:
													return self.config.num_train_timesteps






"""simple docstring"""




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
				from transformers import MarkupLMFeatureExtractor
class     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase     )					->				str:
													A__	     =							parent





						def 				snake_case__	(       self     )					->				int:
													return {}






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

							A__	     =							'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

							return [html_string_a, html_string_a]


@require_bsa
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Dict													=		MarkupLMFeatureExtractor if is_bsa_available() else None





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							MarkupLMFeatureExtractionTester(self     )





						@property
						def 				snake_case__	(       self     )					->				Optional[Any]:
													return self.feature_extract_tester.prepare_feat_extract_dict()





						def 				snake_case__	(       self     )					->				Any:
													# Initialize feature_extractor
													A__	     =							self.feature_extraction_class()

													# Test not batched input
													A__	     =							get_html_strings()[0]
													A__	     =							feature_extractor(__UpperCAmelCase     )

													# fmt: off
													A__	     =							[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
													A__	     =							[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
													# fmt: on

													self.assertEqual(encoding.nodes		,__UpperCAmelCase     )
													self.assertEqual(encoding.xpaths		,__UpperCAmelCase     )

													# Test batched
													A__	     =							get_html_strings()
													A__	     =							feature_extractor(__UpperCAmelCase     )

													# fmt: off
													A__	     =							expected_nodes + [['My First Heading', 'My first paragraph.']]
													A__	     =							expected_xpaths + [['/html/body/h1', '/html/body/p']]

													self.assertEqual(len(encoding.nodes     )		,2     )
													self.assertEqual(len(encoding.xpaths     )		,2     )

													self.assertEqual(encoding.nodes		,__UpperCAmelCase     )
													self.assertEqual(encoding.xpaths		,__UpperCAmelCase     )





