






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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__lowerCamelCase	:		List[str]										=   logging.get_logger(__name__)  # pylint: disable=invalid-name







class  _lowercase   (   _A						):
      def __init__(     self				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			a				,			):
       super().__init__()

       if safety_checker is None:
        logger.warning(
            F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
            """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
            """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
            """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
            """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
            """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""   )

       self.register_modules(
           speech_model=a				,			speech_processor=a				,			vae=a				,			text_encoder=a				,			tokenizer=a				,			unet=a				,			scheduler=a				,			feature_extractor=a				,			)
      def 						lowercase__					(     self				,			a = "auto"   ):
       if slice_size == "auto":
        snake_case__  :    Tuple							       =self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(a   )
      def 						lowercase__					(     self   ):
       self.enable_attention_slicing(a   )



      @torch.no_grad()
      def __call__(     self				,			a				,			a=1_6_0_0_0				,			a = 5_1_2				,			a = 5_1_2				,			a = 5_0				,			a = 7.5				,			a = None				,			a = 1				,			a = 0.0				,			a = None				,			a = None				,			a = "pil"				,			a = True				,			a = None				,			a = 1				,			**a				,			):
       snake_case__  :    Dict							       =self.speech_processor.feature_extractor(
           a				,			return_tensors="""pt"""				,			sampling_rate=a   ).input_features.to(self.device   )
       snake_case__  :    Optional[Any]							       =self.speech_model.generate(a				,			max_length=4_8_0_0_0_0   )

       snake_case__  :    Optional[int]							       =self.speech_processor.tokenizer.batch_decode(a				,			skip_special_tokens=a				,			normalize=a   )[
           0
       ]

       if isinstance(a				,			a   ):
        snake_case__  :    Dict							       =1
       elif isinstance(a				,			a   ):
        snake_case__  :    Any							       =len(a   )
       else:
        raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(a   )}"   )

       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(a				,			a   ) or callback_steps <= 0)
       ):
        raise ValueError(
            F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
            F" {type(a   )}."   )

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

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        snake_case__  :    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}"   )
        snake_case__  :    Any							       =text_input_ids[:, : self.tokenizer.model_max_length]
       snake_case__  :    List[str]							       =self.text_encoder(text_input_ids.to(self.device   )   )[0]

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       snake_case__       ,   snake_case__       ,   snake_case__  :    List[Any]							       =text_embeddings.shape
       snake_case__  :    Union[str, Any]							       =text_embeddings.repeat(1				,			a				,			1   )
       snake_case__  :    Optional[int]							       =text_embeddings.view(bs_embed * num_images_per_prompt				,			a				,			-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.
       snake_case__  :    str							       =guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
        snake_case__  :    List[str]
        if negative_prompt is None:
         snake_case__  :    str							       =[""""""] * batch_size
        elif type(a   ) is not type(a   ):
         raise TypeError(
             F"`negative_prompt` should be the same type to `prompt`, but got {type(a   )} !="
             F" {type(a   )}."   )
        elif isinstance(a				,			a   ):
         snake_case__  :    str							       =[negative_prompt]
        elif batch_size != len(a   ):
         raise ValueError(
             F"`negative_prompt`: {negative_prompt} has batch size {len(a   )}, 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__  :    Union[str, Any]							       =negative_prompt

        snake_case__  :    Optional[int]							       =text_input_ids.shape[-1]
        snake_case__  :    str							       =self.tokenizer(
            a				,			padding="""max_length"""				,			max_length=a				,			truncation=a				,			return_tensors="""pt"""				,			)
        snake_case__  :    List[Any]							       =self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

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

       # set timesteps
       self.scheduler.set_timesteps(a   )

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

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

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

        # predict the noise residual
        snake_case__  :    Optional[Any]							       =self.unet(a				,			a				,			encoder_hidden_states=a   ).sample

        # perform guidance
        if do_classifier_free_guidance:
         snake_case__       ,   snake_case__  :    Optional[int]							       =noise_pred.chunk(2   )
         snake_case__  :    Tuple							       =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        snake_case__  :    Optional[int]							       =self.scheduler.step(a				,			a				,			a				,			**a   ).prev_sample

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

       snake_case__  :    Tuple							       =1 / 0.18215 * latents
       snake_case__  :    Optional[int]							       =self.vae.decode(a   ).sample

       snake_case__  :    Optional[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
       snake_case__  :    Any							       =image.cpu().permute(0				,			2				,			3				,			1   ).float().numpy()

       if output_type == "pil":
        snake_case__  :    Union[str, Any]							       =self.numpy_to_pil(a   )

       if not return_dict:
        return image

       return StableDiffusionPipelineOutput(images=a				,			nsfw_content_detected=a   )













import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Optional[Any]										=							UnCLIPImageVariationPipeline
      _a							:						Optional[int]										=							IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      _a							:						Optional[Any]										=							IMAGE_VARIATION_BATCH_PARAMS

