


'''simple docstring'''







import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput


class 				__lowerCamelCase				( nn.Module ):
    """simple docstring"""


    def __init__(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Any=1.0):
           super().__init__()

           _A  :			Tuple											=      cut_size
           _A  :			Tuple											=      cut_power


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]):
           _A						,				_A  :			Union[str, Any]											=      pixel_values.shape[2:4]
           _A  :			Dict											=      min(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
           _A  :			int											=      min(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							self.cut_size)
           _A  :			Any											=      []
           for _ in range(SCREAMING_SNAKE_CASE):
                  _A  :			List[Any]											=      int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
                  _A  :			Optional[int]											=      torch.randint(0   ,							sideX - size + 1   ,							())
                  _A  :			Optional[Any]											=      torch.randint(0   ,							sideY - size + 1   ,							())
                  _A  :			Tuple											=      pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                  cutouts.append(F.adaptive_avg_poolad(SCREAMING_SNAKE_CASE   ,							self.cut_size))
           return torch.cat(SCREAMING_SNAKE_CASE)






def    lowerCAmelCase__ (       lowerCamelCase  :    str						,lowerCamelCase  :    Optional[int]     ):
       _A  :			Optional[int]											=      F.normalize(__snake_case						,dim=-1     )
       _A  :			int											=      F.normalize(__snake_case						,dim=-1     )
       return (x - y).norm(dim=-1     ).div(2     ).arcsin().pow(2     ).mul(2     )






def    lowerCAmelCase__ (       lowerCamelCase  :    str						,lowerCamelCase  :    Optional[Any]     ):
       for param in model.parameters():
              _A  :			str											=      value


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""


    def __init__(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Any   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							):
           super().__init__()
           self.register_modules(
               vae=SCREAMING_SNAKE_CASE   ,							text_encoder=SCREAMING_SNAKE_CASE   ,							clip_model=SCREAMING_SNAKE_CASE   ,							tokenizer=SCREAMING_SNAKE_CASE   ,							unet=SCREAMING_SNAKE_CASE   ,							scheduler=SCREAMING_SNAKE_CASE   ,							feature_extractor=SCREAMING_SNAKE_CASE   ,							)

           _A  :			int											=      transforms.Normalize(mean=feature_extractor.image_mean   ,							std=feature_extractor.image_std)
           _A  :			Optional[Any]											=      (
               feature_extractor.size
               if isinstance(feature_extractor.size   ,							SCREAMING_SNAKE_CASE)
               else feature_extractor.size['shortest_edge']
           )
           _A  :			List[Any]											=      MakeCutouts(self.cut_out_size)

           set_requires_grad(self.text_encoder   ,							SCREAMING_SNAKE_CASE)
           set_requires_grad(self.clip_model   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any] = "auto"):







           if slice_size == "auto":
                  # half the attention head size is usually a good trade-off between
                  # speed and memory
                  _A  :			Union[str, Any]											=      self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str):
           self.enable_attention_slicing(SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str):
           set_requires_grad(self.vae   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Dict):
           set_requires_grad(self.vae   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							str):
           set_requires_grad(self.unet   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]):
           set_requires_grad(self.unet   ,							SCREAMING_SNAKE_CASE)


    @torch.enable_grad()
    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Dict=True   ,							):
           _A  :			Optional[Any]											=      latents.detach().requires_grad_()

           _A  :			int											=      self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

           # predict the noise residual
           _A  :			Tuple											=      self.unet(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							encoder_hidden_states=SCREAMING_SNAKE_CASE).sample

           if isinstance(self.scheduler   ,							(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
                  _A  :			Tuple											=      self.scheduler.alphas_cumprod[timestep]
                  _A  :			str											=      1 - alpha_prod_t
                  # compute predicted original sample from predicted noise also called
                  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                  _A  :			Dict											=      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                  _A  :			Optional[int]											=      torch.sqrt(SCREAMING_SNAKE_CASE)
                  _A  :			Optional[Any]											=      pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler   ,							SCREAMING_SNAKE_CASE):
                  _A  :			Union[str, Any]											=      self.scheduler.sigmas[index]
                  _A  :			Tuple											=      latents - sigma * noise_pred
           else:
                  raise ValueError(F'scheduler type {type(self.scheduler)} not supported')

