
from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

__UpperCAmelCase							=			"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase=8		):
   SCREAMING_SNAKE_CASE_     =	h // scale_factor**2
   if h % scale_factor**2 != 0:
      new_h += 1
   SCREAMING_SNAKE_CASE_     =	w // scale_factor**2
   if w % scale_factor**2 != 0:
      new_w += 1
   return new_h * scale_factor, new_w * scale_factor





class 							UpperCamelCase__						(     a__     ):






       """simple docstring"""


       def __init__(				self      ,			_A      ,			_A      ,			_A      ,			_A      ,			_A      ,			)							->							Union[str, Any]:
          super().__init__()

          self.register_modules(
              text_encoder=SCREAMING_SNAKE_CASE_      ,			tokenizer=SCREAMING_SNAKE_CASE_      ,			unet=SCREAMING_SNAKE_CASE_      ,			scheduler=SCREAMING_SNAKE_CASE_      ,			movq=SCREAMING_SNAKE_CASE_      ,			)
          SCREAMING_SNAKE_CASE_     =	2 ** (len(self.movq.config.block_out_channels     ) - 1)



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A      ,			_A      ,			_A      ,			_A     )							->							Optional[Any]:
          if latents is None:
             SCREAMING_SNAKE_CASE_     =	randn_tensor(SCREAMING_SNAKE_CASE_      ,			generator=SCREAMING_SNAKE_CASE_      ,			device=SCREAMING_SNAKE_CASE_      ,			dtype=SCREAMING_SNAKE_CASE_     )
          else:
             if latents.shape != shape:
                raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
             SCREAMING_SNAKE_CASE_     =	latents.to(SCREAMING_SNAKE_CASE_     )

          SCREAMING_SNAKE_CASE_     =	latents * scheduler.init_noise_sigma
          return latents



       def   _UpperCamelCase     (				self      ,			_A      ,			_A      ,			_A      ,			_A      ,			_A=None      ,			)							->							Optional[int]:
          SCREAMING_SNAKE_CASE_     =	len(SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ) else 1
          # get prompt text embeddings
          SCREAMING_SNAKE_CASE_     =	self.tokenizer(
              SCREAMING_SNAKE_CASE_      ,			padding='''max_length'''      ,			truncation=SCREAMING_SNAKE_CASE_      ,			max_length=77      ,			return_attention_mask=SCREAMING_SNAKE_CASE_      ,			add_special_tokens=SCREAMING_SNAKE_CASE_      ,			return_tensors='''pt'''      ,			)

          SCREAMING_SNAKE_CASE_     =	text_inputs.input_ids
          SCREAMING_SNAKE_CASE_     =	self.tokenizer(SCREAMING_SNAKE_CASE_      ,			padding='''longest'''      ,			return_tensors='''pt'''     ).input_ids

          if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ):
             SCREAMING_SNAKE_CASE_     =	self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )

          SCREAMING_SNAKE_CASE_     =	text_input_ids.to(SCREAMING_SNAKE_CASE_     )
          SCREAMING_SNAKE_CASE_     =	text_inputs.attention_mask.to(SCREAMING_SNAKE_CASE_     )

          SCREAMING_SNAKE_CASE_     =	self.text_encoder(
              input_ids=SCREAMING_SNAKE_CASE_      ,			attention_mask=SCREAMING_SNAKE_CASE_     )

          SCREAMING_SNAKE_CASE_     =	prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_      ,			dim=0     )
          SCREAMING_SNAKE_CASE_     =	text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_      ,			dim=0     )
          SCREAMING_SNAKE_CASE_     =	text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_      ,			dim=0     )

          if do_classifier_free_guidance:
             SCREAMING_SNAKE_CASE_     =	42
             if negative_prompt is None:
                SCREAMING_SNAKE_CASE_     =	[''] * batch_size
             elif type(SCREAMING_SNAKE_CASE_     ) is not type(SCREAMING_SNAKE_CASE_     ):
                raise TypeError(
                    F'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_     )} !='''
                    F''' {type(SCREAMING_SNAKE_CASE_     )}.'''     )
             elif isinstance(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ):
                SCREAMING_SNAKE_CASE_     =	[negative_prompt]
             elif batch_size != len(SCREAMING_SNAKE_CASE_     ):
                raise ValueError(
                    F'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_     )}, but `prompt`:'''
                    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                    ''' the batch size of `prompt`.'''     )
             else:
                SCREAMING_SNAKE_CASE_     =	negative_prompt

             SCREAMING_SNAKE_CASE_     =	self.tokenizer(
                 SCREAMING_SNAKE_CASE_      ,			padding='''max_length'''      ,			max_length=77      ,			truncation=SCREAMING_SNAKE_CASE_      ,			return_attention_mask=SCREAMING_SNAKE_CASE_      ,			add_special_tokens=SCREAMING_SNAKE_CASE_      ,			return_tensors='''pt'''      ,			)
             SCREAMING_SNAKE_CASE_     =	uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_     )
             SCREAMING_SNAKE_CASE_     =	uncond_input.attention_mask.to(SCREAMING_SNAKE_CASE_     )

             SCREAMING_SNAKE_CASE_     =	self.text_encoder(
                 input_ids=SCREAMING_SNAKE_CASE_      ,			attention_mask=SCREAMING_SNAKE_CASE_     )

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             SCREAMING_SNAKE_CASE_     =	negative_prompt_embeds.shape[1]
             SCREAMING_SNAKE_CASE_     =	negative_prompt_embeds.repeat(1      ,			SCREAMING_SNAKE_CASE_     )
             SCREAMING_SNAKE_CASE_     =	negative_prompt_embeds.view(batch_size * num_images_per_prompt      ,			SCREAMING_SNAKE_CASE_     )

