






import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 							a_				(		a_			,			unittest.TestCase	):


							'''simple docstring'''






							__a:			int									=					UnCLIPPipeline
							__a:			Tuple									=					TEXT_TO_IMAGE_PARAMS - {
							    '''negative_prompt''',
							    '''height''',
							    '''width''',
							    '''negative_prompt_embeds''',
							    '''guidance_scale''',
							    '''prompt_embeds''',
							    '''cross_attention_kwargs''',
							}
							__a:			Union[str, Any]									=					TEXT_TO_IMAGE_BATCH_PARAMS
							__a:			List[str]									=					[
							    '''generator''',
							    '''return_dict''',
							    '''prior_num_inference_steps''',
							    '''decoder_num_inference_steps''',
							    '''super_res_num_inference_steps''',
							]
							__a:			Optional[Any]									=					False

							@property
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														return 3_2

							@property
							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														return 3_2

							@property
							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														return self.time_input_dim

							@property
							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														return self.time_input_dim * 4

							@property
							def   _lowercase (	self     )      ->      str:
														'''simple docstring'''

														return 1_0_0

							@property
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														lowerCAmelCase_       		=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
														return tokenizer

							@property
							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														torch.manual_seed(0     )
														lowerCAmelCase_       		=  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(lowercase_     )

							@property
							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														torch.manual_seed(0     )

														lowerCAmelCase_       		=  {
														    'num_attention_heads': 2,
														    'attention_head_dim': 1_2,
														    'embedding_dim': self.text_embedder_hidden_size,
														    'num_layers': 1,
														}

														lowerCAmelCase_       		=  PriorTransformer(**lowercase_     )
														return model

							@property
							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														torch.manual_seed(0     )

														lowerCAmelCase_       		=  {
														    'clip_embeddings_dim': self.text_embedder_hidden_size,
														    'time_embed_dim': self.time_embed_dim,
														    'cross_attention_dim': self.cross_attention_dim,
														}

														lowerCAmelCase_       		=  UnCLIPTextProjModel(**lowercase_     )
														return model

							@property
							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														torch.manual_seed(0     )

														lowerCAmelCase_       		=  {
														    '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',
														}

														lowerCAmelCase_       		=  UNetaDConditionModel(**lowercase_     )
														return model

							@property
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														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     )      ->      Dict:
														'''simple docstring'''

														torch.manual_seed(0     )

														lowerCAmelCase_       		=  UNetaDModel(**self.dummy_super_res_kwargs     )
														return model

							@property
							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														torch.manual_seed(1     )

														lowerCAmelCase_       		=  UNetaDModel(**self.dummy_super_res_kwargs     )
														return model

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.dummy_prior
														lowerCAmelCase_       		=  self.dummy_decoder
														lowerCAmelCase_       		=  self.dummy_text_proj
														lowerCAmelCase_       		=  self.dummy_text_encoder
														lowerCAmelCase_       		=  self.dummy_tokenizer
														lowerCAmelCase_       		=  self.dummy_super_res_first
														lowerCAmelCase_       		=  self.dummy_super_res_last

														lowerCAmelCase_       		=  UnCLIPScheduler(
														    variance_type='fixed_small_log'    ,    prediction_type='sample'    ,    num_train_timesteps=1_0_0_0    ,    clip_sample_range=5.0    ,    )

														lowerCAmelCase_       		=  UnCLIPScheduler(
														    variance_type='learned_range'    ,    prediction_type='epsilon'    ,    num_train_timesteps=1_0_0_0    ,    )

														lowerCAmelCase_       		=  UnCLIPScheduler(
														    variance_type='fixed_small_log'    ,    prediction_type='epsilon'    ,    num_train_timesteps=1_0_0_0    ,    )

														lowerCAmelCase_       		=  {
														    'prior': prior,
														    'decoder': decoder,
														    'text_proj': text_proj,
														    'text_encoder': text_encoder,
														    'tokenizer': tokenizer,
														    'super_res_first': super_res_first,
														    'super_res_last': super_res_last,
														    'prior_scheduler': prior_scheduler,
														    'decoder_scheduler': decoder_scheduler,
														    'super_res_scheduler': super_res_scheduler,
														}

														return components

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_=0     )      ->      Optional[Any]:
														'''simple docstring'''

														if str(lowercase_     ).startswith('mps'     ):
																					lowerCAmelCase_       		=  torch.manual_seed(lowercase_     )
														else:
																					lowerCAmelCase_       		=  torch.Generator(device=lowercase_     ).manual_seed(lowercase_     )
														lowerCAmelCase_       		=  {
														    'prompt': 'horse',
														    'generator': generator,
														    'prior_num_inference_steps': 2,
														    'decoder_num_inference_steps': 2,
														    'super_res_num_inference_steps': 2,
														    'output_type': 'numpy',
														}
														return inputs

