




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu





class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[str]   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      @property
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						1
             __lowerCamelCase   =						3
             __lowerCamelCase   =						(32, 32)

             __lowerCamelCase   =						floats_tensor((batch_size, num_channels) + sizes    ,       rng=random.Random(0   )   ).to(UpperCamelCase_   )
             return image
      @property
      def 			lowerCAmelCase__						(				self: List[str]   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						UNetaDConditionModel(
                 block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=4    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       )
             return model
      @property
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						AutoencoderKL(
                 block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       )
             return model
      @property
      def 			lowerCAmelCase__						(				self: Any   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						CLIPTextConfig(
                 bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1E-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=10_00    ,       )
             return CLIPTextModel(UpperCamelCase_   )
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             def extract(*UpperCamelCase_: Tuple    ,       **UpperCamelCase_: Union[str, Any]   ):





                    class     lowerCamelCase__:
                          def __init__(				self: Dict   ):
                                 __lowerCamelCase   =						torch.ones([0]   )




                          def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: List[Any]   ):
                                 self.pixel_values.to(UpperCamelCase_   )
                                 return self



                    return Out()

             return extract
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.dummy_cond_unet
             __lowerCamelCase   =						DDIMScheduler(
                 beta_start=0.0_0085    ,       beta_end=0.012    ,       beta_schedule="""scaled_linear"""    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       )

             __lowerCamelCase   =						self.dummy_vae
             __lowerCamelCase   =						self.dummy_text_encoder
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             # make sure here that pndm scheduler skips prk
             __lowerCamelCase   =						StableDiffusionPipeline(
                 unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       vae=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       feature_extractor=self.dummy_extractor    ,       )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						"""A painting of a squirrel eating a burger"""

             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __lowerCamelCase   =						sd_pipe([prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=6.0    ,       num_inference_steps=2    ,       output_type="""np"""   )
             __lowerCamelCase   =						output.images

             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=6.0    ,       num_inference_steps=2    ,       output_type="""np"""    ,       return_dict=UpperCamelCase_    ,       )[0]

             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __lowerCamelCase   =						np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]   )

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.dummy_cond_unet
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             __lowerCamelCase   =						self.dummy_vae
             __lowerCamelCase   =						self.dummy_text_encoder
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             # make sure here that pndm scheduler skips prk
             __lowerCamelCase   =						StableDiffusionPipeline(
                 unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       vae=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       feature_extractor=self.dummy_extractor    ,       )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						"""A painting of a squirrel eating a burger"""
             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __lowerCamelCase   =						sd_pipe([prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=6.0    ,       num_inference_steps=2    ,       output_type="""np"""   )

             __lowerCamelCase   =						output.images

             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=6.0    ,       num_inference_steps=2    ,       output_type="""np"""    ,       return_dict=UpperCamelCase_    ,       )[0]

             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __lowerCamelCase   =						np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]   )

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""    ,       safety_checker=UpperCamelCase_   )
             assert isinstance(UpperCamelCase_    ,       UpperCamelCase_   )
             assert isinstance(pipe.scheduler    ,       UpperCamelCase_   )
             assert pipe.safety_checker is None

             __lowerCamelCase   =						pipe("""example prompt"""    ,       num_inference_steps=2   ).images[0]
             assert image is not None

             # check that there's no error when saving a pipeline with one of the models being None
             with tempfile.TemporaryDirectory() as tmpdirname:
                    pipe.save_pretrained(UpperCamelCase_   )
                    __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained(UpperCamelCase_   )

             # sanity check that the pipeline still works
             assert pipe.safety_checker is None
             __lowerCamelCase   =						pipe("""example prompt"""    ,       num_inference_steps=2   ).images[0]
             assert image is not None




      @unittest.skipIf(torch_device != """cuda"""    ,       """This test requires a GPU"""   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.dummy_cond_unet
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             __lowerCamelCase   =						self.dummy_vae
             __lowerCamelCase   =						self.dummy_text_encoder
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             # put models in fp16
             __lowerCamelCase   =						unet.half()
             __lowerCamelCase   =						vae.half()
             __lowerCamelCase   =						bert.half()

