

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


__lowerCamelCase		   =    getLogger(__name__)


__lowerCamelCase		   =    4
__lowerCamelCase		   =    7

__lowerCamelCase		   =    {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}







def 	lowerCamelCase__			(   __A						:Optional[Any]		):


       """simple docstring"""




       if isinstance(__A  ,torch.Tensor		):
              return image
       elif isinstance(__A  ,PIL.Image.Image		):
              __snake_case					  =       [image]

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

              __snake_case					  =       [np.array(i.resize((w, h)		)		)[None, :] for i in image]
              __snake_case					  =       np.concatenate(__A  ,axis=0		)
              __snake_case					  =       np.array(__A		).astype(np.floataa		) / 255.0
              __snake_case					  =       image.transpose(0  ,3  ,1  ,2		)
              __snake_case					  =       2.0 * image - 1.0
              __snake_case					  =       torch.from_numpy(__A		)
       elif isinstance(image[0]  ,torch.Tensor		):
              __snake_case					  =       torch.cat(__A  ,dim=0		)

       return image




class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       def __init__(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 3_50			,   )			->       Dict:






              """simple docstring"""


              super().__init__(
                  vae=_UpperCamelCase			,   text_encoder=_UpperCamelCase			,   tokenizer=_UpperCamelCase			,   unet=_UpperCamelCase			,   low_res_scheduler=_UpperCamelCase			,   scheduler=_UpperCamelCase			,   safety_checker=_UpperCamelCase			,   feature_extractor=_UpperCamelCase			,   watermarker=_UpperCamelCase			,   max_noise_level=_UpperCamelCase			,   )


       def __call__(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 75			,   _UpperCamelCase = 9.0			,   _UpperCamelCase = 20			,   _UpperCamelCase = None			,   _UpperCamelCase = 1			,   _UpperCamelCase = 0.0			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = "pil"			,   _UpperCamelCase = True			,   _UpperCamelCase = None			,   _UpperCamelCase = 1			,   )			->       Tuple:






              """simple docstring"""


              self.check_inputs(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )

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

              __snake_case					  =       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.
              __snake_case					  =       guidance_scale > 1.0

              # 3. Encode input prompt
              __snake_case					  =       self._encode_prompt(
                  _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   prompt_embeds=_UpperCamelCase			,   negative_prompt_embeds=_UpperCamelCase			,   )

              __snake_case					  =       ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

              # 4. Preprocess image
              __snake_case					  =       preprocess(_UpperCamelCase      )
              __snake_case					  =       image.cpu()

              # 5. set timesteps
              self.scheduler.set_timesteps(_UpperCamelCase			,   device=_UpperCamelCase      )
              __snake_case					  =       self.scheduler.timesteps

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

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

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

              # 7. Check that sizes of image and latents match
              __snake_case					  =       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
              __snake_case					  =       self.prepare_extra_step_kwargs(_UpperCamelCase			,   _UpperCamelCase      )

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

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

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

                            # timestep to tensor
                            __snake_case					  =       np.array([t]			,   dtype=_UpperCamelCase      )

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

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

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

                            # call the callback, if provided
                            if i == len(_UpperCamelCase      ) - 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(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )

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

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

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_UpperCamelCase      )


       def  a		(		self			,   _UpperCamelCase      )			->       List[Any]:






              """simple docstring"""


              __snake_case					  =       1 / 0.0_8333 * latents
              __snake_case					  =       self.vae(latent_sample=_UpperCamelCase      )[0]
              __snake_case					  =       np.clip(image / 2 + 0.5			,   0			,   1      )
              __snake_case					  =       image.transpose((0, 2, 3, 1)      )
              return image






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   )			->       Tuple:






              """simple docstring"""


              if prompt is not None and isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                     __snake_case					  =       1
              elif prompt is not None and isinstance(_UpperCamelCase			,   _UpperCamelCase      ):
                     __snake_case					  =       len(_UpperCamelCase      )
              else:
                     __snake_case					  =       prompt_embeds.shape[0]

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

                     if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                         _UpperCamelCase			,   _UpperCamelCase      ):
                            __snake_case					  =       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
                     __snake_case					  =       self.text_encoder(
                         input_ids=text_input_ids.int().to(_UpperCamelCase      )			,   )
                     __snake_case					  =       prompt_embeds[0]

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

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

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

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

                     __snake_case					  =       self.text_encoder(
                         input_ids=uncond_input.input_ids.int().to(_UpperCamelCase      )			,   )
                     __snake_case					  =       uncond_embeddings[0]

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

              return prompt_embeds




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase__		   =    {'''UserAgent''': UserAgent().random}
UpperCamelCase__		   =    '''https://www.mywaifulist.moe/random'''







def 	lowerCamelCase__			(   __A						:str  ,__A						:str		):


       """simple docstring"""




       __snake_case					  =       requests.get(__A  ,headers=__A		)
       with open(__A  ,"""wb"""		) as file:
              file.write(image.content		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       BeautifulSoup(requests.get(__A  ,headers=__A		).text  ,"""html.parser"""		)
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:title"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:image"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""p"""  ,id="""description"""		).get_text()
       __snake_case  ,						__snake_case					  =       os.path.splitext(os.path.basename(__A		)		)
       __snake_case					  =       title.strip().replace(""" """  ,"""_"""		)
       __snake_case					  =       F'{image_title}{image_extension}'
       save_image(__A  ,__A		)
       return (title, description, image_title)


if __name__ == "__main__":
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__		   =    random_anime_character()
    print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')