      _a							:						str										=							[
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      _a							:						Any										=							False
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim * 4
      @property
      def 						lowercase__					(     self   ):
       return 1_0_0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
       return tokenizer
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    int							       =CLIPTextConfig(
           bos_token_id=0				,			eos_token_id=2				,			hidden_size=self.text_embedder_hidden_size				,			projection_dim=self.text_embedder_hidden_size				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=1_0_0_0				,			)
       return CLIPTextModelWithProjection(a   )
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Optional[int]							       =CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size				,			projection_dim=self.text_embedder_hidden_size				,			num_hidden_layers=5				,			num_attention_heads=4				,			image_size=3_2				,			intermediate_size=3_7				,			patch_size=1				,			)
       return CLIPVisionModelWithProjection(a   )
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    int							       ={
           """clip_embeddings_dim""": self.text_embedder_hidden_size,
           """time_embed_dim""": self.time_embed_dim,
           """cross_attention_dim""": self.cross_attention_dim,
       }

       snake_case__  :    Optional[int]							       =UnCLIPTextProjModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    List[str]							       ={
           """sample_size""": 3_2,
           # RGB in channels
           """in_channels""": 3,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 6,
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": """identity""",
       }

       snake_case__  :    List[str]							       =UNetaDConditionModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       return {
           "sample_size": 6_4,
           "layers_per_block": 1,
           "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
           "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "in_channels": 6,
           "out_channels": 3,
       }
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    Union[str, Any]							       =UNetaDModel(**self.dummy_super_res_kwargs   )
       return model
      @property
      def 						lowercase__					(     self   ):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1   )

       snake_case__  :    int							       =UNetaDModel(**self.dummy_super_res_kwargs   )
       return model
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.dummy_decoder
       snake_case__  :    Any							       =self.dummy_text_proj
       snake_case__  :    Optional[int]							       =self.dummy_text_encoder
       snake_case__  :    Optional[Any]							       =self.dummy_tokenizer
       snake_case__  :    List[str]							       =self.dummy_super_res_first
       snake_case__  :    str							       =self.dummy_super_res_last

       snake_case__  :    List[Any]							       =UnCLIPScheduler(
           variance_type="""learned_range"""				,			prediction_type="""epsilon"""				,			num_train_timesteps=1_0_0_0				,			)

       snake_case__  :    Any							       =UnCLIPScheduler(
           variance_type="""fixed_small_log"""				,			prediction_type="""epsilon"""				,			num_train_timesteps=1_0_0_0				,			)

       snake_case__  :    str							       =CLIPImageProcessor(crop_size=3_2				,			size=3_2   )

       snake_case__  :    List[Any]							       =self.dummy_image_encoder

       return {
           "decoder": decoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "text_proj": text_proj,
           "feature_extractor": feature_extractor,
           "image_encoder": image_encoder,
           "super_res_first": super_res_first,
           "super_res_last": super_res_last,
           "decoder_scheduler": decoder_scheduler,
           "super_res_scheduler": super_res_scheduler,
       }
      def 						lowercase__					(     self				,			a				,			a=0				,			a=True   ):
       snake_case__  :    str							       =floats_tensor((1, 3, 3_2, 3_2)				,			rng=random.Random(a   )   ).to(a   )
       if str(a   ).startswith("""mps"""   ):
        snake_case__  :    str							       =torch.manual_seed(a   )
       else:
        snake_case__  :    List[Any]							       =torch.Generator(device=a   ).manual_seed(a   )

       if pil_image:
        snake_case__  :    Optional[int]							       =input_image * 0.5 + 0.5
        snake_case__  :    Tuple							       =input_image.clamp(0				,			1   )
        snake_case__  :    Union[str, Any]							       =input_image.cpu().permute(0				,			2				,			3				,			1   ).float().numpy()
        snake_case__  :    Tuple							       =DiffusionPipeline.numpy_to_pil(a   )[0]

       return {
           "image": input_image,
           "generator": generator,
           "decoder_num_inference_steps": 2,
           "super_res_num_inference_steps": 2,
           "output_type": "np",
       }
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       ="""cpu"""

       snake_case__  :    Dict							       =self.get_dummy_components()

       snake_case__  :    Tuple							       =self.pipeline_class(**a   )
       snake_case__  :    Optional[Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[int]							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(**a   )
       snake_case__  :    List[str]							       =output.images

       snake_case__  :    Any							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[Any]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    Union[str, Any]							       =image[0, -3:, -3:, -1]
       snake_case__  :    Optional[int]							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       snake_case__  :    Optional[Any]							       =np.array(
           [
               0.9997,
               0.0002,
               0.9997,
               0.9997,
               0.9969,
               0.0023,
               0.9997,
               0.9969,
               0.9970,
           ]   )

       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 						lowercase__					(     self   ):
       snake_case__  :    Dict							       ="""cpu"""

       snake_case__  :    int							       =self.get_dummy_components()

       snake_case__  :    Any							       =self.pipeline_class(**a   )
       snake_case__  :    Tuple							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    int							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    Any							       =pipe(**a   )
       snake_case__  :    Optional[int]							       =output.images

       snake_case__  :    int							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    List[str]							       =image[0, -3:, -3:, -1]
       snake_case__  :    List[Any]							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       snake_case__  :    str							       =np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

