




"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor
class      lowerCamelCase__      (					unittest.TestCase     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=7			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=18			,	SCREAMING_SNAKE_CASE=30			,	SCREAMING_SNAKE_CASE=400			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE=0.8_75			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,	SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			size if size is not None else {"shortest_edge": 20}
					snake_case						:  str        =			parent
					snake_case						:  int        =			batch_size
					snake_case						:  List[Any]        =			num_channels
					snake_case						:  Optional[Any]        =			image_size
					snake_case						:  Optional[Any]        =			min_resolution
					snake_case						:  Optional[int]        =			max_resolution
					snake_case						:  List[Any]        =			do_resize
					snake_case						:  int        =			size
					snake_case						:  Any        =			crop_pct
					snake_case						:  Tuple        =			do_normalize
					snake_case						:  str        =			image_mean
					snake_case						:  Tuple        =			image_std



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "crop_pct": self.crop_pct,
					}







@require_torch
@require_vision
class      lowerCamelCase__      (					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: List[Any]        =       ConvNextImageProcessor if is_vision_available() else None


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			ConvNextImageProcessingTester(self							)


			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"do_resize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"size"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"crop_pct"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"do_normalize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"image_mean"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"image_std"							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			self.image_processing_class.from_dict(self.image_processor_dict							)
					self.assertEqual(image_processor.size			,	{"shortest_edge": 20}							)

					snake_case						:  List[str]        =			self.image_processing_class.from_dict(self.image_processor_dict			,	size=42							)
					self.assertEqual(image_processor.size			,	{"shortest_edge": 42}							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			self.image_processing_class(**self.image_processor_dict							)
					# create random PIL images
					snake_case						:  Tuple        =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE			,	Image.Image							)

					# Test not batched input
					snake_case						:  Dict        =			image_processing(image_inputs[0]			,	return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape			,	(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["shortest_edge"],
					        self.image_processor_tester.size["shortest_edge"],
					    )			,	)

					# Test batched
					snake_case						:  int        =			image_processing(SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape			,	(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["shortest_edge"],
					        self.image_processor_tester.size["shortest_edge"],
					    )			,	)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			self.image_processing_class(**self.image_processor_dict							)
					# create random numpy tensors
					snake_case						:  Optional[int]        =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=SCREAMING_SNAKE_CASE			,	numpify=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE			,	np.ndarray							)

					# Test not batched input
					snake_case						:  Optional[int]        =			image_processing(image_inputs[0]			,	return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape			,	(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["shortest_edge"],
					        self.image_processor_tester.size["shortest_edge"],
					    )			,	)

					# Test batched
					snake_case						:  Tuple        =			image_processing(SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape			,	(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["shortest_edge"],
					        self.image_processor_tester.size["shortest_edge"],
					    )			,	)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			self.image_processing_class(**self.image_processor_dict							)
					# create random PyTorch tensors
					snake_case						:  Any        =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=SCREAMING_SNAKE_CASE			,	torchify=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE			,	torch.Tensor							)

					# Test not batched input
					snake_case						:  List[Any]        =			image_processing(image_inputs[0]			,	return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape			,	(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["shortest_edge"],
					        self.image_processor_tester.size["shortest_edge"],
					    )			,	)

					# Test batched
					snake_case						:  Dict        =			image_processing(SCREAMING_SNAKE_CASE			,	return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape			,	(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size["shortest_edge"],
					        self.image_processor_tester.size["shortest_edge"],
					    )			,	)







"""simple docstring"""

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__A							=     "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"
class      lowerCamelCase__      (					lowerCamelCase_     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__()

					self.register_modules(
					    unet=SCREAMING_SNAKE_CASE			,	scheduler=SCREAMING_SNAKE_CASE			,	)

					snake_case						:  List[str]        =			None


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if is_accelerate_available() and is_accelerate_version(">="			,	"0.14.0"							):
							from accelerate import cpu_offload
					else:
							raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"							)

					snake_case						:  Tuple        =			torch.device(F'''cuda:{gpu_id}'''							)

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

					for cpu_offloaded_model in [self.unet]:
							cpu_offload(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							cpu_offload(self.safety_checker			,	execution_device=SCREAMING_SNAKE_CASE			,	offload_buffers=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE=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."							)

					snake_case						:  Union[str, Any]        =			torch.device(F'''cuda:{gpu_id}'''							)

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

					snake_case						:  List[str]        =			None
					for cpu_offloaded_model in [self.unet]:
							snake_case     ,			snake_case						:  Optional[int]        =			cpu_offload_with_hook(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					if self.safety_checker is not None:
							snake_case     ,			snake_case						:  List[str]        =			cpu_offload_with_hook(self.safety_checker			,	SCREAMING_SNAKE_CASE			,	prev_module_hook=SCREAMING_SNAKE_CASE							)

