"""simple docstring"""


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	):
				A__      :   int  							=       CONFIG_NAME

				@register_to_config
				def __init__(      self	, _lowerCamelCase = True	, _lowerCamelCase = 8	, _lowerCamelCase = "lanczos"	, _lowerCamelCase = True	, _lowerCamelCase = False	, ):
										super().__init__()

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										if images.ndim == 3:
																lowerCamelCase__					=images[None, ...]
										lowerCamelCase__					=(images * 255).round().astype("uint8"      )
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																lowerCamelCase__					=[Image.fromarray(image.squeeze()	, mode="L"      ) for image in images]
										else:
																lowerCamelCase__					=[Image.fromarray(snake_case__      ) for image in images]

										return pil_images

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										if not isinstance(snake_case__	, snake_case__      ):
																lowerCamelCase__					=[images]
										lowerCamelCase__					=[np.array(snake_case__      ).astype(np.floataa      ) / 2_5_5.0 for image in images]
										lowerCamelCase__					=np.stack(snake_case__	, axis=0      )

										return images

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										if images.ndim == 3:
																lowerCamelCase__					=images[..., None]

										lowerCamelCase__					=torch.from_numpy(images.transpose(0	, 3	, 1	, 2      )      )
										return images

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										lowerCamelCase__					=images.cpu().permute(0	, 2	, 3	, 1      ).float().numpy()
										return images

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										return 2.0 * images - 1.0

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										return (images / 2 + 0.5).clamp(0	, 1      )

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										lowerCamelCase__					=image.convert("RGB"      )
										return image

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None	, _lowerCamelCase = None	, ):
										if height is None:
																lowerCamelCase__					=image.height
										if width is None:
																lowerCamelCase__					=image.width

										lowerCamelCase__					=(
										    x - x % self.config.vae_scale_factor for x in (width, height)
										)  # resize to integer multiple of vae_scale_factor
										lowerCamelCase__					=image.resize((width, height)	, resample=PIL_INTERPOLATION[self.config.resample]      )
										return image

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = None	, _lowerCamelCase = None	, ):
										lowerCamelCase__					=(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(snake_case__	, snake_case__      ):
																lowerCamelCase__					=[image]
										elif not (isinstance(snake_case__	, snake_case__      ) and all(isinstance(snake_case__	, snake_case__      ) for i in image      )):
																raise ValueError(
																    F'''Input is in incorrect format: {[type(snake_case__      ) for i in image]}. Currently, we only support {', '.join(snake_case__      )}'''      )

										if isinstance(image[0]	, PIL.Image.Image      ):
																if self.config.do_convert_rgb:
																						lowerCamelCase__					=[self.convert_to_rgb(snake_case__      ) for i in image]
																if self.config.do_resize:
																						lowerCamelCase__					=[self.resize(snake_case__	, snake_case__	, snake_case__      ) for i in image]
																lowerCamelCase__					=self.pil_to_numpy(snake_case__      )  # to np
																lowerCamelCase__					=self.numpy_to_pt(snake_case__      )  # to pt

										elif isinstance(image[0]	, np.ndarray      ):
																lowerCamelCase__					=np.concatenate(snake_case__	, axis=0      ) if image[0].ndim == 4 else np.stack(snake_case__	, axis=0      )
																lowerCamelCase__					=self.numpy_to_pt(snake_case__      )
																lowerCamelCase__					=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__					=torch.cat(snake_case__	, axis=0      ) if image[0].ndim == 4 else torch.stack(snake_case__	, axis=0      )
																lowerCamelCase__					=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__					=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()}]'''	, snake_case__	, )
																lowerCamelCase__					=False

										if do_normalize:
																lowerCamelCase__					=self.normalize(snake_case__      )

										return image






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = "pil"	, _lowerCamelCase = None	, ):
										if not isinstance(snake_case__	, torch.Tensor      ):
																raise ValueError(
																    F'''Input for postprocessing is in incorrect format: {type(snake_case__      )}. We only support pytorch tensor'''      )
										if output_type not in ["latent", "pt", "np", "pil"]:
																lowerCamelCase__					=(
																    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"	, snake_case__	, standard_warn=snake_case__      )
																lowerCamelCase__					="np"

										if output_type == "latent":
																return image

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

										lowerCamelCase__					=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__					=self.pt_to_numpy(snake_case__      )

										if output_type == "np":
																return image

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






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   List[Any]  							=       CONFIG_NAME

				@register_to_config
				def __init__(      self	, _lowerCamelCase = True	, _lowerCamelCase = 8	, _lowerCamelCase = "lanczos"	, _lowerCamelCase = True	, ):
										super().__init__()

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										if images.ndim == 3:
																lowerCamelCase__					=images[None, ...]
										lowerCamelCase__					=(images * 255).round().astype("uint8"      )
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																lowerCamelCase__					=[Image.fromarray(image.squeeze()	, mode="L"      ) for image in images]
										else:
																lowerCamelCase__					=[Image.fromarray(image[:, :, :3]      ) for image in images]

										return pil_images

				@staticmethod
				def 		_a	(      _lowerCamelCase      ):
										return image[:, :, 1] * 2**8 + image[:, :, 2]

				def 		_a	(      self	, _lowerCamelCase      ):
										if images.ndim == 3:
																lowerCamelCase__					=images[None, ...]
										lowerCamelCase__					=images[:, :, :, 3:]
										if images.shape[-1] == 6:
																lowerCamelCase__					=(images_depth * 255).round().astype("uint8"      )
																lowerCamelCase__					=[
																    Image.fromarray(self.rgblike_to_depthmap(snake_case__      )	, mode="I;16"      ) for image_depth in images_depth
																]
										elif images.shape[-1] == 4:
																lowerCamelCase__					=(images_depth * 65535.0).astype(np.uintaa      )
																lowerCamelCase__					=[Image.fromarray(snake_case__	, mode="I;16"      ) for image_depth in images_depth]
										else:
																raise Exception("Not supported"      )

										return pil_images






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = "pil"	, _lowerCamelCase = None	, ):
										if not isinstance(snake_case__	, torch.Tensor      ):
																raise ValueError(
																    F'''Input for postprocessing is in incorrect format: {type(snake_case__      )}. We only support pytorch tensor'''      )
										if output_type not in ["latent", "pt", "np", "pil"]:
																lowerCamelCase__					=(
																    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"	, snake_case__	, standard_warn=snake_case__      )
																lowerCamelCase__					="np"

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

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

										lowerCamelCase__					=self.pt_to_numpy(snake_case__      )

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

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










"""simple docstring"""



import shutil

import requests


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      bool = False       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				"https://api.nasa.gov/planetary/apod"
     return requests.get(snake_case       , params={"api_key": api_key}							).json()


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				get_apod_data(snake_case							)
     UpperCAmelCase__		:  str				=				apod_data["url"]
     UpperCAmelCase__		:  Tuple				=				img_url.split("/"							)[-1]
     UpperCAmelCase__		:  Dict				=				requests.get(snake_case       , stream=snake_case							)

     with open(f'{path}/{img_name}'       , "wb+"							) as img_file:
          shutil.copyfileobj(response.raw       , snake_case							)
     del response
     return apod_data


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				"https://images-api.nasa.gov/search"
     return requests.get(snake_case       , params={"q": query}							).json()


if __name__ == "__main__":
 print(save_apod("""YOUR API KEY"""))
 _lowerCAmelCase     :     List[Any]            =	get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
 print(apollo_aaaa_items[0]["""data"""][0]["""description"""])