							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														lowerCAmelCase_       		=  'cpu'

														lowerCAmelCase_       		=  self.get_dummy_components()

														lowerCAmelCase_       		=  self.pipeline_class(**lowercase_     )
														lowerCAmelCase_       		=  pipe.to(lowercase_     )

														pipe.set_progress_bar_config(disable=lowercase_     )

														lowerCAmelCase_       		=  pipe(**self.get_dummy_inputs(lowercase_     )     )
														lowerCAmelCase_       		=  output.images

														lowerCAmelCase_       		=  pipe(
														    **self.get_dummy_inputs(lowercase_     )    ,    return_dict=lowercase_    ,    )[0]

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

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

														lowerCAmelCase_       		=  np.array(
														    [
														        0.99_97,
														        0.99_88,
														        0.00_28,
														        0.99_97,
														        0.99_84,
														        0.99_65,
														        0.00_29,
														        0.99_86,
														        0.00_25,
														    ]     )

														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     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  torch.device('cpu'     )




														class 							a_				:


																					'''simple docstring'''






																					__a:			Any									=					1


														lowerCAmelCase_       		=  self.get_dummy_components()

														lowerCAmelCase_       		=  self.pipeline_class(**lowercase_     )
														lowerCAmelCase_       		=  pipe.to(lowercase_     )

														lowerCAmelCase_       		=  components['prior']
														lowerCAmelCase_       		=  components['decoder']
														lowerCAmelCase_       		=  components['super_res_first']
														lowerCAmelCase_       		=  components['tokenizer']
														lowerCAmelCase_       		=  components['text_encoder']

														lowerCAmelCase_       		=  torch.Generator(device=lowercase_     ).manual_seed(0     )
														lowerCAmelCase_       		=  prior.dtype
														lowerCAmelCase_       		=  1

														lowerCAmelCase_       		=  (batch_size, prior.config.embedding_dim)
														lowerCAmelCase_       		=  pipe.prepare_latents(
														    lowercase_    ,    dtype=lowercase_    ,    device=lowercase_    ,    generator=lowercase_    ,    latents=lowercase_    ,    scheduler=DummyScheduler()     )
														lowerCAmelCase_       		=  (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
														lowerCAmelCase_       		=  pipe.prepare_latents(
														    lowercase_    ,    dtype=lowercase_    ,    device=lowercase_    ,    generator=lowercase_    ,    latents=lowercase_    ,    scheduler=DummyScheduler()     )

														lowerCAmelCase_       		=  (
														    batch_size,
														    super_res_first.config.in_channels // 2,
														    super_res_first.config.sample_size,
														    super_res_first.config.sample_size,
														)
														lowerCAmelCase_       		=  pipe.prepare_latents(
														    lowercase_    ,    dtype=lowercase_    ,    device=lowercase_    ,    generator=lowercase_    ,    latents=lowercase_    ,    scheduler=DummyScheduler()     )

														pipe.set_progress_bar_config(disable=lowercase_     )

														lowerCAmelCase_       		=  'this is a prompt example'

														lowerCAmelCase_       		=  torch.Generator(device=lowercase_     ).manual_seed(0     )
														lowerCAmelCase_       		=  pipe(
														    [prompt]    ,    generator=lowercase_    ,    prior_num_inference_steps=2    ,    decoder_num_inference_steps=2    ,    super_res_num_inference_steps=2    ,    prior_latents=lowercase_    ,    decoder_latents=lowercase_    ,    super_res_latents=lowercase_    ,    output_type='np'    ,    )
														lowerCAmelCase_       		=  output.images

														lowerCAmelCase_       		=  tokenizer(
														    lowercase_    ,    padding='max_length'    ,    max_length=tokenizer.model_max_length    ,    return_tensors='pt'    ,    )
														lowerCAmelCase_       		=  text_encoder(text_inputs.input_ids     )
														lowerCAmelCase_       		=  text_inputs.attention_mask

														lowerCAmelCase_       		=  torch.Generator(device=lowercase_     ).manual_seed(0     )
														lowerCAmelCase_       		=  pipe(
														    generator=lowercase_    ,    prior_num_inference_steps=2    ,    decoder_num_inference_steps=2    ,    super_res_num_inference_steps=2    ,    prior_latents=lowercase_    ,    decoder_latents=lowercase_    ,    super_res_latents=lowercase_    ,    text_model_output=lowercase_    ,    text_attention_mask=lowercase_    ,    output_type='np'    ,    )[0]

