


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							:							Optional[int]    							=					logging.get_logger(__name__)  # pylint: disable=invalid-name


__UpperCamelCase							:							List[Any]    							=					"""
    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    __magic_name__						(  __A):


     def __init__(   self				:  Optional[int]				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  Optional[int]      )	->   str:





      '''simple docstring'''






      super().__init__()

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

      UpperCamelCase__       :     Optional[int] 							= None


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  Dict=0      )	->   int:





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

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

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


     def   UpperCAmelCase__						(   self				:  List[str]				,						lowerCamelCase__				:  List[Any]=0      )	->   Dict:





      '''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__       :     List[Any] 							= torch.device(F"cuda:{gpu_id}"      )

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

      UpperCamelCase__       :     Dict 							= None
      for cpu_offloaded_model in [self.unet]:
       UpperCamelCase__       :     Optional[Any] 							= cpu_offload_with_hook(_lowercase				,						_lowercase				,						prev_module_hook=_lowercase      )

      if self.safety_checker is not None:
       UpperCamelCase__       :     Tuple 							= cpu_offload_with_hook(self.safety_checker				,						_lowercase				,						prev_module_hook=_lowercase      )

      # We'll offload the last model manually.
      UpperCamelCase__       :     List[str] 							= hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   str:





      '''simple docstring'''






      if not hasattr(self.unet				,						'''_hf_hook'''      ):
       return self.device
      for module in self.unet.modules():
       if (
           hasattr(_lowercase				,						'''_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				:  Optional[int]				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  str				,						lowerCamelCase__				:  Tuple				,						lowerCamelCase__				:  Union[str, Any]				,						lowerCamelCase__				:  str				,						lowerCamelCase__				:  List[str]				,						lowerCamelCase__				:  str				,						lowerCamelCase__				:  Optional[Any]=None      )	->   List[Any]:





      '''simple docstring'''






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

      # 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   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  Optional[Any] = "pil"      )	->   Optional[Any]:





      '''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__       :     Any 							= (sample / 2 + 0.5).clamp(0				,						1      )
      if output_type == "pt":
       return sample

      # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
      UpperCamelCase__       :     List[str] 							= 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(_lowercase      )
      return sample


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  Any				,						lowerCamelCase__				:  List[str]				,						lowerCamelCase__				:  int=None      )	->   Dict:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  Union[str, Any]				,						lowerCamelCase__				:  Any				,						lowerCamelCase__				:  Dict				,						lowerCamelCase__				:  Dict				,						lowerCamelCase__				:  Union[str, Any]				,						lowerCamelCase__				:  Dict				,						lowerCamelCase__				:  Optional[int]      )	->   Tuple:





      '''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__       :     Tuple 							= (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(_lowercase				,						_lowercase      ) or callback_steps <= 0)
      ):
       raise ValueError(
           F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
           F" {type(_lowercase      )}."      )


     @torch.no_grad()
     @replace_example_docstring(_lowercase      )
     def __call__(   self				:  Optional[int]				,						lowerCamelCase__				:  Dict = 1				,						lowerCamelCase__				:  Optional[int] = None				,						lowerCamelCase__				:  str = 1				,						lowerCamelCase__				:  Dict = None				,						lowerCamelCase__				:  Any = None				,						lowerCamelCase__				:  Tuple = None				,						lowerCamelCase__				:  Any = "pil"				,						lowerCamelCase__				:  Any = True				,						lowerCamelCase__				:  List[Any] = None				,						lowerCamelCase__				:  List[Any] = 1				,						)	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= self.unet.config.sample_size
      UpperCamelCase__       :     List[str] 							= self._execution_device

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

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

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

      # 4. Prepare timesteps
      if timesteps is not None:
       self.scheduler.set_timesteps(timesteps=_lowercase				,						device=_lowercase      )
       UpperCamelCase__       :     int 							= self.scheduler.timesteps
       UpperCamelCase__       :     List[Any] 							= len(_lowercase      )
      else:
       self.scheduler.set_timesteps(_lowercase      )
       UpperCamelCase__       :     List[Any] 							= self.scheduler.timesteps

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

        UpperCamelCase__       :     List[Any] 							= self.scheduler.step(_lowercase				,						_lowercase				,						_lowercase				,						generator=_lowercase      )[0]

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

        # 6. Post-process image sample
      UpperCamelCase__       :     Any 							= self.postprocess_image(_lowercase				,						output_type=_lowercase      )

      # 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=_lowercase      )









import numpy as np






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       str      )						->      Union[str, Any]:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							str			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		abs(predict - actual      )
     lowerCAmelCase_       :							Dict			 =		difference.mean()

     return score






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							int			 =		np.array(_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		predict - actual
     lowerCAmelCase_       :							Tuple			 =		np.square(_a      )

     lowerCAmelCase_       :							Tuple			 =		square_diff.mean()
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Tuple      )						->      Optional[int]:
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							int			 =		predict - actual
     lowerCAmelCase_       :							Any			 =		np.square(_a      )
     lowerCAmelCase_       :							Union[str, Any]			 =		square_diff.mean()
     lowerCAmelCase_       :							Optional[Any]			 =		np.sqrt(_a      )
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Optional[Any]      )						->      List[Any]:
     lowerCAmelCase_       :							Dict			 =		np.array(_a      )
     lowerCAmelCase_       :							List[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							str			 =		np.log(predict + 1      )
     lowerCAmelCase_       :							Any			 =		np.log(actual + 1      )

     lowerCAmelCase_       :							List[str]			 =		log_predict - log_actual
     lowerCAmelCase_       :							Dict			 =		np.square(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		square_diff.mean()

     lowerCAmelCase_       :							int			 =		np.sqrt(_a      )

     return score






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]      )						->      int:
     lowerCAmelCase_       :							Any			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[int]			 =		predict - actual
     lowerCAmelCase_       :							str			 =		np.sum(_a      ) / len(_a      )
     lowerCAmelCase_       :							List[str]			 =		np.sum(_a      ) / len(_a      )
     # print(numerator, denumerator)
     lowerCAmelCase_       :							int			 =		float(_a      ) / denumerator * 1_00

     return score







def 				_lowerCAmelCase			(	_a :       Any       , _a :       Union[str, Any]      )						->      str:
     return np.mean(np.array(_a      ) == np.array(_a      )      )