             # make sure here that pndm scheduler skips prk
             __lowerCamelCase   =						StableDiffusionPipeline(
                 unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       vae=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       feature_extractor=self.dummy_extractor    ,       )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						"""A painting of a squirrel eating a burger"""
             __lowerCamelCase   =						sd_pipe([prompt]    ,       num_inference_steps=2    ,       output_type="""np"""   ).images

             assert image.shape == (1, 64, 64, 3)





@nightly
@require_torch_gpu
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: Any   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    ,       safety_checker=UpperCamelCase_   )
             __lowerCamelCase   =						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						(
                 """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
                 """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
                 """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
                 """ children from bahnhof zoo, detailed """
             )
             __lowerCamelCase   =						40_03_66_03_46
             __lowerCamelCase   =						7

             # without safety guidance (sld_guidance_scale = 0)
             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=UpperCamelCase_    ,       num_inference_steps=50    ,       output_type="""np"""    ,       width=5_12    ,       height=5_12    ,       sld_guidance_scale=0    ,       )

             __lowerCamelCase   =						output.images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

             assert image.shape == (1, 5_12, 5_12, 3)

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

             # without safety guidance (strong configuration)
             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=UpperCamelCase_    ,       num_inference_steps=50    ,       output_type="""np"""    ,       width=5_12    ,       height=5_12    ,       sld_guidance_scale=20_00    ,       sld_warmup_steps=7    ,       sld_threshold=0.025    ,       sld_momentum_scale=0.5    ,       sld_mom_beta=0.7    ,       )

             __lowerCamelCase   =						output.images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

             assert image.shape == (1, 5_12, 5_12, 3)

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    ,       safety_checker=UpperCamelCase_   )
             __lowerCamelCase   =						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						"""padme amidala taking a bath artwork, safe for work, no nudity"""
             __lowerCamelCase   =						27_34_97_17_55
             __lowerCamelCase   =						7

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=UpperCamelCase_    ,       num_inference_steps=50    ,       output_type="""np"""    ,       width=5_12    ,       height=5_12    ,       sld_guidance_scale=0    ,       )

             __lowerCamelCase   =						output.images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

             assert image.shape == (1, 5_12, 5_12, 3)

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

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=UpperCamelCase_    ,       num_inference_steps=50    ,       output_type="""np"""    ,       width=5_12    ,       height=5_12    ,       sld_guidance_scale=20_00    ,       sld_warmup_steps=7    ,       sld_threshold=0.025    ,       sld_momentum_scale=0.5    ,       sld_mom_beta=0.7    ,       )

             __lowerCamelCase   =						output.images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

             assert image.shape == (1, 5_12, 5_12, 3)

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




      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						(
                 """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
                 """ leyendecker"""
             )
             __lowerCamelCase   =						10_44_35_52_34
             __lowerCamelCase   =						12

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=UpperCamelCase_    ,       num_inference_steps=50    ,       output_type="""np"""    ,       width=5_12    ,       height=5_12    ,       sld_guidance_scale=0    ,       )

             __lowerCamelCase   =						output.images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   )

             assert image.shape == (1, 5_12, 5_12, 3)

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-7

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=UpperCamelCase_    ,       num_inference_steps=50    ,       output_type="""np"""    ,       width=5_12    ,       height=5_12    ,       sld_guidance_scale=20_00    ,       sld_warmup_steps=7    ,       sld_threshold=0.025    ,       sld_momentum_scale=0.5    ,       sld_mom_beta=0.7    ,       )

             __lowerCamelCase   =						output.images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]
             __lowerCamelCase   =						np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]   )
             assert image.shape == (1, 5_12, 5_12, 3)

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







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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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





class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase):
      @register_to_config
      def __init__(				self: Optional[Any]    ,       UpperCamelCase_: bool    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None   ):
             super().__init__()

             __lowerCamelCase   =						learnable

             if self.learnable:
                    assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                    assert length is not None, "learnable=True requires `length` to be set"

