




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       '\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 		UpperCamelCase__     (unittest.TestCase   ,    lowerCAmelCase__					):

    '''simple docstring'''



    def    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				int      			= load_tool("summarization"					)
           self.tool.setup()
           lowerCamelCase       :				Tuple      			= load_tool("summarization"					,  remote=UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Any:
           lowerCamelCase       :				Optional[Any]      			= self.tool(UpperCamelCase__					)
           self.assertEqual(
               UpperCamelCase__					,  "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    _lowercase					(   self					)					->				str:
           lowerCamelCase       :				Optional[Any]      			= self.remote_tool(UpperCamelCase__					)
           self.assertEqual(
               UpperCamelCase__					,  "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    _lowercase					(   self					)					->				int:
           lowerCamelCase       :				List[str]      			= self.tool(text=UpperCamelCase__					)
           self.assertEqual(
               UpperCamelCase__					,  "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    _lowercase					(   self					)					->				List[Any]:
           lowerCamelCase       :				List[Any]      			= self.remote_tool(text=UpperCamelCase__					)
           self.assertEqual(
               UpperCamelCase__					,  "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."					,  )












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 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[Any]     					=				CONFIG_NAME







    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = True					,  UpperCamelCase__ = 8					,  UpperCamelCase__ = "lanczos"					,  UpperCamelCase__ = True					,  UpperCamelCase__ = False					,  )					->				Optional[int]:
           super().__init__()







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				PIL.Image.Image:
           if images.ndim == 3:
                  lowerCamelCase       :				str      			= images[None, ...]
           lowerCamelCase       :				Any      			= (images * 255).round().astype("uint8"					)
           if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  lowerCamelCase       :				List[Any]      			= [Image.fromarray(image.squeeze()					,  mode="L"					) for image in images]
           else:
                  lowerCamelCase       :				List[Any]      			= [Image.fromarray(UpperCamelCase__					) for image in images]

           return pil_images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				np.ndarray:
           if not isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  lowerCamelCase       :				List[str]      			= [images]
           lowerCamelCase       :				Union[str, Any]      			= [np.array(UpperCamelCase__					).astype(np.floataa					) / 255.0 for image in images]
           lowerCamelCase       :				List[str]      			= np.stack(UpperCamelCase__					,  axis=0					)

           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				torch.FloatTensor:
           if images.ndim == 3:
                  lowerCamelCase       :				Union[str, Any]      			= images[..., None]

           lowerCamelCase       :				List[Any]      			= torch.from_numpy(images.transpose(0					,  3					,  1					,  2					)					)
           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				np.ndarray:
           lowerCamelCase       :				List[str]      			= images.cpu().permute(0					,  2					,  3					,  1					).float().numpy()
           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				int:
           return 2.0 * images - 1.0







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				Dict:
           return (images / 2 + 0.5).clamp(0					,  1					)







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				PIL.Image.Image:
           lowerCamelCase       :				Union[str, Any]      			= image.convert("RGB"					)
           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				PIL.Image.Image:
           if height is None:
                  lowerCamelCase       :				Any      			= image.height
           if width is None:
                  lowerCamelCase       :				Union[str, Any]      			= image.width

           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= (
               x - x % self.config.vae_scale_factor for x in (width, height)
           )  # resize to integer multiple of vae_scale_factor
           lowerCamelCase       :				Optional[Any]      			= image.resize((width, height)					,  resample=PIL_INTERPOLATION[self.config.resample]					)
           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				torch.Tensor:
           lowerCamelCase       :				Union[str, Any]      			= (PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  lowerCamelCase       :				Optional[int]      			= [image]
           elif not (isinstance(UpperCamelCase__					,  UpperCamelCase__					) and all(isinstance(UpperCamelCase__					,  UpperCamelCase__					) for i in image					)):
                  raise ValueError(
                      F'''Input is in incorrect format: {[type(UpperCamelCase__					) for i in image]}. Currently, we only support {", ".join(UpperCamelCase__					)}'''					)

           if isinstance(image[0]					,  PIL.Image.Image					):
                  if self.config.do_convert_rgb:
                         lowerCamelCase       :				Tuple      			= [self.convert_to_rgb(UpperCamelCase__					) for i in image]
                  if self.config.do_resize:
                         lowerCamelCase       :				Tuple      			= [self.resize(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					) for i in image]
                  lowerCamelCase       :				Optional[Any]      			= self.pil_to_numpy(UpperCamelCase__					)  # to np
                  lowerCamelCase       :				List[str]      			= self.numpy_to_pt(UpperCamelCase__					)  # to pt

           elif isinstance(image[0]					,  np.ndarray					):
                  lowerCamelCase       :				Optional[int]      			= np.concatenate(UpperCamelCase__					,  axis=0					) if image[0].ndim == 4 else np.stack(UpperCamelCase__					,  axis=0					)
                  lowerCamelCase       :				Optional[int]      			= self.numpy_to_pt(UpperCamelCase__					)
                  lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= 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					):
                  lowerCamelCase       :				Dict      			= torch.cat(UpperCamelCase__					,  axis=0					) if image[0].ndim == 4 else torch.stack(UpperCamelCase__					,  axis=0					)
                  lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= 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]
           lowerCamelCase       :				str      			= 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()}]'''					,  UpperCamelCase__					,  )
                  lowerCamelCase       :				Optional[Any]      			= False

           if do_normalize:
                  lowerCamelCase       :				int      			= self.normalize(UpperCamelCase__					)