           _A  :			List[Any]											=      1 / self.vae.config.scaling_factor * sample
           _A  :			Optional[Any]											=      self.vae.decode(SCREAMING_SNAKE_CASE).sample
           _A  :			Union[str, Any]											=      (image / 2 + 0.5).clamp(0   ,							1)

           if use_cutouts:
                  _A  :			str											=      self.make_cutouts(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
           else:
                  _A  :			List[Any]											=      transforms.Resize(self.cut_out_size)(SCREAMING_SNAKE_CASE)
           _A  :			Any											=      self.normalize(SCREAMING_SNAKE_CASE).to(latents.dtype)

           _A  :			Any											=      self.clip_model.get_image_features(SCREAMING_SNAKE_CASE)
           _A  :			int											=      image_embeddings_clip / image_embeddings_clip.norm(p=2   ,							dim=-1   ,							keepdim=SCREAMING_SNAKE_CASE)

           if use_cutouts:
                  _A  :			str											=      spherical_dist_loss(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                  _A  :			Dict											=      dists.view([num_cutouts, sample.shape[0], -1])
                  _A  :			Optional[Any]											=      dists.sum(2).mean(0).sum() * clip_guidance_scale
           else:
                  _A  :			Tuple											=      spherical_dist_loss(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE).mean() * clip_guidance_scale

           _A  :			str											=      -torch.autograd.grad(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)[0]

           if isinstance(self.scheduler   ,							SCREAMING_SNAKE_CASE):
                  _A  :			str											=      latents.detach() + grads * (sigma**2)
                  _A  :			Optional[int]											=      noise_pred_original
           else:
                  _A  :			str											=      noise_pred_original - torch.sqrt(SCREAMING_SNAKE_CASE) * grads
           return noise_pred, latents


    @torch.no_grad()
    def __call__(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Any = 512   ,							SCREAMING_SNAKE_CASE    :							Dict = 512   ,							SCREAMING_SNAKE_CASE    :							List[str] = 50   ,							SCREAMING_SNAKE_CASE    :							str = 7.5   ,							SCREAMING_SNAKE_CASE    :							Optional[Any] = 1   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any] = 0.0   ,							SCREAMING_SNAKE_CASE    :							Dict = 100   ,							SCREAMING_SNAKE_CASE    :							Any = None   ,							SCREAMING_SNAKE_CASE    :							Dict = 4   ,							SCREAMING_SNAKE_CASE    :							Tuple = True   ,							SCREAMING_SNAKE_CASE    :							int = None   ,							SCREAMING_SNAKE_CASE    :							int = None   ,							SCREAMING_SNAKE_CASE    :							Tuple = "pil"   ,							SCREAMING_SNAKE_CASE    :							List[str] = True   ,							):







           if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  _A  :			Optional[int]											=      1
           elif isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  _A  :			Optional[Any]											=      len(SCREAMING_SNAKE_CASE)
           else:
                  raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE)}')

           if height % 8 != 0 or width % 8 != 0:
                  raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

           # get prompt text embeddings
           _A  :			int											=      self.tokenizer(
               SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							max_length=self.tokenizer.model_max_length   ,							truncation=SCREAMING_SNAKE_CASE   ,							return_tensors='pt'   ,							)
           _A  :			Dict											=      self.text_encoder(text_input.input_ids.to(self.device))[0]
           # duplicate text embeddings for each generation per prompt
           _A  :			Dict											=      text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE   ,							dim=0)

           if clip_guidance_scale > 0:
                  if clip_prompt is not None:
                         _A  :			Optional[Any]											=      self.tokenizer(
                             SCREAMING_SNAKE_CASE   ,							padding='max_length'   ,							max_length=self.tokenizer.model_max_length   ,							truncation=SCREAMING_SNAKE_CASE   ,							return_tensors='pt'   ,							).input_ids.to(self.device)
                  else:
                         _A  :			Union[str, Any]											=      text_input.input_ids.to(self.device)
                  _A  :			Union[str, Any]											=      self.clip_model.get_text_features(SCREAMING_SNAKE_CASE)
                  _A  :			Dict											=      text_embeddings_clip / text_embeddings_clip.norm(p=2   ,							dim=-1   ,							keepdim=SCREAMING_SNAKE_CASE)
                  # duplicate text embeddings clip for each generation per prompt
                  _A  :			int											=      text_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE   ,							dim=0)