                    __lowerCamelCase   =						torch.zeros(UpperCamelCase_    ,       UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						None

             __lowerCamelCase   =						torch.nn.Parameter(UpperCamelCase_   )





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	VQModel
      UpperCAmelCase__      :	CLIPTextModel
      UpperCAmelCase__      :	CLIPTokenizer
      UpperCAmelCase__      :	TransformeraDModel
      UpperCAmelCase__      :	LearnedClassifierFreeSamplingEmbeddings
      UpperCAmelCase__      :	VQDiffusionScheduler
      def __init__(				self: str    ,       UpperCamelCase_: VQModel    ,       UpperCamelCase_: CLIPTextModel    ,       UpperCamelCase_: CLIPTokenizer    ,       UpperCamelCase_: TransformeraDModel    ,       UpperCamelCase_: VQDiffusionScheduler    ,       UpperCamelCase_: LearnedClassifierFreeSamplingEmbeddings    ,       ):
             super().__init__()

             self.register_modules(
                 vqvae=UpperCamelCase_    ,       transformer=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       learned_classifier_free_sampling_embeddings=UpperCamelCase_    ,       )
      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						len(UpperCamelCase_   ) if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ) else 1

             # get prompt text embeddings
             __lowerCamelCase   =						self.tokenizer(
                 UpperCamelCase_    ,       padding="""max_length"""    ,       max_length=self.tokenizer.model_max_length    ,       return_tensors="""pt"""    ,       )
             __lowerCamelCase   =						text_inputs.input_ids

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                    __lowerCamelCase   =						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}'   )
                    __lowerCamelCase   =						text_input_ids[:, : self.tokenizer.model_max_length]
             __lowerCamelCase   =						self.text_encoder(text_input_ids.to(self.device   )   )[0]

             # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
             # While CLIP does normalize the pooled output of the text transformer when combining
             # the image and text embeddings, CLIP does not directly normalize the last hidden state.
             #
             # CLIP normalizing the pooled output.
             # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
             __lowerCamelCase   =						prompt_embeds / prompt_embeds.norm(dim=-1    ,       keepdim=UpperCamelCase_   )

             # duplicate text embeddings for each generation per prompt
             __lowerCamelCase   =						prompt_embeds.repeat_interleave(UpperCamelCase_    ,       dim=0   )

             if do_classifier_free_guidance:
                    if self.learned_classifier_free_sampling_embeddings.learnable:
                           __lowerCamelCase   =						self.learned_classifier_free_sampling_embeddings.embeddings
                           __lowerCamelCase   =						negative_prompt_embeds.unsqueeze(0   ).repeat(UpperCamelCase_    ,       1    ,       1   )
                    else:
                           __lowerCamelCase   =						[""""""] * batch_size

                           __lowerCamelCase   =						text_input_ids.shape[-1]
                           __lowerCamelCase   =						self.tokenizer(
                               UpperCamelCase_    ,       padding="""max_length"""    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       return_tensors="""pt"""    ,       )
                           __lowerCamelCase   =						self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
                           # See comment for normalizing text embeddings
                           __lowerCamelCase   =						negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1    ,       keepdim=UpperCamelCase_   )

                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                    __lowerCamelCase   =						negative_prompt_embeds.shape[1]
                    __lowerCamelCase   =						negative_prompt_embeds.repeat(1    ,       UpperCamelCase_    ,       1   )
                    __lowerCamelCase   =						negative_prompt_embeds.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
                    __lowerCamelCase   =						torch.cat([negative_prompt_embeds, prompt_embeds]   )

             return prompt_embeds
      @torch.no_grad()
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[str, List[str]]    ,       UpperCamelCase_: int = 1_00    ,       UpperCamelCase_: float = 5.0    ,       UpperCamelCase_: float = 1.0    ,       UpperCamelCase_: int = 1    ,       UpperCamelCase_: Optional[Union[torch.Generator, List[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    ,       ):
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						1
             elif isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						len(UpperCamelCase_   )
             else:
                    raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_   )}'   )

