code: 
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  import PIL
  import torch
  
  from diffusers.image_processor import VaeImageProcessor
  
  class 			lowerCamelCase__    (  unittest.TestCase			):
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				1
  					snake_case		:						Dict								=				3
  					snake_case		:						int								=				8
  					snake_case		:						int								=				8
  
  					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)
  
  					return sample
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if isinstance(image[0]				, PIL.Image.Image							):
  							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
  					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
  							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					return image
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Any								=				self.dummy_sample
  					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Optional[Any]								=				image_processor.postprocess(
  							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
  							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  
  							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
  							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
  							assert (
  							    np.abs(in_np - out_np							).max() < 1E-6
  							), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
  					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)
  
  					for output_type in ["pt", "np", "pil"]:
  							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
  							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
  									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
  									assert (
  									    np.abs(in_np - out_np							).max() < 1E-6
  									), F'''decoded output does not match input for output_type {output_type}'''
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[str]								=				self.dummy_sample
  					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
  					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						Union[str, Any]								=				self.dummy_sample
  					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Any								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
  					list(SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						str								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
  					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
  
  					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
  					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str   ):
  		snake_case		:						List[str]								=				tmp_path / "text.txt"
  		snake_case		:						Tuple								=				textwrap.dedent(
  		    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "   )
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     Dict   ):
  		snake_case		:						Any								=				tmp_path / "text_with_image.txt"
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.mark.parametrize("keep_linebreaks"       ,     [True, False]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Tuple   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						List[Any]								=				f.read().splitlines(keepends=lowercase__   )
  		snake_case		:						List[Any]								=				Text(keep_linebreaks=lowercase__       ,     encoding="utf-8"   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						int								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  
  
  @require_pil
  def   UpperCamelCase__     (		lowercase__  :     Optional[Any]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Union[str, Any]								=				f.read().splitlines()[0]
  		snake_case		:						List[Any]								=				Text(encoding="utf-8"       ,     features=Features({"image": Image()}   )   )
  		snake_case		:						Tuple								=				text._generate_tables([[text_file_with_image]]   )
  		snake_case		:						List[str]								=				pa.concat_tables([table for _, table in generator]   )
  		assert pa_table.schema.field("image"   ).type == Image()()
  		snake_case		:						Optional[Any]								=				pa_table.to_pydict()["image"]
  		assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  @pytest.mark.parametrize("sample_by"       ,     ["line", "paragraph", "document"]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Tuple								=				f.read()
  		if sample_by == "line":
  				snake_case		:						Optional[int]								=				expected_content.splitlines()
  		elif sample_by == "paragraph":
  				snake_case		:						str								=				expected_content.split("\n\n"   )
  		elif sample_by == "document":
  				snake_case		:						List[Any]								=				[expected_content]
  		snake_case		:						str								=				Text(sample_by=lowercase__       ,     encoding="utf-8"       ,     chunksize=100   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						Optional[Any]								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  