






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


snake_case     					=		logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     def __init__(					self							:						Any       ,			UpperCAmelCase_							:						AutoencoderKL       ,			UpperCAmelCase_							:						CLIPTextModel       ,			UpperCAmelCase_							:						CLIPTokenizer       ,			UpperCAmelCase_							:						UNetaDConditionModel       ,			UpperCAmelCase_							:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,			UpperCAmelCase_							:						StableDiffusionSafetyChecker       ,			UpperCAmelCase_							:						CLIPImageProcessor       ,			):
       super().__init__()
       self.register_modules(
           vae=UpperCAmelCase_       ,			text_encoder=UpperCAmelCase_       ,			tokenizer=UpperCAmelCase_       ,			unet=UpperCAmelCase_       ,			scheduler=UpperCAmelCase_       ,			safety_checker=UpperCAmelCase_       ,			feature_extractor=UpperCAmelCase_       ,			)


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


     def 					_A							(					self							:						str   ):
       self.enable_attention_slicing(UpperCAmelCase_   )






     @torch.no_grad()
     def __call__(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Union[str, List[str]]       ,			UpperCAmelCase_							:						int = 512       ,			UpperCAmelCase_							:						int = 512       ,			UpperCAmelCase_							:						int = 50       ,			UpperCAmelCase_							:						float = 7.5       ,			UpperCAmelCase_							:						Optional[Union[str, List[str]]] = None       ,			UpperCAmelCase_							:						Optional[int] = 1       ,			UpperCAmelCase_							:						float = 0.0       ,			UpperCAmelCase_							:						Optional[torch.Generator] = None       ,			UpperCAmelCase_							:						Optional[torch.FloatTensor] = None       ,			UpperCAmelCase_							:						Optional[str] = "pil"       ,			UpperCAmelCase_							:						bool = True       ,			UpperCAmelCase_							:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,			UpperCAmelCase_							:						int = 1       ,			UpperCAmelCase_							:						Optional[torch.FloatTensor] = None       ,			**UpperCAmelCase_							:						Dict       ,			):

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

       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}.'''   )

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ) or callback_steps <= 0)
       ):
         raise ValueError(
             f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
             f''' {type(UpperCAmelCase_   )}.'''   )

       # get prompt text embeddings
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.tokenizer(
           UpperCAmelCase_       ,			padding="max_length"       ,			max_length=self.tokenizer.model_max_length       ,			return_tensors="pt"       ,			)
       SCREAMING_SNAKE_CASE							:   List[str]						    =     text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
         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}'''   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     text_input_ids[:, : self.tokenizer.model_max_length]

       if text_embeddings is None:
         SCREAMING_SNAKE_CASE							:   int						    =     self.text_encoder(text_input_ids.to(self.device   )   )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     text_embeddings.shape
       SCREAMING_SNAKE_CASE							:   str						    =     text_embeddings.repeat(1       ,			UpperCAmelCase_       ,			1   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     text_embeddings.view(bs_embed * num_images_per_prompt       ,			UpperCAmelCase_       ,			-1   )

       # 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.
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
         SCREAMING_SNAKE_CASE							:   List[str]
         if negative_prompt is None:
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     [""]
         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_   ):
           SCREAMING_SNAKE_CASE							:   str						    =     [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:
           SCREAMING_SNAKE_CASE							:   Tuple						    =     negative_prompt

         SCREAMING_SNAKE_CASE							:   Any						    =     text_input_ids.shape[-1]
         SCREAMING_SNAKE_CASE							:   Dict						    =     self.tokenizer(
             UpperCAmelCase_       ,			padding="max_length"       ,			max_length=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			return_tensors="pt"       ,			)
         SCREAMING_SNAKE_CASE							:   Dict						    =     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     uncond_embeddings.shape[1]
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     uncond_embeddings.repeat(UpperCAmelCase_       ,			UpperCAmelCase_       ,			1   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     uncond_embeddings.view(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
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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`.
       SCREAMING_SNAKE_CASE							:   str						    =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     text_embeddings.dtype
       if latents is None:
         if self.device.type == "mps":
           # randn does not exist on mps
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.randn(
               UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			device="cpu"       ,			dtype=UpperCAmelCase_   ).to(self.device   )
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.randn(UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			device="cpu"       ,			dtype=UpperCAmelCase_   ).to(
               self.device   )
         else:
           SCREAMING_SNAKE_CASE							:   Any						    =     torch.randn(
               UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			device=self.device       ,			dtype=UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   Any						    =     torch.randn(UpperCAmelCase_       ,			generator=UpperCAmelCase_       ,			device=self.device       ,			dtype=UpperCAmelCase_   )
       else:
         if latents_reference.shape != latents_shape:
           raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
         SCREAMING_SNAKE_CASE							:   str						    =     latents_reference.to(self.device   )
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     latents.to(self.device   )

       # This is the key part of the pipeline where we
       # try to ensure that the generated images w/ the same seed
       # but different sizes actually result in similar images
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (latents_shape[3] - latents_shape_reference[3]) // 2
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     (latents_shape[2] - latents_shape_reference[2]) // 2
       SCREAMING_SNAKE_CASE							:   int						    =     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     0 if dx < 0 else dx
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     0 if dy < 0 else dy
       SCREAMING_SNAKE_CASE							:   Tuple						    =     max(-dx       ,			0   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     max(-dy       ,			0   )
       # import pdb
       # pdb.set_trace()
       SCREAMING_SNAKE_CASE							:   Dict						    =     latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(UpperCAmelCase_   )

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

       # scale the initial noise by the standard deviation required by the scheduler
       SCREAMING_SNAKE_CASE							:   List[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]
       SCREAMING_SNAKE_CASE							:   Dict						    =     "eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {}
       if accepts_eta:
         SCREAMING_SNAKE_CASE							:   Dict						    =     eta

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

         # predict the noise residual
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.unet(UpperCAmelCase_       ,			UpperCAmelCase_       ,			encoder_hidden_states=UpperCAmelCase_   ).sample

         # perform guidance
         if do_classifier_free_guidance:
           SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Any						    =     noise_pred.chunk(2   )
           SCREAMING_SNAKE_CASE							:   str						    =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

         # call the callback, if provided
         if callback is not None and i % callback_steps == 0:
           callback(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   str						    =     1 / 0.18_215 * latents
       SCREAMING_SNAKE_CASE							:   Any						    =     self.vae.decode(UpperCAmelCase_   ).sample

       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     (image / 2 + 0.5).clamp(0       ,			1   )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       SCREAMING_SNAKE_CASE							:   int						    =     image.cpu().permute(0       ,			2       ,			3       ,			1   ).float().numpy()

       if self.safety_checker is not None:
         SCREAMING_SNAKE_CASE							:   Any						    =     self.feature_extractor(self.numpy_to_pil(UpperCAmelCase_   )       ,			return_tensors="pt"   ).to(
             self.device   )
         SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   List[str]						    =     self.safety_checker(
             images=UpperCAmelCase_       ,			clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype   )   )
       else:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     None

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

       if not return_dict:
         return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=UpperCAmelCase_       ,			nsfw_content_detected=UpperCAmelCase_   )










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

