'''simple docstring'''





import torch

from diffusers import DiffusionPipeline




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						List[Any]       ,		lowercase__					:						Tuple					)							->str:


										'''simple docstring'''




										super().__init__()

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







				def __call__(						self					:						int					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						torch.randn(
										    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,		)
										_UpperCamelCase			:			Union[str, Any]   			=						1

										_UpperCamelCase			:			Any   			=						self.unet(lowercase__       ,		lowercase__					).sample
										_UpperCamelCase			:			Any   			=						self.scheduler.step(lowercase__       ,		lowercase__       ,		lowercase__					).prev_sample

										_UpperCamelCase			:			Dict   			=						scheduler_output - scheduler_output + torch.ones_like(lowercase__					)

										return result



'''simple docstring'''





import re
from pathlib import Path
from unittest import TestCase

import pytest




@pytest.mark.integration
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			Optional[int]   			=						re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"					)
																_UpperCamelCase			:			Dict   			=						input_file.read()
																_UpperCamelCase			:			Dict   			=						regexp.search(lowercase__					)

										return match





				def  snake_case__				(						self					:						str       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										with open(lowercase__       ,		encoding="utf-8"					) as input_file:
																_UpperCamelCase			:			str   			=						re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,		re.DOTALL					)
																_UpperCamelCase			:			Optional[Any]   			=						input_file.read()
																# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
																_UpperCamelCase			:			List[str]   			=						regexp.finditer(lowercase__					)

										_UpperCamelCase			:			Any   			=						[match for match in matches if match is not None and match.group(1					) is not None]
										return matches[0] if matches else None





				def  snake_case__				(						self					:						Optional[int]					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			List[Any]   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_encoding_on_file_open(str(lowercase__					)					):
																						raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''					)







				def  snake_case__				(						self					:						Optional[Any]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						Path("./datasets"					)
										_UpperCamelCase			:			Any   			=						list(dataset_paths.absolute().glob("**/*.py"					)					)

										for dataset in dataset_files:
																if self._no_print_statements(str(lowercase__					)					):
																						raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''					)


