





"""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


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


lowerCamelCase_	:		Union[str, Any]         =   '\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   , snake_case_   , snake_case_    ):

        """simple docstring"""

        super().__init__()

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

        A_    :			List[Any]									=    None

      def    lowerCamelCase_     (	self   , 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'    )

        A_    :			Optional[int]									=    torch.device(F"""cuda:{gpu_id}"""    )

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

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

      def    lowerCamelCase_     (	self   , 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.'    )

        A_    :			Any									=    torch.device(F"""cuda:{gpu_id}"""    )

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

        A_    :			Optional[int]									=    None
        for cpu_offloaded_model in [self.unet]:
          A_		,			A_    :			List[Any]									=    cpu_offload_with_hook(snake_case_   , snake_case_   , prev_module_hook=snake_case_    )

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

        # We'll offload the last model manually.
        A_    :			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(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   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        A_    :			Optional[int]									=    (batch_size, num_channels, height, width)
        if isinstance(snake_case_   , snake_case_    ) and len(snake_case_    ) != batch_size:
          raise ValueError(
              F"""You have passed a list of generators of length {len(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:
          A_    :			List[Any]									=    randn_tensor(snake_case_   , generator=snake_case_   , device=snake_case_   , dtype=snake_case_    )
        else:
          A_    :			Optional[int]									=    latents.to(device=snake_case_   , dtype=snake_case_    )

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

      def    lowerCamelCase_     (	self   , snake_case_   , 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
        A_    :			Tuple									=    (sample / 2 + 0.5).clamp(0   , 1    )
        if output_type == "pt":
          return sample

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

        # Output_type must be 'pil'
        A_    :			str									=    self.numpy_to_pil(snake_case_    )
        return sample

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

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_   , 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:
          A_    :			List[Any]									=    (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(snake_case_   , 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(snake_case_    )}."""    )

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

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.unet.config.sample_size
        A_    :			Any									=    self._execution_device

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

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

        # 3. Handle class_labels for class-conditional models
        A_    :			List[str]									=    self.prepare_class_labels(snake_case_   , snake_case_   , class_labels=snake_case_    )

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=snake_case_   , device=snake_case_    )
          A_    :			Tuple									=    self.scheduler.timesteps
          A_    :			List[Any]									=    len(snake_case_    )
        else:
          self.scheduler.set_timesteps(snake_case_    )
          A_    :			str									=    self.scheduler.timesteps

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

            A_    :			Any									=    self.scheduler.step(snake_case_   , snake_case_   , snake_case_   , generator=snake_case_    )[0]

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

        # 6. Post-process image sample
        A_    :			str									=    self.postprocess_image(snake_case_   , output_type=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=snake_case_    )





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   """hf-internal-testing/tiny-stable-diffusion-pipe"""
				SCREAMING_SNAKE_CASE_			:				str									=   1
				SCREAMING_SNAKE_CASE_			:				Dict									=   77
				SCREAMING_SNAKE_CASE_			:				List[str]									=   32
				SCREAMING_SNAKE_CASE_			:				List[str]									=   4
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   64
				SCREAMING_SNAKE_CASE_			:				int									=   torch.manual_seed(0  )





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[Any]=0.9_999				)   ->	Tuple:
							a       =     UNetaDConditionModel.from_pretrained(self.model_id   , subfolder="unet"				)
							a       =     unet.to(__lowerCamelCase				)
							a       =     EMAModel(unet.parameters()   , decay=__lowerCamelCase   , model_cls=__lowerCamelCase   , model_config=unet.config				)
							return unet, ema_unet





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Any:
							a       =     torch.randn(
							    self.batch_size   , self.num_in_channels   , self.latent_height   , self.latent_width   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randint(0   , 10_00   , size=(self.batch_size,)   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randn(
							    self.batch_size   , self.prompt_length   , self.text_encoder_hidden_dim   , generator=self.generator				).to(__lowerCamelCase				)
							return noisy_latents, timesteps, encoder_hidden_states





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Any				)   ->	Optional[int]:
							a       =     {}
							for k, param in unet.state_dict().items():
										a       =     torch.randn_like(__lowerCamelCase				) + (param * torch.randn_like(__lowerCamelCase				))
										updated_state_dict.update({k: updated_param}				)
							unet.load_state_dict(__lowerCamelCase				)
							return unet





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2				):
										ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 3





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[int]:
							a       ,						a       =     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()				)
							a       =     list(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							a       ,						a       =     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							a       =     self.simulate_backprop(__lowerCamelCase				)

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()				)

							# Now the EMA'd parameters won't be equal to the original model parameters.
							a       =     list(unet_pseudo_updated_step_one.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Any:
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							a       ,						a       =     self.get_models()

							# First backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							# Second backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							a       ,						a       =     self.get_models(decay=0.0				)
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Dict:
							a       ,						a       =     self.get_models()
							a       ,						a       ,						a       =     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(__lowerCamelCase				)
										a       =     UNetaDConditionModel.from_pretrained(__lowerCamelCase   , model_cls=__lowerCamelCase				)
										a       =     loaded_unet.to(unet.device				)

							# Since no EMA step has been performed the outputs should match.
							a       =     unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample
							a       =     loaded_unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample

							assert torch.allclose(__lowerCamelCase   , __lowerCamelCase   , atol=1e-4				)






