code: 
  
  
  
  
  import unittest
  
  from transformers import load_tool
  
  from .test_tools_common import ToolTesterMixin
  
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					"\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"
  
  
  
  class 			a_					(    unittest.TestCase     ,						SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	load_tool('summarization'			)
  				self.tool.setup()
  				SCREAMING_SNAKE_CASE_			=	load_tool('summarization'			,    remote=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.tool(SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.remote_tool(SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.tool(text=SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.remote_tool(text=SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
style_context: 
  
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   CONFIG_NAME
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[None, ...]
  				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
  				if images.shape[-1] == 1:
  							# special case for grayscale (single channel) images
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
  				else:
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			) for image in images]
  
  				return pil_images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	[images]
  				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
  				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[..., None]
  
  				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(images.transpose(0			,    3			,    1			,    2			)			)
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 2.0 * images - 1.0
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return (images / 2 + 0.5).clamp(0			,    1			)
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if height is None:
  							SCREAMING_SNAKE_CASE_			=	image.height
  				if width is None:
  							SCREAMING_SNAKE_CASE_			=	image.width
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(
  				    x - x % self.config.vae_scale_factor for x in (width, height)
  				)  # resize to integer multiple of vae_scale_factor
  				SCREAMING_SNAKE_CASE_			=	image.resize((width, height)			,    resample=PIL_INTERPOLATION[self.config.resample]			)
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
  				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	[image]
  				elif not (isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) and all(isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image			)):
  							raise ValueError(
  							    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE			)}'			)
  
  				if isinstance(image[0]			,    PIL.Image.Image			):
  							if self.config.do_convert_rgb:
  										SCREAMING_SNAKE_CASE_			=	[self.convert_to_rgb(SCREAMING_SNAKE_CASE			) for i in image]
  							if self.config.do_resize:
  										SCREAMING_SNAKE_CASE_			=	[self.resize(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image]
  							SCREAMING_SNAKE_CASE_			=	self.pil_to_numpy(SCREAMING_SNAKE_CASE			)  # to np
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)  # to pt
  
  				elif isinstance(image[0]			,    np.ndarray			):
  							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
  							if self.config.do_resize and (
  							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
  							):
  										raise ValueError(
  										    f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
  										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)
  
  				elif isinstance(image[0]			,    torch.Tensor			):
  							SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
  
  							# don't need any preprocess if the image is latents
  							if channel == 4:
  										return image
  
  							if self.config.do_resize and (
  							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
  							):
  										raise ValueError(
  										    f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
  										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)
  
          # expected range [0,1], normalize to [-1,1]
  				SCREAMING_SNAKE_CASE_			=	self.config.do_normalize
  				if image.min() < 0:
  							warnings.warn(
  							    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
  							    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'			,    SCREAMING_SNAKE_CASE			,    )
  							SCREAMING_SNAKE_CASE_			=	False
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
  							raise ValueError(
  							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
  				if output_type not in ["latent", "pt", "np", "pil"]:
  							SCREAMING_SNAKE_CASE_			=	(
  							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
  							    '`pil`, `np`, `pt`, `latent`'
  							)
  							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	'np'
  
  				if output_type == "latent":
  							return image
  
  				if do_denormalize is None:
  							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]
  
  				SCREAMING_SNAKE_CASE_			=	torch.stack(
  				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
  				if output_type == "pt":
  							return image
  
  				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)
  
  				if output_type == "np":
  							return image
  
  				if output_type == "pil":
  							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   CONFIG_NAME
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[None, ...]
  				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
  				if images.shape[-1] == 1:
  							# special case for grayscale (single channel) images
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
  				else:
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image[:, :, :3]			) for image in images]
  
  				return pil_images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[None, ...]
  				SCREAMING_SNAKE_CASE_			=	images[:, :, :, 3:]
  				if images.shape[-1] == 6:
  							SCREAMING_SNAKE_CASE_			=	(images_depth * 255).round().astype('uint8'			)
  							SCREAMING_SNAKE_CASE_			=	[
  							    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE			)			,    mode='I;16'			) for image_depth in images_depth
  							]
  				elif images.shape[-1] == 4:
  							SCREAMING_SNAKE_CASE_			=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
  				else:
  							raise Exception('Not supported'			)
  
  				return pil_images
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
  							raise ValueError(
  							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
  				if output_type not in ["latent", "pt", "np", "pil"]:
  							SCREAMING_SNAKE_CASE_			=	(
  							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
  							    '`pil`, `np`, `pt`, `latent`'
  							)
  							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	'np'
  
  				if do_denormalize is None:
  							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]
  
  				SCREAMING_SNAKE_CASE_			=	torch.stack(
  				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
  				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)
  
  				if output_type == "np":
  							if image.shape[-1] == 6:
  										SCREAMING_SNAKE_CASE_			=	np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]			,    axis=0			)
  							else:
  										SCREAMING_SNAKE_CASE_			=	image[:, :, :, 3:]
  							return image[:, :, :, :3], image_depth
  
  				if output_type == "pil":
  							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			), self.numpy_to_depth(SCREAMING_SNAKE_CASE			)
  				else:
  							raise Exception(f'This type {output_type} is not supported'			)
  