       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 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="""cpu"""

       snake_case__  :    Dict							       =self.get_dummy_components()

       snake_case__  :    Tuple							       =self.pipeline_class(**a   )
       snake_case__  :    List[Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Tuple							       =self.get_dummy_inputs(a				,			pil_image=a   )
       snake_case__  :    Any							       =[
           pipeline_inputs["""image"""],
           pipeline_inputs["""image"""],
       ]

       snake_case__  :    Tuple							       =pipe(**a   )
       snake_case__  :    str							       =output.images

       snake_case__  :    Tuple							       =self.get_dummy_inputs(a				,			pil_image=a   )
       snake_case__  :    str							       =[
           tuple_pipeline_inputs["""image"""],
           tuple_pipeline_inputs["""image"""],
       ]

       snake_case__  :    Union[str, Any]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    List[Any]							       =image[0, -3:, -3:, -1]
       snake_case__  :    Tuple							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (2, 6_4, 6_4, 3)

       snake_case__  :    int							       =np.array(
           [
               0.9997,
               0.9989,
               0.0008,
               0.0021,
               0.9960,
               0.0018,
               0.0014,
               0.0002,
               0.9933,
           ]   )

       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 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =torch.device("""cpu"""   )







       class  _lowercase   :
             _a							:						Tuple										=							1




       snake_case__  :    Optional[Any]							       =self.get_dummy_components()

       snake_case__  :    List[str]							       =self.pipeline_class(**a   )
       snake_case__  :    int							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[Any]							       =torch.Generator(device=a   ).manual_seed(0   )
       snake_case__  :    List[Any]							       =pipe.decoder.dtype
       snake_case__  :    str							       =1

       snake_case__  :    Dict							       =(
           batch_size,
           pipe.decoder.config.in_channels,
           pipe.decoder.config.sample_size,
           pipe.decoder.config.sample_size,
       )
       snake_case__  :    List[Any]							       =pipe.prepare_latents(
           a				,			dtype=a				,			device=a				,			generator=a				,			latents=a				,			scheduler=DummyScheduler()   )

       snake_case__  :    Optional[int]							       =(
           batch_size,
           pipe.super_res_first.config.in_channels // 2,
           pipe.super_res_first.config.sample_size,
           pipe.super_res_first.config.sample_size,
       )
       snake_case__  :    Dict							       =pipe.prepare_latents(
           a				,			dtype=a				,			device=a				,			generator=a				,			latents=a				,			scheduler=DummyScheduler()   )

       snake_case__  :    Union[str, Any]							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(
           **a				,			decoder_latents=a				,			super_res_latents=a   ).images

       snake_case__  :    Any							       =self.get_dummy_inputs(a				,			pil_image=a   )
       # Don't pass image, instead pass embedding
       snake_case__  :    Optional[Any]							       =pipeline_inputs.pop("""image"""   )
       snake_case__  :    Optional[int]							       =pipe.image_encoder(a   ).image_embeds

       snake_case__  :    Dict							       =pipe(
           **a				,			decoder_latents=a				,			super_res_latents=a				,			image_embeddings=a				,			).images

       # make sure passing text embeddings manually is identical
       assert np.abs(img_out_a - img_out_a   ).max() < 1e-4
      @skip_mps
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =torch_device == """cpu"""

       # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
       snake_case__  :    Tuple							       =1e-2

       self._test_attention_slicing_forward_pass(
           test_max_difference=a				,			expected_max_diff=a   )
      @skip_mps
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =torch_device == """cpu"""
       snake_case__  :    List[Any]							       =True
       snake_case__  :    List[Any]							       =[
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       self._test_inference_batch_single_identical(
           test_max_difference=a				,			relax_max_difference=a				,			additional_params_copy_to_batched_inputs=a				,			)
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =[
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        snake_case__  :    List[Any]							       =[2, 3]
        self._test_inference_batch_consistent(
            batch_sizes=a				,			additional_params_copy_to_batched_inputs=a				,			)
       else:
        self._test_inference_batch_consistent(
            additional_params_copy_to_batched_inputs=a   )
      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_save_load_local()



      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""   )
       snake_case__  :    Union[str, Any]							       =load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""   )

       snake_case__  :    List[Any]							       =UnCLIPImageVariationPipeline.from_pretrained(
           """kakaobrain/karlo-v1-alpha-image-variations"""				,			torch_dtype=torch.floataa   )
       snake_case__  :    Optional[int]							       =pipeline.to(a   )
       pipeline.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[Any]							       =torch.Generator(device="""cpu"""   ).manual_seed(0   )
       snake_case__  :    Tuple							       =pipeline(
           a				,			generator=a				,			output_type="""np"""				,			)

       snake_case__  :    Union[str, Any]							       =output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       assert_mean_pixel_difference(a				,			a				,			1_5   )





