



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


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


UpperCamelCase_									=   '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     UNetaDModel  ,lowerCamelCase_:     CMStochasticIterativeScheduler							)	->      None:
       super().__init__()

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

       UpperCAmelCase_     :       List[str]  		=					None


   def       A__    (    self:     Tuple  ,lowerCamelCase_:     Dict=0							)	->      Any:
       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_     :       Tuple  		=					torch.device(F'''cuda:{gpu_id}'''							)

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

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


   def       A__    (    self:     int  ,lowerCamelCase_:     Any=0							)	->      List[Any]:
       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_     :       Any  		=					torch.device(F'''cuda:{gpu_id}'''							)

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

       UpperCAmelCase_     :       Optional[Any]  		=					None
       for cpu_offloaded_model in [self.unet]:
           UpperCAmelCase_		, UpperCAmelCase_     :       int  		=					cpu_offload_with_hook(lowerCamelCase_  ,lowerCamelCase_  ,prev_module_hook=lowerCamelCase_							)

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

       # We'll offload the last model manually.
       UpperCAmelCase_     :       Union[str, Any]  		=					hook


   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def       A__    (    self:     Optional[int]							)	->      Optional[Any]:
       if not hasattr(self.unet  ,"""_hf_hook"""							):
           return self.device
       for module in self.unet.modules():
           if (
               hasattr(lowerCamelCase_  ,"""_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       A__    (    self:     Dict  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     int  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     str=None							)	->      Optional[int]:
       UpperCAmelCase_     :       int  		=					(batch_size, num_channels, height, width)
       if isinstance(lowerCamelCase_  ,lowerCamelCase_							) and len(lowerCamelCase_							) != batch_size:
           raise ValueError(
               F'''You have passed a list of generators of length {len(lowerCamelCase_							)}, 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_     :       Dict  		=					randn_tensor(lowerCamelCase_  ,generator=lowerCamelCase_  ,device=lowerCamelCase_  ,dtype=lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       List[Any]  		=					latents.to(device=lowerCamelCase_  ,dtype=lowerCamelCase_							)

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


   def       A__    (    self:     int  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     str = "pil"							)	->      Optional[Any]:
       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_     :       List[Any]  		=					(sample / 2 + 0.5).clamp(0  ,1							)
       if output_type == "pt":
           return sample

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

       # Output_type must be 'pil'
       UpperCAmelCase_     :       Any  		=					self.numpy_to_pil(lowerCamelCase_							)
       return sample


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


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     Union[str, Any]							)	->      Tuple:
       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(lowerCamelCase_  ,lowerCamelCase_							) or callback_steps <= 0)
       ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(lowerCamelCase_							)}.'''							)






   @torch.no_grad()
   @replace_example_docstring(lowerCamelCase_							)
   def __call__(    self:     Tuple  ,lowerCamelCase_:     int = 1  ,lowerCamelCase_:     Optional[Union[torch.Tensor, List[int], int]] = None  ,lowerCamelCase_:     int = 1  ,lowerCamelCase_:     List[int] = None  ,lowerCamelCase_:     Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowerCamelCase_:     Optional[torch.FloatTensor] = None  ,lowerCamelCase_:     Optional[str] = "pil"  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,lowerCamelCase_:     int = 1  ,)	->      int:
       UpperCAmelCase_     :       List[Any]  		=					self.unet.config.sample_size
       UpperCAmelCase_     :       Optional[int]  		=					self._execution_device

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

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

       # 3. Handle class_labels for class-conditional models
       UpperCAmelCase_     :       List[Any]  		=					self.prepare_class_labels(lowerCamelCase_  ,lowerCamelCase_  ,class_labels=lowerCamelCase_							)

       # 4. Prepare timesteps
       if timesteps is not None:
           self.scheduler.set_timesteps(timesteps=lowerCamelCase_  ,device=lowerCamelCase_							)
           UpperCAmelCase_     :       Tuple  		=					self.scheduler.timesteps
           UpperCAmelCase_     :       Tuple  		=					len(lowerCamelCase_							)
       else:
           self.scheduler.set_timesteps(lowerCamelCase_							)
           UpperCAmelCase_     :       Tuple  		=					self.scheduler.timesteps

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

               UpperCAmelCase_     :       Union[str, Any]  		=					self.scheduler.step(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_  ,generator=lowerCamelCase_							)[0]

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

        # 6. Post-process image sample
       UpperCAmelCase_     :       Tuple  		=					self.postprocess_image(lowerCamelCase_  ,output_type=lowerCamelCase_							)

       # 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=lowerCamelCase_							)




'''simple docstring'''

import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       KandinskyVaaPriorEmbaEmbPipeline
 _lowerCamelCase			:       Tuple            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       [
     'num_images_per_prompt',
     'strength',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       List[str]            =       False

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						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 100

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



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

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       )
    return CLIPTextModelWithProjection(a_       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    torch.manual_seed(0       )

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

    UpperCamelCase__			    =    PriorTransformer(**a_       )
    # 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
    UpperCamelCase__			    =    nn.Parameter(torch.ones(model.clip_std.shape       )       )
    return model

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    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,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.dummy_prior
    UpperCamelCase__			    =    self.dummy_image_encoder
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    self.dummy_tokenizer
    UpperCamelCase__			    =    self.dummy_image_processor

    UpperCamelCase__			    =    UnCLIPScheduler(
        variance_type="fixed_small_log",       prediction_type="sample",       num_train_timesteps=1000,       clip_sample=a_,       clip_sample_range=10.0,       )

    UpperCamelCase__			    =    {
        "prior": prior,
        "image_encoder": image_encoder,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "scheduler": scheduler,
        "image_processor": image_processor,
    }

    return components

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						List[str],       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 64, 64),       rng=random.Random(a_       )       ).to(a_       )
    UpperCamelCase__			    =    image.cpu().permute(0,       2,       3,       1       )[0]
    UpperCamelCase__			    =    Image.fromarray(np.uinta(a_       )       ).convert("RGB"       ).resize((256, 256)       )

    UpperCamelCase__			    =    {
        "prompt": "horse",
        "image": init_image,
        "strength": 0.5,
        "generator": generator,
        "guidance_scale": 4.0,
        "num_inference_steps": 2,
        "output_type": "np",
    }
    return inputs

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

    UpperCamelCase__			    =    self.pipeline_class(**a_       )
    UpperCamelCase__			    =    pipe.to(a_       )

    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    pipe(**self.get_dummy_inputs(a_       )       )
    UpperCamelCase__			    =    output.image_embeds

    UpperCamelCase__			    =    pipe(
        **self.get_dummy_inputs(a_       ),       return_dict=a_,       )[0]

    UpperCamelCase__			    =    image[0, -10:]
    UpperCamelCase__			    =    image_from_tuple[0, -10:]

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [
            0.1_071_284,
            1.3_330_271,
            0.61_260_223,
            -0.6_691_065,
            -0.3_846_852,
            -1.0_303_661,
            0.22_716_111,
            0.03_348_901,
            0.30_040_675,
            -0.24_805_029,
        ]       )

    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 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

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

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

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