														# make sure passing text embeddings manually is identical
														assert np.abs(image - image_from_text     ).max() < 1e-4

							@skip_mps
							def   _lowercase (	self     )      ->      int:
														'''simple docstring'''

														lowerCAmelCase_       		=  torch_device == 'cpu'

														self._test_attention_slicing_forward_pass(test_max_difference=lowercase_    ,    expected_max_diff=0.01     )

							@skip_mps
							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_       		=  torch_device == 'cpu'
														lowerCAmelCase_       		=  True
														lowerCAmelCase_       		=  [
														    'prior_num_inference_steps',
														    'decoder_num_inference_steps',
														    'super_res_num_inference_steps',
														]

														self._test_inference_batch_single_identical(
														    test_max_difference=lowercase_    ,    relax_max_difference=lowercase_    ,    additional_params_copy_to_batched_inputs=lowercase_    ,    )

							def   _lowercase (	self     )      ->      str:
														'''simple docstring'''

														lowerCAmelCase_       		=  [
														    'prior_num_inference_steps',
														    'decoder_num_inference_steps',
														    'super_res_num_inference_steps',
														]

														if torch_device == "mps":
																					# TODO: MPS errors with larger batch sizes
																					lowerCAmelCase_       		=  [2, 3]
																					self._test_inference_batch_consistent(
																					    batch_sizes=lowercase_    ,    additional_params_copy_to_batched_inputs=lowercase_    ,    )
														else:
																					self._test_inference_batch_consistent(
																					    additional_params_copy_to_batched_inputs=lowercase_     )

							@skip_mps
							def   _lowercase (	self     )      ->      List[str]:
														'''simple docstring'''

														return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														return super().test_save_load_local()






							@skip_mps
							def   _lowercase (	self     )      ->      Tuple:
														'''simple docstring'''

														return super().test_save_load_optional_components()




@nightly
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  load_numpy(
														    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
														    '/unclip/karlo_v1_alpha_horse_cpu.npy'     )

														lowerCAmelCase_       		=  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'     )
														pipeline.set_progress_bar_config(disable=lowercase_     )

														lowerCAmelCase_       		=  torch.manual_seed(0     )
														lowerCAmelCase_       		=  pipeline(
														    'horse'    ,    num_images_per_prompt=1    ,    generator=lowercase_    ,    output_type='np'    ,    )

														lowerCAmelCase_       		=  output.images[0]

														assert image.shape == (2_5_6, 2_5_6, 3)
														assert np.abs(expected_image - image     ).max() < 1e-1




@slow
@require_torch_gpu
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  load_numpy(
														    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
														    '/unclip/karlo_v1_alpha_horse_fp16.npy'     )

														lowerCAmelCase_       		=  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    ,    torch_dtype=torch.floataa     )
														lowerCAmelCase_       		=  pipeline.to(lowercase_     )
														pipeline.set_progress_bar_config(disable=lowercase_     )

														lowerCAmelCase_       		=  torch.Generator(device='cpu'     ).manual_seed(0     )
														lowerCAmelCase_       		=  pipeline(
														    'horse'    ,    generator=lowercase_    ,    output_type='np'    ,    )

														lowerCAmelCase_       		=  output.images[0]

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

														assert_mean_pixel_difference(lowercase_    ,    lowercase_     )






							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														torch.cuda.empty_cache()
														torch.cuda.reset_max_memory_allocated()
														torch.cuda.reset_peak_memory_stats()

														lowerCAmelCase_       		=  UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    ,    torch_dtype=torch.floataa     )
														lowerCAmelCase_       		=  pipe.to(lowercase_     )
														pipe.set_progress_bar_config(disable=lowercase_     )
														pipe.enable_attention_slicing()
														pipe.enable_sequential_cpu_offload()

														lowerCAmelCase_       		=  pipe(
														    'horse'    ,    num_images_per_prompt=1    ,    prior_num_inference_steps=2    ,    decoder_num_inference_steps=2    ,    super_res_num_inference_steps=2    ,    output_type='np'    ,    )

														lowerCAmelCase_       		=  torch.cuda.max_memory_allocated()
														# make sure that less than 7 GB is allocated
														assert mem_bytes < 7 * 1_0**9











