
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							torch.FloatTensor
     UpperCAmelCase__				:							Optional[torch.FloatTensor]           =		None



def 						lowercase_			(   A__ ,		A__=0.999 ,		A__="cosine" ,		)							->							List[Any]:
    """simple docstring"""

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(A__		):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(A__		):
            return math.exp(t * -12.0		)

    else:
        raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'		)

    snake_case      =					[]
    for i in range(A__		):
        snake_case      =					i / num_diffusion_timesteps
        snake_case      =					(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(A__		) / alpha_bar_fn(A__		) ,		A__		)		)
    return torch.tensor(A__ ,		dtype=torch.floataa		)







class      lowerCamelCase		(  A_							, A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		[e.name for e in KarrasDiffusionSchedulers]
     UpperCAmelCase__				:							Optional[int]           =		1





     @register_to_config
     def __init__(self      :    int				,    _A      :    int = 1_0_0_0				,    _A      :    float = 0.00_01				,    _A      :    float = 0.02				,    _A      :    str = "linear"				,    _A      :    Optional[Union[np.ndarray, List[float]]] = None				,    _A      :    str = "epsilon"				,    _A      :    str = "linear"				,    _A      :    Optional[bool] = False				,    _A      :    str = "linspace"				,    _A      :    int = 0				,    )						->						List[str]:
         if trained_betas is not None:
             snake_case      =					torch.tensor(_A				,    dtype=torch.floataa   )
         elif beta_schedule == "linear":
             snake_case      =					torch.linspace(_A				,    _A				,    _A				,    dtype=torch.floataa   )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             snake_case      =					(
                 torch.linspace(beta_start**0.5				,    beta_end**0.5				,    _A				,    dtype=torch.floataa   ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             snake_case      =					betas_for_alpha_bar(_A   )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

         snake_case      =					1.0 - self.betas
         snake_case      =					torch.cumprod(self.alphas				,    dim=0   )

         snake_case      =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
         snake_case      =					np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
         snake_case      =					torch.from_numpy(_A   )

         # setable values
         snake_case      =					None
         snake_case      =					np.linspace(0				,    num_train_timesteps - 1				,    _A				,    dtype=_A   )[::-1].copy()
         snake_case      =					torch.from_numpy(_A   )
         snake_case      =					False
         snake_case      =					use_karras_sigmas





     @property
     def 		UpperCAmelCase(self      :    Dict   )						->						int:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5





     def 		UpperCAmelCase(self      :    str				,    _A      :    torch.FloatTensor				,    _A      :    Union[float, torch.FloatTensor]   )						->						torch.FloatTensor:
         if isinstance(_A				,    torch.Tensor   ):
             snake_case      =					timestep.to(self.timesteps.device   )
         snake_case      =					(self.timesteps == timestep).nonzero().item()
         snake_case      =					self.sigmas[step_index]

         snake_case      =					sample / ((sigma**2 + 1) ** 0.5)

         snake_case      =					True
         return sample





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    int				,    _A      :    Union[str, torch.device] = None   )						->						Optional[int]:
         snake_case      =					num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             snake_case      =					np.linspace(0				,    self.config.num_train_timesteps - 1				,    _A				,    dtype=_A   )[
                 ::-1
             ].copy()
         elif self.config.timestep_spacing == "leading":
             snake_case      =					self.config.num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             snake_case      =					(np.arange(0				,    _A   ) * step_ratio).round()[::-1].copy().astype(_A   )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             snake_case      =					self.config.num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             snake_case      =					(np.arange(self.config.num_train_timesteps				,    0				,    -step_ratio   )).round().copy().astype(_A   )
             timesteps -= 1
         else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'   )

         snake_case      =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
         snake_case      =					np.log(_A   )

         if self.config.interpolation_type == "linear":
             snake_case      =					np.interp(_A				,    np.arange(0				,    len(_A   )   )				,    _A   )
         elif self.config.interpolation_type == "log_linear":
             snake_case      =					torch.linspace(np.log(sigmas[-1]   )				,    np.log(sigmas[0]   )				,    num_inference_steps + 1   ).exp()
         else:
             raise ValueError(
                 f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                 " 'linear' or 'log_linear'"   )

         if self.use_karras_sigmas:
             snake_case      =					self._convert_to_karras(in_sigmas=_A				,    num_inference_steps=self.num_inference_steps   )
             snake_case      =					np.array([self._sigma_to_t(_A				,    _A   ) for sigma in sigmas]   )

         snake_case      =					np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
         snake_case      =					torch.from_numpy(_A   ).to(device=_A   )
         if str(_A   ).startswith("mps"   ):
             # mps does not support float64
             snake_case      =					torch.from_numpy(_A   ).to(_A				,    dtype=torch.floataa   )
         else:
             snake_case      =					torch.from_numpy(_A   ).to(device=_A   )





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    Union[str, Any]				,    _A      :    List[Any]   )						->						Tuple:
         # get log sigma
         snake_case      =					np.log(_A   )

         # get distribution
         snake_case      =					log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         snake_case      =					np.cumsum((dists >= 0)				,    axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
         snake_case      =					low_idx + 1

         snake_case      =					log_sigmas[low_idx]
         snake_case      =					log_sigmas[high_idx]

         # interpolate sigmas
         snake_case      =					(low - log_sigma) / (low - high)
         snake_case      =					np.clip(_A				,    0				,    1   )

         # transform interpolation to time range
         snake_case      =					(1 - w) * low_idx + w * high_idx
         snake_case      =					t.reshape(sigma.shape   )
         return t





