



"""simple docstring"""

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,
)


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

lowercase__			   =  "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: List[Any]=8					):
							__a		:		List[str]									=       height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__a		:		Any									=       width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=5_1_2  ,       _lowerCamelCase				: str=5_1_2					):
							__a		:		Dict									=       pil_image.resize((w, h)  ,       resample=Image.BICUBIC  ,       reducing_gap=1					)
							__a		:		Any									=       np.array(pil_image.convert("""RGB"""					)					)
							__a		:		Any									=       arr.astype(np.floataa					) / 1_27.5 - 1
							__a		:		Any									=       np.transpose(_lowerCamelCase  ,       [2, 0, 1]					)
							__a		:		Dict									=       torch.from_numpy(_lowerCamelCase					).unsqueeze(0					)
							return image







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     ):





													'''simple docstring'''

													super().__init__()

													self.register_modules(
													    unet=_lowercase						,     scheduler=_lowercase						,     movq=_lowercase						,     )
													__a		:		List[str]									=       2 ** (len(self.movq.config.block_out_channels							) - 1)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       min(int(num_inference_steps * strength							)						,     _lowercase							)

													__a		:		str									=       max(num_inference_steps - init_timestep						,     0							)
													__a		:		List[Any]									=       self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase						,     _lowercase=None							):





													'''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							)}'''							)

													__a		:		Optional[Any]									=       image.to(device=_lowercase						,     dtype=_lowercase							)

													__a		:		Optional[int]									=       batch_size * num_images_per_prompt

													if image.shape[1] == 4:
																				__a		:		int									=       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							):
																											__a		:		int									=       [
																											    self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(_lowercase							)
																											]
																											__a		:		Optional[Any]									=       torch.cat(_lowercase						,     dim=0							)
																				else:
																											__a		:		Optional[Any]									=       self.movq.encode(_lowercase							).latent_dist.sample(_lowercase							)

																				__a		:		Union[str, Any]									=       self.movq.config.scaling_factor * init_latents

													__a		:		Dict									=       torch.cat([init_latents]						,     dim=0							)

													__a		:		Dict									=       init_latents.shape
													__a		:		Optional[int]									=       randn_tensor(_lowercase						,     generator=_lowercase						,     device=_lowercase						,     dtype=_lowercase							)

													# get latents
													__a		:		Any									=       self.scheduler.add_noise(_lowercase						,     _lowercase						,     _lowercase							)

													__a		:		Any									=       init_latents

													return latents



						def     lowerCAmelCase__(self						,     _lowercase=0							):





													'''simple docstring'''

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

													__a		:		Any									=       torch.device(F'''cuda:{gpu_id}'''							)

													__a		:		List[Any]									=       [
													    self.unet,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																				if cpu_offloaded_model is not None:
																											cpu_offload(_lowercase						,     _lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase=0							):





													'''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."""							)

													__a		:		List[Any]									=       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)

													__a		:		Tuple									=       None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				__a							,     __a		:		Optional[int]									=       cpu_offload_with_hook(_lowercase						,     _lowercase						,     prev_module_hook=_lowercase							)

													# We'll offload the last model manually.
													__a		:		Tuple									=       hook



						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     lowerCAmelCase__(self							):





													'''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 = 512						,     _lowercase = 512						,     _lowercase = 100						,     _lowercase = 4.0						,     _lowercase = 0.3						,     _lowercase = 1						,     _lowercase = None						,     _lowercase = "pil"						,     _lowercase = True						,     ):





													'''simple docstring'''

													__a		:		List[Any]									=       self._execution_device

													__a		:		List[str]									=       guidance_scale > 1.0

													if isinstance(_lowercase						,     _lowercase							):
																				__a		:		Tuple									=       torch.cat(_lowercase						,     dim=0							)
													__a		:		Union[str, Any]									=       image_embeds.shape[0]
													if isinstance(_lowercase						,     _lowercase							):
																				__a		:		Optional[int]									=       torch.cat(_lowercase						,     dim=0							)

													if do_classifier_free_guidance:
																				__a		:		int									=       image_embeds.repeat_interleave(_lowercase						,     dim=0							)
																				__a		:		Union[str, Any]									=       negative_image_embeds.repeat_interleave(_lowercase						,     dim=0							)

													__a		:		Union[str, Any]									=       torch.cat([negative_image_embeds, image_embeds]						,     dim=0							).to(dtype=self.unet.dtype						,     device=_lowercase							)

													if not isinstance(_lowercase						,     _lowercase							):
																				__a		:		List[str]									=       [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'''							)

													__a		:		str									=       torch.cat([prepare_image(_lowercase						,     _lowercase						,     _lowercase							) for i in image]						,     dim=0							)
													__a		:		Optional[int]									=       image.to(dtype=image_embeds.dtype						,     device=_lowercase							)

													__a		:		Optional[Any]									=       self.movq.encode(_lowercase							)["""latents"""]
													__a		:		List[Any]									=       latents.repeat_interleave(_lowercase						,     dim=0							)
													self.scheduler.set_timesteps(_lowercase						,     device=_lowercase							)
													__a							,     __a		:		Optional[int]									=       self.get_timesteps(_lowercase						,     _lowercase						,     _lowercase							)
													__a		:		Optional[int]									=       timesteps[:1].repeat(batch_size * num_images_per_prompt							)
													__a							,     __a		:		int									=       downscale_height_and_width(_lowercase						,     _lowercase						,     self.movq_scale_factor							)
													__a		:		List[str]									=       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
																				__a		:		Dict									=       torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																				__a		:		Tuple									=       {"""image_embeds""": image_embeds}
																				__a		:		str									=       self.unet(
																				    sample=_lowercase						,     timestep=_lowercase						,     encoder_hidden_states=_lowercase						,     added_cond_kwargs=_lowercase						,     return_dict=_lowercase						,     )[0]

																				if do_classifier_free_guidance:
																											__a							,     __a		:		Union[str, Any]									=       noise_pred.split(latents.shape[1]						,     dim=1							)
																											__a							,     __a		:		int									=       noise_pred.chunk(2							)
																											__a							,     __a		:		int									=       variance_pred.chunk(2							)
																											__a		:		List[Any]									=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																											__a		:		Optional[Any]									=       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"]
																				):
																											__a							,     __a		:		Optional[int]									=       noise_pred.split(latents.shape[1]						,     dim=1							)