from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowerCamelCase_		  =						"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""
def   lowerCamelCase      (		a_				,		a_				,		a_=8			)			->    Tuple:
							lowerCAmelCase_       		=  height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							lowerCAmelCase_       		=  width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor
def   lowerCamelCase      (		a_				,		a_=512				,		a_=512			)			->    Any:
							lowerCAmelCase_       		=  pil_image.resize((w, h)				,		resample=Image.BICUBIC				,		reducing_gap=1			)
							lowerCAmelCase_       		=  np.array(pil_image.convert('RGB'			)			)
							lowerCAmelCase_       		=  arr.astype(np.floataa			) / 127.5 - 1
							lowerCAmelCase_       		=  np.transpose(a_				,		[2, 0, 1]			)
							lowerCAmelCase_       		=  torch.from_numpy(a_			).unsqueeze(0			)
							return image




class 							a_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )      ->      List[Any]:
														'''simple docstring'''

														super().__init__()

														self.register_modules(
														    unet=lowercase_    ,    scheduler=lowercase_    ,    movq=lowercase_    ,    )
														lowerCAmelCase_       		=  2 ** (len(self.movq.config.block_out_channels     ) - 1)

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  min(int(num_inference_steps * strength     )    ,    lowercase_     )

														lowerCAmelCase_       		=  max(num_inference_steps - init_timestep    ,    0     )
														lowerCAmelCase_       		=  self.scheduler.timesteps[t_start:]

														return timesteps, num_inference_steps - t_start

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_=None     )      ->      str:
														'''simple docstring'''

														if not isinstance(lowercase_    ,    (torch.Tensor, PIL.Image.Image, list)     ):
																					raise ValueError(
																					    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase_     )}'''     )

														lowerCAmelCase_       		=  image.to(device=lowercase_    ,    dtype=lowercase_     )

														lowerCAmelCase_       		=  batch_size * num_images_per_prompt

														if image.shape[1] == 4:
																					lowerCAmelCase_       		=  image

														else:
																					if isinstance(lowercase_    ,    lowercase_     ) and len(lowercase_     ) != batch_size:
																												raise ValueError(
																												    f'''You have passed a list of generators of length {len(lowercase_     )}, but requested an effective batch'''
																												    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

																					elif isinstance(lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  [
																												    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowercase_     )
																												]
																												lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
																					else:
																												lowerCAmelCase_       		=  self.movq.encode(lowercase_     ).latent_dist.sample(lowercase_     )

																					lowerCAmelCase_       		=  self.movq.config.scaling_factor * init_latents

														lowerCAmelCase_       		=  torch.cat([init_latents]    ,    dim=0     )

														lowerCAmelCase_       		=  init_latents.shape
														lowerCAmelCase_       		=  randn_tensor(lowercase_    ,    generator=lowercase_    ,    device=lowercase_    ,    dtype=lowercase_     )

														# get latents
														lowerCAmelCase_       		=  self.scheduler.add_noise(lowercase_    ,    lowercase_    ,    lowercase_     )

														lowerCAmelCase_       		=  init_latents

														return latents

							def   _lowercase (	self    ,    lowercase_=0     )      ->      int:
														'''simple docstring'''

														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError('Please install accelerate via `pip install accelerate`'     )

														lowerCAmelCase_       		=  torch.device(f'''cuda:{gpu_id}'''     )

														lowerCAmelCase_       		=  [
														    self.unet,
														    self.movq,
														]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowercase_    ,    lowercase_     )

							def   _lowercase (	self    ,    lowercase_=0     )      ->      Union[str, Any]:
														'''simple docstring'''

														if is_accelerate_available() and is_accelerate_version('>='    ,    '0.17.0.dev0'     ):
																					from accelerate import cpu_offload_with_hook
														else:
																					raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'     )

														lowerCAmelCase_       		=  torch.device(f'''cuda:{gpu_id}'''     )

														if self.device.type != "cpu":
																					self.to('cpu'    ,    silence_dtype_warnings=lowercase_     )
																					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

														lowerCAmelCase_       		=  None
														for cpu_offloaded_model in [self.unet, self.movq]:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  cpu_offload_with_hook(lowercase_    ,    lowercase_    ,    prev_module_hook=lowercase_     )

														# We'll offload the last model manually.
														lowerCAmelCase_       		=  hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														if not hasattr(self.unet    ,    '_hf_hook'     ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(lowercase_    ,    '_hf_hook'     )
																					    and hasattr(module._hf_hook    ,    'execution_device'     )
																					    and module._hf_hook.execution_device is not None
																					):
																												return torch.device(module._hf_hook.execution_device     )
														return self.device






