"""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 lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				str		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				parent



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									return {}




def 						SCREAMING_SNAKE_CASE					(  )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				"<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>"

				SCREAMING_SNAKE_CASE__		  =				"\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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		MarkupLMFeatureExtractor if is_bsa_available() else None



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				MarkupLMFeatureExtractionTester(self		)



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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.feature_extraction_class()

									# Test not batched input
									SCREAMING_SNAKE_CASE__		  =				get_html_strings()[0]
									SCREAMING_SNAKE_CASE__		  =				feature_extractor(_snake_case		)

									# fmt: off
									SCREAMING_SNAKE_CASE__		  =				[["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"]]
									SCREAMING_SNAKE_CASE__		  =				[["/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   , _snake_case		)
									self.assertEqual(encoding.xpaths   , _snake_case		)

									# Test batched
									SCREAMING_SNAKE_CASE__		  =				get_html_strings()
									SCREAMING_SNAKE_CASE__		  =				feature_extractor(_snake_case		)

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

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

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





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler.from_config(scheduler.config		)

									self.register_modules(unet=_snake_case   , scheduler=_snake_case		)



					@torch.no_grad()
					def __call__(						self			:				int   , _snake_case			:				int = 1   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				float = 0.0   , _snake_case			:				int = 50   , _snake_case			:				Optional[bool] = None   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(_snake_case   , _snake_case		) and len(_snake_case		) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(_snake_case		)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				(image / 2 + 0.5).clamp(0   , 1		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		).numpy()
									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




