


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 lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       super().__init__()

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

       snake_case_			= None



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase=0   ):
       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_			= torch.device(F'''cuda:{gpu_id}'''   )

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

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



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase=0   ):
       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_			= torch.device(F'''cuda:{gpu_id}'''   )

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

       snake_case_			= None
       for cpu_offloaded_model in [self.unet]:
             snake_case_			,       snake_case_			= cpu_offload_with_hook(_UpperCAmelCase						, _UpperCAmelCase						, prev_module_hook=_UpperCAmelCase   )

       if self.safety_checker is not None:
             snake_case_			,       snake_case_			= cpu_offload_with_hook(self.safety_checker						, _UpperCAmelCase						, prev_module_hook=_UpperCAmelCase   )

       # We'll offload the last model manually.
       snake_case_			= hook



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		UpperCamelCase__	(      self   ):
       if not hasattr(self.unet						, '''_hf_hook'''   ):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase						, '''_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 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase=None   ):
       snake_case_			= (batch_size, num_channels, height, width)
       if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) and len(_UpperCAmelCase   ) != batch_size:
             raise ValueError(
                 F'''You have passed a list of generators of length {len(_UpperCAmelCase   )}, 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_			= randn_tensor(_UpperCAmelCase						, generator=_UpperCAmelCase						, device=_UpperCAmelCase						, dtype=_UpperCAmelCase   )
       else:
             snake_case_			= latents.to(device=_UpperCAmelCase						, dtype=_UpperCAmelCase   )

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case_			= latents * self.scheduler.init_noise_sigma
       return latents



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = "pil"   ):
       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_			= (sample / 2 + 0.5).clamp(0						, 1   )
       if output_type == "pt":
             return sample

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

       # Output_type must be 'pil'
       snake_case_			= self.numpy_to_pil(_UpperCAmelCase   )
       return sample



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase=None   ):
       if self.unet.config.num_class_embeds is not None:
             if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ):
                   snake_case_			= torch.tensor(_UpperCAmelCase						, dtype=torch.int   )
             elif isinstance(_UpperCAmelCase						, _UpperCAmelCase   ):
                   assert batch_size == 1, "Batch size must be 1 if classes is an int"
                   snake_case_			= 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_			= torch.randint(0						, self.unet.config.num_class_embeds						, size=(batch_size,)   )
             snake_case_			= class_labels.to(_UpperCAmelCase   )
       else:
             snake_case_			= None
       return class_labels



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       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_			= (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(_UpperCAmelCase						, _UpperCAmelCase   ) or callback_steps <= 0)
       ):
             raise ValueError(
                 F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                 F''' {type(_UpperCAmelCase   )}.'''   )





 @torch.no_grad()
 @replace_example_docstring(_UpperCAmelCase   )
 def __call__(      self						, _UpperCAmelCase = 1						, _UpperCAmelCase = None						, _UpperCAmelCase = 1						, _UpperCAmelCase = None						, _UpperCAmelCase = None						, _UpperCAmelCase = None						, _UpperCAmelCase = "pil"						, _UpperCAmelCase = True						, _UpperCAmelCase = None						, _UpperCAmelCase = 1						, ):
       snake_case_			= self.unet.config.sample_size
       snake_case_			= self._execution_device

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

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

       # 3. Handle class_labels for class-conditional models
       snake_case_			= self.prepare_class_labels(_UpperCAmelCase						, _UpperCAmelCase						, class_labels=_UpperCAmelCase   )

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

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

                   snake_case_			= self.scheduler.step(_UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, generator=_UpperCAmelCase   )[0]

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

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

       # 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=_UpperCAmelCase   )



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 lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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=10_00						, )
       return CLIPTextModelWithProjection(_UpperCAmelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

       snake_case_			= {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_			= PriorTransformer(**_UpperCAmelCase   )
       # 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
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						, image_size=2_24						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=_UpperCAmelCase						, do_normalize=_UpperCAmelCase						, do_resize=_UpperCAmelCase						, 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=2_24						, )

       return image_processor



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

       snake_case_			= UnCLIPScheduler(
           variance_type='''fixed_small_log'''						, prediction_type='''sample'''						, num_train_timesteps=10_00						, clip_sample=_UpperCAmelCase						, clip_sample_range=10.0						, )

       snake_case_			= {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

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



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

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

       assert image.shape == (1, 32)

       snake_case_			= 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 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

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





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

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