							@torch.no_grad()
							@replace_example_docstring(lowercase_     )
							def __call__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 5_1_2    ,    lowercase_ = 5_1_2    ,    lowercase_ = 1_0_0    ,    lowercase_ = 4.0    ,    lowercase_ = 0.3    ,    lowercase_ = 1    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    )      ->      int:
														'''simple docstring'''

														lowerCAmelCase_       		=  self._execution_device

														lowerCAmelCase_       		=  guidance_scale > 1.0

														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )
														if isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  torch.cat(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  image_embeds.shape[0]

														if do_classifier_free_guidance:
																					lowerCAmelCase_       		=  image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  negative_image_embeds.repeat_interleave(lowercase_    ,    dim=0     )
																					lowerCAmelCase_       		=  hint.repeat_interleave(lowercase_    ,    dim=0     )

														lowerCAmelCase_       		=  torch.cat([negative_image_embeds, image_embeds]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )
														lowerCAmelCase_       		=  torch.cat([hint, hint]    ,    dim=0     ).to(dtype=self.unet.dtype    ,    device=lowercase_     )

														if not isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  [image]
														if not all(isinstance(lowercase_    ,    (PIL.Image.Image, torch.Tensor)     ) for i in image     ):
																					raise ValueError(
																					    f'''Input is in incorrect format: {[type(lowercase_     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''     )

														lowerCAmelCase_       		=  torch.cat([prepare_image(lowercase_    ,    lowercase_    ,    lowercase_     ) for i in image]    ,    dim=0     )
														lowerCAmelCase_       		=  image.to(dtype=image_embeds.dtype    ,    device=lowercase_     )

														lowerCAmelCase_       		=  self.movq.encode(lowercase_     )['latents']
														lowerCAmelCase_       		=  latents.repeat_interleave(lowercase_    ,    dim=0     )
														self.scheduler.set_timesteps(lowercase_    ,    device=lowercase_     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.get_timesteps(lowercase_    ,    lowercase_    ,    lowercase_     )
														lowerCAmelCase_       		=  timesteps[:1].repeat(batch_size * num_images_per_prompt     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  downscale_height_and_width(lowercase_    ,    lowercase_    ,    self.movq_scale_factor     )
														lowerCAmelCase_       		=  self.prepare_latents(
														    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    image_embeds.dtype    ,    lowercase_    ,    lowercase_     )
														for i, t in enumerate(self.progress_bar(lowercase_     )     ):
																					# expand the latents if we are doing classifier free guidance
																					lowerCAmelCase_       		=  torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

																					lowerCAmelCase_       		=  {'image_embeds': image_embeds, 'hint': hint}
																					lowerCAmelCase_       		=  self.unet(
																					    sample=lowercase_    ,    timestep=lowercase_    ,    encoder_hidden_states=lowercase_    ,    added_cond_kwargs=lowercase_    ,    return_dict=lowercase_    ,    )[0]

																					if do_classifier_free_guidance:
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.split(latents.shape[1]    ,    dim=1     )
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.chunk(2     )
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  variance_pred.chunk(2     )
																												lowerCAmelCase_       		=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																												lowerCAmelCase_       		=  torch.cat([noise_pred, variance_pred_text]    ,    dim=1     )

																					if not (
																					    hasattr(self.scheduler.config    ,    'variance_type'     )
																					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																					):
																												lowerCAmelCase_		,     lowerCAmelCase_       		=  noise_pred.split(latents.shape[1]    ,    dim=1     )

																					# compute the previous noisy sample x_t -> x_t-1

																					lowerCAmelCase_       		=  self.scheduler.step(
																					    lowercase_    ,    lowercase_    ,    lowercase_    ,    generator=lowercase_    ,    )[0]

														# post-processing
														lowerCAmelCase_       		=  self.movq.decode(lowercase_    ,    force_not_quantize=lowercase_     )['sample']

														if output_type not in ["pt", "np", "pil"]:
																					raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''     )

														if output_type in ["np", "pil"]:
																					lowerCAmelCase_       		=  image * 0.5 + 0.5
																					lowerCAmelCase_       		=  image.clamp(0    ,    1     )
																					lowerCAmelCase_       		=  image.cpu().permute(0    ,    2    ,    3    ,    1     ).float().numpy()

														if output_type == "pil":
																					lowerCAmelCase_       		=  self.numpy_to_pil(lowercase_     )

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=lowercase_     )