           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = None					,  )					->				Dict:
           if not isinstance(UpperCamelCase__					,  torch.Tensor					):
                  raise ValueError(
                      F'''Input for postprocessing is in incorrect format: {type(UpperCamelCase__					)}. We only support pytorch tensor'''					)
           if output_type not in ["latent", "pt", "np", "pil"]:
                  lowerCamelCase       :				str      			= (
                      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"					,  UpperCamelCase__					,  standard_warn=UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= "np"

           if output_type == "latent":
                  return image

           if do_denormalize is None:
                  lowerCamelCase       :				List[str]      			= [self.config.do_normalize] * image.shape[0]

           lowerCamelCase       :				Dict      			= 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

           lowerCamelCase       :				Any      			= self.pt_to_numpy(UpperCamelCase__					)

           if output_type == "np":
                  return image

           if output_type == "pil":
                  return self.numpy_to_pil(UpperCamelCase__					)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				CONFIG_NAME







    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = True					,  UpperCamelCase__ = 8					,  UpperCamelCase__ = "lanczos"					,  UpperCamelCase__ = True					,  )					->				Tuple:
           super().__init__()







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				int:
           if images.ndim == 3:
                  lowerCamelCase       :				Union[str, Any]      			= images[None, ...]
           lowerCamelCase       :				Any      			= (images * 255).round().astype("uint8"					)
           if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  lowerCamelCase       :				Any      			= [Image.fromarray(image.squeeze()					,  mode="L"					) for image in images]
           else:
                  lowerCamelCase       :				Dict      			= [Image.fromarray(image[:, :, :3]					) for image in images]

           return pil_images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				Optional[int]:
           return image[:, :, 1] * 2**8 + image[:, :, 2]







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Any:
           if images.ndim == 3:
                  lowerCamelCase       :				Dict      			= images[None, ...]
           lowerCamelCase       :				Any      			= images[:, :, :, 3:]
           if images.shape[-1] == 6:
                  lowerCamelCase       :				List[str]      			= (images_depth * 255).round().astype("uint8"					)
                  lowerCamelCase       :				Any      			= [
                      Image.fromarray(self.rgblike_to_depthmap(UpperCamelCase__					)					,  mode="I;16"					) for image_depth in images_depth
                  ]
           elif images.shape[-1] == 4:
                  lowerCamelCase       :				Any      			= (images_depth * 65535.0).astype(np.uintaa					)
                  lowerCamelCase       :				Any      			= [Image.fromarray(UpperCamelCase__					,  mode="I;16"					) for image_depth in images_depth]
           else:
                  raise Exception("Not supported"					)

           return pil_images







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = None					,  )					->				Dict:
           if not isinstance(UpperCamelCase__					,  torch.Tensor					):
                  raise ValueError(
                      F'''Input for postprocessing is in incorrect format: {type(UpperCamelCase__					)}. We only support pytorch tensor'''					)
           if output_type not in ["latent", "pt", "np", "pil"]:
                  lowerCamelCase       :				List[str]      			= (
                      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"					,  UpperCamelCase__					,  standard_warn=UpperCamelCase__					)
                  lowerCamelCase       :				Tuple      			= "np"

           if do_denormalize is None:
                  lowerCamelCase       :				Union[str, Any]      			= [self.config.do_normalize] * image.shape[0]

           lowerCamelCase       :				List[Any]      			= torch.stack(
               [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

           lowerCamelCase       :				Union[str, Any]      			= self.pt_to_numpy(UpperCamelCase__					)

           if output_type == "np":
                  if image.shape[-1] == 6:
                         lowerCamelCase       :				List[str]      			= np.stack([self.rgblike_to_depthmap(im[:, :, 3:]					) for im in image]					,  axis=0					)
                  else:
                         lowerCamelCase       :				Optional[Any]      			= image[:, :, :, 3:]
                  return image[:, :, :, :3], image_depth

           if output_type == "pil":
                  return self.numpy_to_pil(UpperCamelCase__					), self.numpy_to_depth(UpperCamelCase__					)
           else:
                  raise Exception(F'''This type {output_type} is not supported'''					)






