"""simple docstring"""







from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


UpperCamelCase_							:							Union[str, Any]				 =		getLogger(__name__)


UpperCamelCase_							:							int				 =		4
UpperCamelCase_							:							Optional[int]				 =		7

UpperCamelCase_							:							int				 =		{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}


def 	a__   (		UpperCAmelCase__							:      int      )->						List[Any]:
						if isinstance(UpperCAmelCase__		,      torch.Tensor      ):
												return image
						elif isinstance(UpperCAmelCase__		,      PIL.Image.Image      ):
												UpperCAmelCase__       						=		[image]

						if isinstance(image[0]		,      PIL.Image.Image      ):
												UpperCAmelCase__						,   UpperCAmelCase__       						=		image[0].size
												UpperCAmelCase__						,   UpperCAmelCase__       						=		(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

												UpperCAmelCase__       						=		[np.array(i.resize((w, h)      )      )[None, :] for i in image]
												UpperCAmelCase__       						=		np.concatenate(UpperCAmelCase__		,      axis=0      )
												UpperCAmelCase__       						=		np.array(UpperCAmelCase__      ).astype(np.floataa      ) / 2_55.0
												UpperCAmelCase__       						=		image.transpose(0		,      3		,      1		,      2      )
												UpperCAmelCase__       						=		2.0 * image - 1.0
												UpperCAmelCase__       						=		torch.from_numpy(UpperCAmelCase__      )
						elif isinstance(image[0]		,      torch.Tensor      ):
												UpperCAmelCase__       						=		torch.cat(UpperCAmelCase__		,      dim=0      )

						return image







class       _lowercase		( __lowerCamelCase	):

				def __init__(  self							:     str							,     _lowercase							:     OnnxRuntimeModel							,     _lowercase							:     OnnxRuntimeModel							,     _lowercase							:     Any							,     _lowercase							:     OnnxRuntimeModel							,     _lowercase							:     DDPMScheduler							,     _lowercase							:     Any							,     _lowercase							:     int = 3_5_0							,     ):


										'''simple docstring'''
										super().__init__(
										    vae=_lowercase							,     text_encoder=_lowercase							,     tokenizer=_lowercase							,     unet=_lowercase							,     low_res_scheduler=_lowercase							,     scheduler=_lowercase							,     safety_checker=_lowercase							,     feature_extractor=_lowercase							,     watermarker=_lowercase							,     max_noise_level=_lowercase							,     )

				def __call__(  self							:     List[str]							,     _lowercase							:     Union[str, List[str]]							,     _lowercase							:     Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]							,     _lowercase							:     int = 7_5							,     _lowercase							:     float = 9.0							,     _lowercase							:     int = 2_0							,     _lowercase							:     Optional[Union[str, List[str]]] = None							,     _lowercase							:     Optional[int] = 1							,     _lowercase							:     float = 0.0							,     _lowercase							:     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,     _lowercase							:     Optional[torch.FloatTensor] = None							,     _lowercase							:     Optional[np.ndarray] = None							,     _lowercase							:     Optional[np.ndarray] = None							,     _lowercase							:     Optional[str] = "pil"							,     _lowercase							:     bool = True							,     _lowercase							:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     _lowercase							:     Optional[int] = 1							,     ):


										'''simple docstring'''
										self.check_inputs(_lowercase							,     _lowercase							,     _lowercase							,     _lowercase  )

										# 2. Define call parameters
										if prompt is not None and isinstance(_lowercase							,     _lowercase  ):
																UpperCAmelCase__       						=		1
										elif prompt is not None and isinstance(_lowercase							,     _lowercase  ):
																UpperCAmelCase__       						=		len(_lowercase  )
										else:
																UpperCAmelCase__       						=		prompt_embeds.shape[0]

										UpperCAmelCase__       						=		self._execution_device
										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										UpperCAmelCase__       						=		guidance_scale > 1.0

										# 3. Encode input prompt
										UpperCAmelCase__       						=		self._encode_prompt(
										    _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     prompt_embeds=_lowercase							,     negative_prompt_embeds=_lowercase							,     )

										UpperCAmelCase__       						=		ORT_TO_PT_TYPE[str(text_embeddings.dtype  )]

										# 4. Preprocess image
										UpperCAmelCase__       						=		preprocess(_lowercase  )
										UpperCAmelCase__       						=		image.cpu()

										# 5. set timesteps
										self.scheduler.set_timesteps(_lowercase							,     device=_lowercase  )
										UpperCAmelCase__       						=		self.scheduler.timesteps

