



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 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def __init__(       self							:			Union[str, Any]	,	a_							:			UNetaDModel	,	a_							:			CMStochasticIterativeScheduler			):
					'''simple docstring'''





					super().__init__()

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

					__UpperCAmelCase   :					Any													=     None




			def 			snake_case__					(       self							:			List[str]	,	a_							:			List[str]=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'''			)

					__UpperCAmelCase   :					Optional[Any]													=     torch.device(F'cuda:{gpu_id}'			)

					if self.device.type != "cpu":
							self.to('''cpu'''	,	silence_dtype_warnings=a_			)
							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(a_	,	a_			)

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




			def 			snake_case__					(       self							:			Tuple	,	a_							:			Optional[int]=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.'''			)

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

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

					__UpperCAmelCase   :					Tuple													=     None
					for cpu_offloaded_model in [self.unet]:
							__UpperCAmelCase							, __UpperCAmelCase   :					str													=     cpu_offload_with_hook(a_	,	a_	,	prev_module_hook=a_			)

					if self.safety_checker is not None:
							__UpperCAmelCase							, __UpperCAmelCase   :					Any													=     cpu_offload_with_hook(self.safety_checker	,	a_	,	prev_module_hook=a_			)

					# We'll offload the last model manually.
					__UpperCAmelCase   :					Optional[Any]													=     hook




			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not hasattr(self.unet	,	'''_hf_hook'''			):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(a_	,	'''_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 			snake_case__					(       self							:			int	,	a_							:			Dict	,	a_							:			List[Any]	,	a_							:			List[str]	,	a_							:			str	,	a_							:			Union[str, Any]	,	a_							:			Tuple	,	a_							:			Optional[Any]	,	a_							:			Dict=None			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     (batch_size, num_channels, height, width)
					if isinstance(a_	,	a_			) and len(a_			) != batch_size:
							raise ValueError(
							    F'You have passed a list of generators of length {len(a_			)}, 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:
							__UpperCAmelCase   :					Tuple													=     randn_tensor(a_	,	generator=a_	,	device=a_	,	dtype=a_			)
					else:
							__UpperCAmelCase   :					Tuple													=     latents.to(device=a_	,	dtype=a_			)

					# scale the initial noise by the standard deviation required by the scheduler
					__UpperCAmelCase   :					Union[str, Any]													=     latents * self.scheduler.init_noise_sigma
					return latents




			def 			snake_case__					(       self							:			Any	,	a_							:			torch.FloatTensor	,	a_							:			str = "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
					__UpperCAmelCase   :					Union[str, Any]													=     (sample / 2 + 0.5).clamp(0	,	1			)
					if output_type == "pt":
							return sample

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

					# Output_type must be 'pil'
					__UpperCAmelCase   :					Optional[Any]													=     self.numpy_to_pil(a_			)
					return sample




			def 			snake_case__					(       self							:			Any	,	a_							:			Dict	,	a_							:			Dict	,	a_							:			Tuple=None			):
					'''simple docstring'''





					if self.unet.config.num_class_embeds is not None:
							if isinstance(a_	,	a_			):
									__UpperCAmelCase   :					str													=     torch.tensor(a_	,	dtype=torch.int			)
							elif isinstance(a_	,	a_			):
									assert batch_size == 1, "Batch size must be 1 if classes is an int"
									__UpperCAmelCase   :					int													=     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
									__UpperCAmelCase   :					List[str]													=     torch.randint(0	,	self.unet.config.num_class_embeds	,	size=(batch_size,)			)
							__UpperCAmelCase   :					Optional[Any]													=     class_labels.to(a_			)
					else:
							__UpperCAmelCase   :					Optional[int]													=     None
					return class_labels




			def 			snake_case__					(       self							:			Union[str, Any]	,	a_							:			Optional[Any]	,	a_							:			Union[str, Any]	,	a_							:			List[Any]	,	a_							:			str	,	a_							:			List[str]	,	a_							:			List[str]			):
					'''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:
							__UpperCAmelCase   :					int													=     (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(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_			)}.'			)




			@torch.no_grad()
			@replace_example_docstring(a_			)
			def __call__(       self							:			Optional[Any]	,	a_							:			int = 1	,	a_							:			Optional[Union[torch.Tensor, List[int], int]] = None	,	a_							:			int = 1	,	a_							:			List[int] = None	,	a_							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None	,	a_							:			Optional[torch.FloatTensor] = None	,	a_							:			Optional[str] = "pil"	,	a_							:			bool = True	,	a_							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,	a_							:			int = 1	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     self.unet.config.sample_size
					__UpperCAmelCase   :					str													=     self._execution_device

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

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

					# 3. Handle class_labels for class-conditional models
					__UpperCAmelCase   :					int													=     self.prepare_class_labels(a_	,	a_	,	class_labels=a_			)

					# 4. Prepare timesteps
					if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=a_	,	device=a_			)
							__UpperCAmelCase   :					Optional[Any]													=     self.scheduler.timesteps
							__UpperCAmelCase   :					int													=     len(a_			)
					else:
							self.scheduler.set_timesteps(a_			)
							__UpperCAmelCase   :					Tuple													=     self.scheduler.timesteps