					# We'll offload the last model manually.
					snake_case						:  Optional[Any]        =			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(SCREAMING_SNAKE_CASE			,	"_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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					snake_case						:  Any        =			(batch_size, num_channels, height, width)
					if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) != batch_size:
							raise ValueError(
							    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE							)}, but requested an effective batch'''
							    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

					if latents is None:
							snake_case						:  Any        =			randn_tensor(SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  Tuple        =			latents.to(device=SCREAMING_SNAKE_CASE			,	dtype=SCREAMING_SNAKE_CASE							)

					# scale the initial noise by the standard deviation required by the scheduler
					snake_case						:  Optional[int]        =			latents * self.scheduler.init_noise_sigma
					return latents


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = "pil"							):



					"""simple docstring"""







					if output_type not in ["pt", "np", "pil"]:
							raise ValueError(
							    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''							)

					# Equivalent to diffusers.VaeImageProcessor.denormalize
					snake_case						:  List[str]        =			(sample / 2 + 0.5).clamp(0			,	1							)
					if output_type == "pt":
							return sample

					# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
					snake_case						:  Optional[int]        =			sample.cpu().permute(0			,	2			,	3			,	1							).numpy()
					if output_type == "np":
							return sample

					# Output_type must be 'pil'
					snake_case						:  List[str]        =			self.numpy_to_pil(SCREAMING_SNAKE_CASE							)
					return sample


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					if self.unet.config.num_class_embeds is not None:
							if isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									snake_case						:  Optional[Any]        =			torch.tensor(SCREAMING_SNAKE_CASE			,	dtype=torch.int							)
							elif isinstance(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):
									assert batch_size == 1, "Batch size must be 1 if classes is an int"
									snake_case						:  str        =			torch.tensor([class_labels]			,	dtype=torch.int							)
							elif class_labels is None:
									# Randomly generate batch_size class labels
									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
									snake_case						:  Tuple        =			torch.randint(0			,	self.unet.config.num_class_embeds			,	size=(batch_size,)							)
							snake_case						:  int        =			class_labels.to(SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  str        =			None
					return class_labels


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if num_inference_steps is None and timesteps is None:
							raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."							)

					if num_inference_steps is not None and timesteps is not None:
							logger.warning(
							    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
							    " `timesteps` will be used over `num_inference_steps`."							)

					if latents is not None:
							snake_case						:  str        =			(batch_size, 3, img_size, img_size)
							if latents.shape != expected_shape:
									raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''							)

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



			@torch.no_grad()
			@replace_example_docstring(SCREAMING_SNAKE_CASE							)
			def __call__(       self			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = "pil"			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = 1			,	):



					"""simple docstring"""







					snake_case						:  List[str]        =			self.unet.config.sample_size
					snake_case						:  Dict        =			self._execution_device

					# 1. Check inputs
					self.check_inputs(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

					# 2. Prepare image latents
					# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
					snake_case						:  Any        =			self.prepare_latents(
					    batch_size=SCREAMING_SNAKE_CASE			,	num_channels=self.unet.config.in_channels			,	height=SCREAMING_SNAKE_CASE			,	width=SCREAMING_SNAKE_CASE			,	dtype=self.unet.dtype			,	device=SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE			,	latents=SCREAMING_SNAKE_CASE			,	)

					# 3. Handle class_labels for class-conditional models
					snake_case						:  Union[str, Any]        =			self.prepare_class_labels(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE							)

					# 4. Prepare timesteps
					if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE			,	device=SCREAMING_SNAKE_CASE							)
							snake_case						:  Dict        =			self.scheduler.timesteps
							snake_case						:  str        =			len(SCREAMING_SNAKE_CASE							)
					else:
							self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			self.scheduler.timesteps

					# 5. Denoising loop
					# Multistep sampling: implements Algorithm 1 in the paper
					with self.progress_bar(total=SCREAMING_SNAKE_CASE							) as progress_bar:
							for i, t in enumerate(SCREAMING_SNAKE_CASE							):
									snake_case						:  Any        =			self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
									snake_case						:  Dict        =			self.unet(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	class_labels=SCREAMING_SNAKE_CASE			,	return_dict=SCREAMING_SNAKE_CASE							)[0]

									snake_case						:  List[Any]        =			self.scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)[0]

									# call the callback, if provided
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

        # 6. Post-process image sample
					snake_case						:  Dict        =			self.postprocess_image(SCREAMING_SNAKE_CASE			,	output_type=SCREAMING_SNAKE_CASE							)

					# Offload last model to CPU
					if hasattr(self			,	"final_offload_hook"							) and self.final_offload_hook is not None:
							self.final_offload_hook.offload()

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE							)