             __lowerCamelCase   =						batch_size * num_images_per_prompt

             __lowerCamelCase   =						guidance_scale > 1.0

             __lowerCamelCase   =						self._encode_prompt(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )

             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 the initial completely masked latents unless the user supplied it

             __lowerCamelCase   =						(batch_size, self.transformer.num_latent_pixels)
             if latents is None:
                    __lowerCamelCase   =						self.transformer.num_vector_embeds - 1
                    __lowerCamelCase   =						torch.full(UpperCamelCase_    ,       UpperCamelCase_   ).to(self.device   )
             else:
                    if latents.shape != latents_shape:
                           raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
                    if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                           raise ValueError(
                               """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
                               F' {self.transformer.num_vector_embeds - 1} (inclusive).'   )
                    __lowerCamelCase   =						latents.to(self.device   )

             # set timesteps
             self.scheduler.set_timesteps(UpperCamelCase_    ,       device=self.device   )

             __lowerCamelCase   =						self.scheduler.timesteps.to(self.device   )

             __lowerCamelCase   =						latents

             for i, t in enumerate(self.progress_bar(UpperCamelCase_   )   ):
                    # expand the sample if we are doing classifier free guidance
                    __lowerCamelCase   =						torch.cat([sample] * 2   ) if do_classifier_free_guidance else sample

                    # predict the un-noised image
                    # model_output == `log_p_x_0`
                    __lowerCamelCase   =						self.transformer(UpperCamelCase_    ,       encoder_hidden_states=UpperCamelCase_    ,       timestep=UpperCamelCase_   ).sample

                    if do_classifier_free_guidance:
                           __lowerCamelCase,   __lowerCamelCase   =						model_output.chunk(2   )
                           __lowerCamelCase   =						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                           model_output -= torch.logsumexp(UpperCamelCase_    ,       dim=1    ,       keepdim=UpperCamelCase_   )

                    __lowerCamelCase   =						self.truncate(UpperCamelCase_    ,       UpperCamelCase_   )

                    # remove `log(0)`'s (`-inf`s)
                    __lowerCamelCase   =						model_output.clamp(-70   )

                    # compute the previous noisy sample x_t -> x_t-1
                    __lowerCamelCase   =						self.scheduler.step(UpperCamelCase_    ,       timestep=UpperCamelCase_    ,       sample=UpperCamelCase_    ,       generator=UpperCamelCase_   ).prev_sample

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

             __lowerCamelCase   =						self.vqvae.config.vq_embed_dim
             __lowerCamelCase   =						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
             __lowerCamelCase   =						self.vqvae.quantize.get_codebook_entry(UpperCamelCase_    ,       shape=UpperCamelCase_   )
             __lowerCamelCase   =						self.vqvae.decode(UpperCamelCase_    ,       force_not_quantize=UpperCamelCase_   ).sample

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

             if output_type == "pil":
                    __lowerCamelCase   =						self.numpy_to_pil(UpperCamelCase_   )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: torch.FloatTensor    ,       UpperCamelCase_: float   ):
             __lowerCamelCase,   __lowerCamelCase   =						torch.sort(UpperCamelCase_    ,       1    ,       descending=UpperCamelCase_   )
             __lowerCamelCase   =						torch.exp(UpperCamelCase_   )
             __lowerCamelCase   =						sorted_p_x_0.cumsum(dim=1   ) < truncation_rate

             # Ensure that at least the largest probability is not zeroed out
             __lowerCamelCase   =						torch.full_like(keep_mask[:, 0:1, :]    ,       UpperCamelCase_   )
             __lowerCamelCase   =						torch.cat((all_true, keep_mask)    ,       dim=1   )
             __lowerCamelCase   =						keep_mask[:, :-1, :]

             __lowerCamelCase   =						keep_mask.gather(1    ,       indices.argsort(1   )   )

             __lowerCamelCase   =						log_p_x_0.clone()

             __lowerCamelCase   =						-torch.inf  # -inf = log(0)

             return rv