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

									__UpperCAmelCase   :					Dict													=     self.scheduler.step(a_	,	a_	,	a_	,	generator=a_			)[0]

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

        # 6. Post-process image sample
					__UpperCAmelCase   :					Union[str, Any]													=     self.postprocess_image(a_	,	output_type=a_			)

					# 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=a_			)










import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image
else:

				class 		UpperCAmelCase__      :




							'''simple docstring'''






							@staticmethod
							def 			snake_case__					(       *a_							:			Union[str, Any]	,	**a_							:			Any			):
									'''simple docstring'''





									pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			UpperCamelCase      						=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




			def 			snake_case__					(       self							:			Any	,	a_							:			str	,	a_							:			Dict	,	a_							:			Dict			):
					'''simple docstring'''





					__UpperCAmelCase   :					Any													=     ImageClassificationPipeline(model=a_	,	image_processor=a_	,	top_k=2			)
					__UpperCAmelCase   :					int													=     [
					    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			),
					    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					]
					return image_classifier, examples




			def 			snake_case__					(       self							:			str	,	a_							:			Optional[int]	,	a_							:			List[str]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)

					self.assertEqual(
					    a_	,	[
					        {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					        {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					    ]	,	)

					import datasets

					__UpperCAmelCase   :					Optional[Any]													=     datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''	,	'''image'''	,	split='''test'''			)

					# Accepts URL + PIL.Image + lists
					__UpperCAmelCase   :					Any													=     image_classifier(
					    [
					        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			),
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        # RGBA
					        dataset[0]['''file'''],
					        # LA
					        dataset[1]['''file'''],
					        # L
					        dataset[2]['''file'''],
					    ]			)
					self.assertEqual(
					    a_	,	[
					        [
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					        ],
					        [
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					        ],
					        [
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					        ],
					        [
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					        ],
					        [
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					            {'''score''': ANY(a_			), '''label''': ANY(a_			)},
					        ],
					    ]	,	)




			@require_torch
			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     '''hf-internal-testing/tiny-random-vit'''
					__UpperCAmelCase   :					Optional[int]													=     pipeline('''image-classification'''	,	model=a_			)

					__UpperCAmelCase   :					Optional[int]													=     image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}]	,	)

					__UpperCAmelCase   :					Dict													=     image_classifier(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]	,	top_k=2	,	)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[
					        [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
					        [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
					    ]	,	)




			@require_tf
			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     '''hf-internal-testing/tiny-random-vit'''
					__UpperCAmelCase   :					Optional[Any]													=     pipeline('''image-classification'''	,	model=a_	,	framework='''tf'''			)

					__UpperCAmelCase   :					Optional[Any]													=     image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}]	,	)

					__UpperCAmelCase   :					Any													=     image_classifier(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]	,	top_k=2	,	)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[
					        [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
					        [{'''label''': '''LABEL_1''', '''score''': 0.5_7_4}, {'''label''': '''LABEL_0''', '''score''': 0.4_2_6}],
					    ]	,	)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     PreTrainedTokenizer()

					# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
					__UpperCAmelCase   :					Dict													=     pipeline(
					    '''image-classification'''	,	model='''hf-internal-testing/tiny-random-vit'''	,	tokenizer=a_			)

					self.assertIs(image_classifier.tokenizer	,	a_			)




			@slow
			@require_torch
			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     pipeline('''image-classification'''	,	model='''deepmind/vision-perceiver-conv'''			)
					__UpperCAmelCase   :					List[Any]													=     image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[
					        {'''score''': 0.4_3_8_5, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.3_2_1, '''label''': '''tiger cat'''},
					        {'''score''': 0.0_5_0_2, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.0_1_3_7, '''label''': '''crib, cot'''},
					        {'''score''': 0.0_0_7, '''label''': '''radiator'''},
					    ]	,	)

					__UpperCAmelCase   :					List[str]													=     pipeline('''image-classification'''	,	model='''deepmind/vision-perceiver-fourier'''			)
					__UpperCAmelCase   :					List[Any]													=     image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[
					        {'''score''': 0.5_6_5_8, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.1_3_0_9, '''label''': '''tiger cat'''},
					        {'''score''': 0.0_7_2_2, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.0_7_0_7, '''label''': '''remote control, remote'''},
					        {'''score''': 0.0_0_8_2, '''label''': '''computer keyboard, keypad'''},
					    ]	,	)

					__UpperCAmelCase   :					List[str]													=     pipeline('''image-classification'''	,	model='''deepmind/vision-perceiver-learned'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''			)
					self.assertEqual(
					    nested_simplify(a_	,	decimals=4			)	,	[
					        {'''score''': 0.3_0_2_2, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.2_3_6_2, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.1_8_5_6, '''label''': '''tiger cat'''},
					        {'''score''': 0.0_3_2_4, '''label''': '''remote control, remote'''},
					        {'''score''': 0.0_0_9_6, '''label''': '''quilt, comforter, comfort, puff'''},
					    ]	,	)