																				# compute the previous noisy sample x_t -> x_t-1
																				__a		:		Optional[int]									=       self.scheduler.step(
																				    _lowercase						,     _lowercase						,     _lowercase						,     generator=_lowercase						,     )[0]

													# post-processing
													__a		:		int									=       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"]:
																				__a		:		List[Any]									=       image * 0.5 + 0.5
																				__a		:		Union[str, Any]									=       image.clamp(0						,     1							)
																				__a		:		List[str]									=       image.cpu().permute(0						,     2						,     3						,     1							).float().numpy()

													if output_type == "pil":
																				__a		:		Optional[int]									=       self.numpy_to_pil(_lowercase							)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=_lowercase							)






"""simple docstring"""

import unittest

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

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					KandinskyVaaPriorPipeline
						_lowerCAmelCase							=					["prompt"]
						_lowerCAmelCase							=					["prompt", "negative_prompt"]
						_lowerCAmelCase							=					[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						_lowerCAmelCase							=					False



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 32



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.time_input_dim * 4



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 100



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		List[str]									=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		str									=       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=37						,     layer_norm_eps=1e-05						,     num_attention_heads=4						,     num_hidden_layers=5						,     pad_token_id=1						,     vocab_size=1000						,     )
													return CLIPTextModelWithProjection(_lowercase							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)

													__a		:		Dict									=       {
													    """num_attention_heads""": 2,
													    """attention_head_dim""": 12,
													    """embedding_dim""": self.text_embedder_hidden_size,
													    """num_layers""": 1,
													}

													__a		:		Tuple									=       PriorTransformer(**_lowercase							)
													# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
													__a		:		int									=       nn.Parameter(torch.ones(model.clip_std.shape							)							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													torch.manual_seed(0							)
													__a		:		List[str]									=       CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size						,     image_size=224						,     projection_dim=self.text_embedder_hidden_size						,     intermediate_size=37						,     num_attention_heads=4						,     num_channels=3						,     num_hidden_layers=5						,     patch_size=14						,     )

													__a		:		Optional[Any]									=       CLIPVisionModelWithProjection(_lowercase							)
													return model



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       CLIPImageProcessor(
													    crop_size=224						,     do_center_crop=_lowercase						,     do_normalize=_lowercase						,     do_resize=_lowercase						,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,     resample=3						,     size=224						,     )

													return image_processor



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       self.dummy_prior
													__a		:		int									=       self.dummy_image_encoder
													__a		:		Any									=       self.dummy_text_encoder
													__a		:		int									=       self.dummy_tokenizer
													__a		:		Optional[Any]									=       self.dummy_image_processor

													__a		:		List[Any]									=       UnCLIPScheduler(
													    variance_type="""fixed_small_log"""						,     prediction_type="""sample"""						,     num_train_timesteps=1000						,     clip_sample=_lowercase						,     clip_sample_range=10.0						,     )

													__a		:		List[Any]									=       {
													    """prior""": prior,
													    """image_encoder""": image_encoder,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """scheduler""": scheduler,
													    """image_processor""": image_processor,
													}

													return components



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase=0							):





													'''simple docstring'''

													if str(_lowercase							).startswith("""mps"""							):
																				__a		:		Dict									=       torch.manual_seed(_lowercase							)
													else:
																				__a		:		Union[str, Any]									=       torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
													__a		:		Union[str, Any]									=       {
													    """prompt""": """horse""",
													    """generator""": generator,
													    """guidance_scale""": 4.0,
													    """num_inference_steps""": 2,
													    """output_type""": """np""",
													}
													return inputs



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       """cpu"""

													__a		:		Union[str, Any]									=       self.get_dummy_components()

													__a		:		Dict									=       self.pipeline_class(**_lowercase							)
													__a		:		Tuple									=       pipe.to(_lowercase							)

													pipe.set_progress_bar_config(disable=_lowercase							)

													__a		:		Optional[int]									=       pipe(**self.get_dummy_inputs(_lowercase							)							)
													__a		:		str									=       output.image_embeds

													__a		:		Any									=       pipe(
													    **self.get_dummy_inputs(_lowercase							)						,     return_dict=_lowercase						,     )[0]

													__a		:		List[Any]									=       image[0, -10:]
													__a		:		List[Any]									=       image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__a		:		Optional[Any]									=       np.array(
													    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]							)

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



						@skip_mps
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       torch_device == """cpu"""
													__a		:		Any									=       True
													__a		:		Any									=       False

													self._test_inference_batch_single_identical(
													    test_max_difference=_lowercase						,     relax_max_difference=_lowercase						,     test_mean_pixel_difference=_lowercase						,     )





						@skip_mps
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       torch_device == """cpu"""
													__a		:		Union[str, Any]									=       False

													self._test_attention_slicing_forward_pass(
													    test_max_difference=_lowercase						,     test_mean_pixel_difference=_lowercase						,     )