										# 5. Add noise to image
										UpperCAmelCase__       						=		torch.tensor([noise_level]							,     dtype=torch.long							,     device=_lowercase  )
										UpperCAmelCase__       						=		torch.randn(image.shape							,     generator=_lowercase							,     device=_lowercase							,     dtype=_lowercase  )
										UpperCAmelCase__       						=		self.low_res_scheduler.add_noise(_lowercase							,     _lowercase							,     _lowercase  )

										UpperCAmelCase__       						=		2 if do_classifier_free_guidance else 1
										UpperCAmelCase__       						=		np.concatenate([image] * batch_multiplier * num_images_per_prompt  )
										UpperCAmelCase__       						=		np.concatenate([noise_level] * image.shape[0]  )

										# 6. Prepare latent variables
										UpperCAmelCase__						,   UpperCAmelCase__       						=		image.shape[2:]
										UpperCAmelCase__       						=		self.prepare_latents(
										    batch_size * num_images_per_prompt							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     _lowercase							,     )

										# 7. Check that sizes of image and latents match
										UpperCAmelCase__       						=		image.shape[1]
										if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																raise ValueError(
																    """Incorrect configuration settings! The config of `pipeline.unet` expects"""
																    f' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
																    f' `num_channels_image`: {num_channels_image} '
																    f' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
																    """ `pipeline.unet` or your `image` input."""  )

										# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
										UpperCAmelCase__       						=		self.prepare_extra_step_kwargs(_lowercase							,     _lowercase  )

										UpperCAmelCase__       						=		next(
										    (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")							,     """tensor(float)"""  )
										UpperCAmelCase__       						=		ORT_TO_NP_TYPE[timestep_dtype]

										# 9. Denoising loop
										UpperCAmelCase__       						=		len(_lowercase  ) - num_inference_steps * self.scheduler.order
										with self.progress_bar(total=_lowercase  ) as progress_bar:
																for i, t in enumerate(_lowercase  ):
																						# expand the latents if we are doing classifier free guidance
																						UpperCAmelCase__       						=		np.concatenate([latents] * 2  ) if do_classifier_free_guidance else latents

																						# concat latents, mask, masked_image_latents in the channel dimension
																						UpperCAmelCase__       						=		self.scheduler.scale_model_input(_lowercase							,     _lowercase  )
																						UpperCAmelCase__       						=		np.concatenate([latent_model_input, image]							,     axis=1  )

																						# timestep to tensor
																						UpperCAmelCase__       						=		np.array([t]							,     dtype=_lowercase  )

																						# predict the noise residual
																						UpperCAmelCase__       						=		self.unet(
																						    sample=_lowercase							,     timestep=_lowercase							,     encoder_hidden_states=_lowercase							,     class_labels=noise_level.astype(np.intaa  )							,     )[0]

																						# perform guidance
																						if do_classifier_free_guidance:
																												UpperCAmelCase__						,   UpperCAmelCase__       						=		np.split(_lowercase							,     2  )
																						UpperCAmelCase__       						=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						# compute the previous noisy sample x_t -> x_t-1
																						UpperCAmelCase__       						=		self.scheduler.step(
																						    torch.from_numpy(_lowercase  )							,     _lowercase							,     _lowercase							,     **_lowercase  ).prev_sample

																						# call the callback, if provided
																						if i == len(_lowercase  ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																												progress_bar.update()
																												if callback is not None and i % callback_steps == 0:
																																		callback(_lowercase							,     _lowercase							,     _lowercase  )

        # 10. Post-processing
										UpperCAmelCase__       						=		self.decode_latents(latents.float()  )

										# 11. Convert to PIL
										if output_type == "pil":
																UpperCAmelCase__       						=		self.numpy_to_pil(_lowercase  )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=_lowercase  )

				def 							A			(  self							:     str							,     _lowercase							:     Union[str, Any]  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		1 / 0.0_8333 * latents
										UpperCAmelCase__       						=		self.vae(latent_sample=_lowercase  )[0]
										UpperCAmelCase__       						=		np.clip(image / 2 + 0.5							,     0							,     1  )
										UpperCAmelCase__       						=		image.transpose((0, 2, 3, 1)  )
										return image

				def 							A			(  self							:     int							,     _lowercase							:     Union[str, List[str]]							,     _lowercase							:     Dict							,     _lowercase							:     Optional[int]							,     _lowercase							:     bool							,     _lowercase							:     Optional[str]							,     _lowercase							:     Optional[torch.FloatTensor] = None							,     _lowercase							:     Optional[torch.FloatTensor] = None							,     ):