           # 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.
           _A  :			Any											=      guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                  _A  :			Tuple											=      text_input.input_ids.shape[-1]
                  _A  :			Union[str, Any]											=      self.tokenizer(['']   ,							padding='max_length'   ,							max_length=SCREAMING_SNAKE_CASE   ,							return_tensors='pt')
                  _A  :			Optional[Any]											=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]
                  # duplicate unconditional embeddings for each generation per prompt
                  _A  :			Tuple											=      uncond_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE   ,							dim=0)

                  # 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
                  _A  :			Tuple											=      torch.cat([uncond_embeddings, text_embeddings])

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           _A  :			Dict											=      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           _A  :			Union[str, Any]											=      text_embeddings.dtype
           if latents is None:
                  if self.device.type == "mps":
                         # randn does not work reproducibly on mps
                         _A  :			int											=      torch.randn(SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE   ,							device='cpu'   ,							dtype=SCREAMING_SNAKE_CASE).to(
                             self.device)
                  else:
                         _A  :			Any											=      torch.randn(SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE   ,							device=self.device   ,							dtype=SCREAMING_SNAKE_CASE)
           else:
                  if latents.shape != latents_shape:
                         raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
                  _A  :			str											=      latents.to(self.device)

           # set timesteps
           _A  :			Optional[int]											=      'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
           _A  :			Any											=      {}
           if accepts_offset:
                  _A  :			List[str]											=      1

           self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           _A  :			str											=      self.scheduler.timesteps.to(self.device)

           # scale the initial noise by the standard deviation required by the scheduler
           _A  :			Any											=      latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           _A  :			str											=      'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
           _A  :			List[str]											=      {}
           if accepts_eta:
                  _A  :			Union[str, Any]											=      eta

           # check if the scheduler accepts generator
           _A  :			str											=      'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
           if accepts_generator:
                  _A  :			List[str]											=      generator

           for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE)):
                  # expand the latents if we are doing classifier free guidance
                  _A  :			List[Any]											=      torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                  _A  :			List[Any]											=      self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  # predict the noise residual
                  _A  :			Any											=      self.unet(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							encoder_hidden_states=SCREAMING_SNAKE_CASE).sample

                  # perform classifier free guidance
                  if do_classifier_free_guidance:
                         _A						,				_A  :			Union[str, Any]											=      noise_pred.chunk(2)
                         _A  :			Dict											=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                  # perform clip guidance
                  if clip_guidance_scale > 0:
                         _A  :			str											=      (
                             text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                         )
                         _A						,				_A  :			int											=      self.cond_fn(
                             SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							)

                  # compute the previous noisy sample x_t -> x_t-1
                  _A  :			Dict											=      self.scheduler.step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE).prev_sample

           # scale and decode the image latents with vae
           _A  :			List[Any]											=      1 / self.vae.config.scaling_factor * latents
           _A  :			Optional[int]											=      self.vae.decode(SCREAMING_SNAKE_CASE).sample

           _A  :			Tuple											=      (image / 2 + 0.5).clamp(0   ,							1)
           _A  :			Tuple											=      image.cpu().permute(0   ,							2   ,							3   ,							1).numpy()

           if output_type == "pil":
                  _A  :			Optional[int]											=      self.numpy_to_pil(SCREAMING_SNAKE_CASE)

           if not return_dict:
                  return (image, None)

           return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE   ,							nsfw_content_detected=SCREAMING_SNAKE_CASE)










'''simple docstring'''







from unittest.mock import patch

import datasets
from datasets import Dataset






def    lowerCAmelCase__ (       ):
       _A  :			Optional[int]											=      Dataset.from_dict({'col_1': [3, 2, 0, 1]}     )

       with patch('tqdm.auto.tqdm'     ) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_called()