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    torch.FloatTensor				,    _A      :    Union[str, Any]   )						->						torch.FloatTensor:
         snake_case      =					in_sigmas[-1].item()
         snake_case      =					in_sigmas[0].item()

         snake_case      =					7.0  # 7.0 is the value used in the paper
         snake_case      =					np.linspace(0				,    1				,    _A   )
         snake_case      =					sigma_min ** (1 / rho)
         snake_case      =					sigma_max ** (1 / rho)
         snake_case      =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas





     def 		UpperCAmelCase(self      :    Union[str, Any]				,    _A      :    torch.FloatTensor				,    _A      :    Union[float, torch.FloatTensor]				,    _A      :    torch.FloatTensor				,    _A      :    float = 0.0				,    _A      :    float = 0.0				,    _A      :    float = float("inf"   )				,    _A      :    float = 1.0				,    _A      :    Optional[torch.Generator] = None				,    _A      :    bool = True				,    )						->						Union[EulerDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(_A				,    _A   )
             or isinstance(_A				,    torch.IntTensor   )
             or isinstance(_A				,    torch.LongTensor   )
         ):
             raise ValueError(
                 (
                     "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                     " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                     " one of the `scheduler.timesteps` as a timestep."
                 )				,    )

         if not self.is_scale_input_called:
             logger.warning(
                 "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                 "See `StableDiffusionPipeline` for a usage example."   )

         if isinstance(_A				,    torch.Tensor   ):
             snake_case      =					timestep.to(self.timesteps.device   )

         snake_case      =					(self.timesteps == timestep).nonzero().item()
         snake_case      =					self.sigmas[step_index]

         snake_case      =					min(s_churn / (len(self.sigmas   ) - 1)				,    2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

         snake_case      =					randn_tensor(
             model_output.shape				,    dtype=model_output.dtype				,    device=model_output.device				,    generator=_A   )

         snake_case      =					noise * s_noise
         snake_case      =					sigma * (gamma + 1)

         if gamma > 0:
             snake_case      =					sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
             snake_case      =					model_output
         elif self.config.prediction_type == "epsilon":
             snake_case      =					sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             snake_case      =					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'   )

         # 2. Convert to an ODE derivative
         snake_case      =					(sample - pred_original_sample) / sigma_hat

         snake_case      =					self.sigmas[step_index + 1] - sigma_hat

         snake_case      =					sample + derivative * dt

         if not return_dict:
             return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=_A				,    pred_original_sample=_A   )





     def 		UpperCAmelCase(self      :    int				,    _A      :    torch.FloatTensor				,    _A      :    torch.FloatTensor				,    _A      :    torch.FloatTensor				,    )						->						torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         snake_case      =					self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype   )
         if original_samples.device.type == "mps" and torch.is_floating_point(_A   ):
             # mps does not support float64
             snake_case      =					self.timesteps.to(original_samples.device				,    dtype=torch.floataa   )
             snake_case      =					timesteps.to(original_samples.device				,    dtype=torch.floataa   )
         else:
             snake_case      =					self.timesteps.to(original_samples.device   )
             snake_case      =					timesteps.to(original_samples.device   )

         snake_case      =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

         snake_case      =					sigmas[step_indices].flatten()
         while len(sigma.shape   ) < len(original_samples.shape   ):
             snake_case      =					sigma.unsqueeze(-1   )

         snake_case      =					original_samples + noise * sigma
         return noisy_samples





     def __len__(self      :    Optional[Any]   )						->						List[str]:
         return self.config.num_train_timesteps



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      lowerCamelCase		(  A_  ):





     def __init__(self      :    Tuple				,    _A      :    UNetaDModel				,    _A      :    CMStochasticIterativeScheduler   )						->						None:
         super().__init__()

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

         snake_case      =					None





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]=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"   )

         snake_case      =					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 		UpperCAmelCase(self      :    str				,    _A      :    Optional[Any]=0   )						->						Union[str, 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."   )

         snake_case      =					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)

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

         if self.safety_checker is not None:
             snake_case     ,							snake_case      =					cpu_offload_with_hook(self.safety_checker				,    _A				,    prev_module_hook=_A   )

         # 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      :    Dict   )						->						Any:
         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 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]				,    _A      :    Tuple				,    _A      :    List[str]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    List[str]				,    _A      :    List[str]				,    _A      :    Optional[Any]=None   )						->						Tuple:
         snake_case      =					(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:
             snake_case      =					randn_tensor(_A				,    generator=_A				,    device=_A				,    dtype=_A   )
         else:
             snake_case      =					latents.to(device=_A				,    dtype=_A   )

         # 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      :    List[Any]				,    _A      :    torch.FloatTensor				,    _A      :    str = "pil"   )						->						str:
         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(_A   )
         return sample





     def 		UpperCAmelCase(self      :    str				,    _A      :    List[Any]				,    _A      :    Optional[Any]				,    _A      :    List[Any]=None   )						->						List[str]:
         if self.unet.config.num_class_embeds is not None:
             if isinstance(_A				,    _A   ):
                 snake_case      =					torch.tensor(_A				,    dtype=torch.int   )
             elif isinstance(_A				,    _A   ):
                 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(_A   )
         else:
             snake_case      =					None
         return class_labels





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    Tuple				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    str   )						->						Optional[Any]:
         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(_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      :    Union[str, 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				,    )						->						List[str]:
         snake_case      =					self.unet.config.sample_size
         snake_case      =					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)
         snake_case      =					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
         snake_case      =					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   )
             snake_case      =					self.scheduler.timesteps
             snake_case      =					len(_A   )
         else:
             self.scheduler.set_timesteps(_A   )
             snake_case      =					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   ):
                 snake_case      =					self.scheduler.scale_model_input(_A				,    _A   )
                 snake_case      =					self.unet(_A				,    _A				,    class_labels=_A				,    return_dict=_A   )[0]

                 snake_case      =					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
         snake_case      =					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   )