										'''simple docstring'''
										if prompt is not None and isinstance(_lowercase							,     _lowercase  ):
																UpperCAmelCase__       						=		1
										elif prompt is not None and isinstance(_lowercase							,     _lowercase  ):
																UpperCAmelCase__       						=		len(_lowercase  )
										else:
																UpperCAmelCase__       						=		prompt_embeds.shape[0]

										if prompt_embeds is None:
																UpperCAmelCase__       						=		self.tokenizer(
																    _lowercase							,     padding="""max_length"""							,     max_length=self.tokenizer.model_max_length							,     truncation=_lowercase							,     return_tensors="""pt"""							,     )
																UpperCAmelCase__       						=		text_inputs.input_ids
																UpperCAmelCase__       						=		self.tokenizer(_lowercase							,     padding="""longest"""							,     return_tensors="""pt"""  ).input_ids

																if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																    _lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		self.tokenizer.batch_decode(
																						    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
																						logger.warning(
																						    """The following part of your input was truncated because CLIP can only handle sequences up to"""
																						    f' {self.tokenizer.model_max_length} tokens: {removed_text}'  )

																# no positional arguments to text_encoder
																UpperCAmelCase__       						=		self.text_encoder(
																    input_ids=text_input_ids.int().to(_lowercase  )							,     )
																UpperCAmelCase__       						=		prompt_embeds[0]

										UpperCAmelCase__						,   UpperCAmelCase__						,   UpperCAmelCase__       						=		prompt_embeds.shape
										# duplicate text embeddings for each generation per prompt, using mps friendly method
										UpperCAmelCase__       						=		prompt_embeds.repeat(1							,     _lowercase  )
										UpperCAmelCase__       						=		prompt_embeds.reshape(bs_embed * num_images_per_prompt							,     _lowercase							,     -1  )

										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance and negative_prompt_embeds is None:
																UpperCAmelCase__       						=		42
																if negative_prompt is None:
																						UpperCAmelCase__       						=		[""""""] * batch_size
																elif type(_lowercase  ) is not type(_lowercase  ):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(_lowercase  )} !='
																						    f' {type(_lowercase  )}.'  )
																elif isinstance(_lowercase							,     _lowercase  ):
																						UpperCAmelCase__       						=		[negative_prompt]
																elif batch_size != len(_lowercase  ):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(_lowercase  )}, but `prompt`:'
																						    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																						    """ the batch size of `prompt`."""  )
																else:
																						UpperCAmelCase__       						=		negative_prompt

																UpperCAmelCase__       						=		text_input_ids.shape[-1]
																UpperCAmelCase__       						=		self.tokenizer(
																    _lowercase							,     padding="""max_length"""							,     max_length=_lowercase							,     truncation=_lowercase							,     return_tensors="""pt"""							,     )

																# if hasattr(uncond_input, "attention_mask"):
																#     attention_mask = uncond_input.attention_mask.to(device)
																# else:
																#     attention_mask = None

																UpperCAmelCase__       						=		self.text_encoder(
																    input_ids=uncond_input.input_ids.int().to(_lowercase  )							,     )
																UpperCAmelCase__       						=		uncond_embeddings[0]

										if do_classifier_free_guidance:
																UpperCAmelCase__       						=		uncond_embeddings.shape[1]
																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																UpperCAmelCase__       						=		uncond_embeddings.repeat(1							,     _lowercase  )
																UpperCAmelCase__       						=		uncond_embeddings.reshape(batch_size * num_images_per_prompt							,     _lowercase							,     -1  )

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																UpperCAmelCase__       						=		np.concatenate([uncond_embeddings, prompt_embeds]  )

										return prompt_embeds


"""simple docstring"""







import shutil

import requests


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      bool = False		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		"""https://api.nasa.gov/planetary/apod"""
						return requests.get(UpperCAmelCase__		,      params={"""api_key""": api_key}      ).json()


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		get_apod_data(UpperCAmelCase__      )
						UpperCAmelCase__       						=		apod_data["""url"""]
						UpperCAmelCase__       						=		img_url.split("""/"""      )[-1]
						UpperCAmelCase__       						=		requests.get(UpperCAmelCase__		,      stream=UpperCAmelCase__      )

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


def 	a__   (		UpperCAmelCase__							:      str      )->						dict:
						UpperCAmelCase__       						=		"""https://images-api.nasa.gov/search"""
						return requests.get(UpperCAmelCase__		,      params={"""q""": query}      ).json()


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