             SCREAMING_SNAKE_CASE_     =	uncond_text_encoder_hidden_states.shape[1]
             SCREAMING_SNAKE_CASE_     =	uncond_text_encoder_hidden_states.repeat(1      ,			SCREAMING_SNAKE_CASE_      ,			1     )
             SCREAMING_SNAKE_CASE_     =	uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt      ,			SCREAMING_SNAKE_CASE_      ,			-1     )
             SCREAMING_SNAKE_CASE_     =	uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_      ,			dim=0     )

             # done duplicates

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             SCREAMING_SNAKE_CASE_     =	torch.cat([negative_prompt_embeds, prompt_embeds]     )
             SCREAMING_SNAKE_CASE_     =	torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

             SCREAMING_SNAKE_CASE_     =	torch.cat([uncond_text_mask, text_mask]     )

          return prompt_embeds, text_encoder_hidden_states, text_mask



       def   _UpperCamelCase     (				self      ,			_A=0     )							->							Tuple:
          if is_accelerate_available():
             from accelerate import cpu_offload
          else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''     )

          SCREAMING_SNAKE_CASE_     =	torch.device(F'''cuda:{gpu_id}'''     )

          SCREAMING_SNAKE_CASE_     =	[
              self.unet,
              self.text_encoder,
              self.movq,
          ]
          for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                cpu_offload(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     )



       def   _UpperCamelCase     (				self      ,			_A=0     )							->							Dict:
          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.'''     )

          SCREAMING_SNAKE_CASE_     =	torch.device(F'''cuda:{gpu_id}'''     )

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

          SCREAMING_SNAKE_CASE_     =	None
          for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
             SCREAMING_SNAKE_CASE_     =	cpu_offload_with_hook(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			prev_module_hook=SCREAMING_SNAKE_CASE_     )

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

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



       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def   _UpperCamelCase     (				self     )							->							Optional[Any]:
          if not hasattr(self.unet      ,			'''_hf_hook'''     ):
             return self.device
          for module in self.unet.modules():
             if (
                 hasattr(SCREAMING_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




       @torch.no_grad()
       @replace_example_docstring(SCREAMING_SNAKE_CASE_     )
       def __call__(				self      ,			_A      ,			_A      ,			_A      ,			_A = None      ,			_A = 512      ,			_A = 512      ,			_A = 100      ,			_A = 4.0      ,			_A = 1      ,			_A = None      ,			_A = None      ,			_A = "pil"      ,			_A = True      ,			)							->							str:

          if isinstance(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ):
             SCREAMING_SNAKE_CASE_     =	1
          elif isinstance(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ):
             SCREAMING_SNAKE_CASE_     =	len(SCREAMING_SNAKE_CASE_     )
          else:
             raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_     )}'''     )

          SCREAMING_SNAKE_CASE_     =	self._execution_device

          SCREAMING_SNAKE_CASE_     =	batch_size * num_images_per_prompt
          SCREAMING_SNAKE_CASE_     =	guidance_scale > 1.0

          SCREAMING_SNAKE_CASE_     =	self._encode_prompt(
              SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     )

          if isinstance(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ):
             SCREAMING_SNAKE_CASE_     =	torch.cat(SCREAMING_SNAKE_CASE_      ,			dim=0     )
          if isinstance(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_     ):
             SCREAMING_SNAKE_CASE_     =	torch.cat(SCREAMING_SNAKE_CASE_      ,			dim=0     )

          if do_classifier_free_guidance:
             SCREAMING_SNAKE_CASE_     =	image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_      ,			dim=0     )
             SCREAMING_SNAKE_CASE_     =	negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_      ,			dim=0     )

          SCREAMING_SNAKE_CASE_     =	torch.cat([negative_image_embeds, image_embeds]      ,			dim=0     ).to(
              dtype=prompt_embeds.dtype      ,			device=SCREAMING_SNAKE_CASE_     )

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_      ,			device=SCREAMING_SNAKE_CASE_     )
          SCREAMING_SNAKE_CASE_     =	self.scheduler.timesteps

          SCREAMING_SNAKE_CASE_     =	self.unet.config.in_channels

          SCREAMING_SNAKE_CASE_     =	get_new_h_w(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			self.movq_scale_factor     )

          # create initial latent
          SCREAMING_SNAKE_CASE_     =	self.prepare_latents(
              (batch_size, num_channels_latents, height, width)      ,			text_encoder_hidden_states.dtype      ,			SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			self.scheduler      ,			)

          for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_     )     ):
             # expand the latents if we are doing classifier free guidance
             SCREAMING_SNAKE_CASE_     =	torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

             SCREAMING_SNAKE_CASE_     =	{'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
             SCREAMING_SNAKE_CASE_     =	self.unet(
                 sample=SCREAMING_SNAKE_CASE_      ,			timestep=SCREAMING_SNAKE_CASE_      ,			encoder_hidden_states=SCREAMING_SNAKE_CASE_      ,			added_cond_kwargs=SCREAMING_SNAKE_CASE_      ,			return_dict=SCREAMING_SNAKE_CASE_      ,			)[0]

             if do_classifier_free_guidance:
                SCREAMING_SNAKE_CASE_     =	noise_pred.split(latents.shape[1]      ,			dim=1     )
                SCREAMING_SNAKE_CASE_     =	noise_pred.chunk(2     )
                SCREAMING_SNAKE_CASE_     =	variance_pred.chunk(2     )
                SCREAMING_SNAKE_CASE_     =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                SCREAMING_SNAKE_CASE_     =	torch.cat([noise_pred, variance_pred_text]      ,			dim=1     )

             if not (
                 hasattr(self.scheduler.config      ,			'''variance_type'''     )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                SCREAMING_SNAKE_CASE_     =	noise_pred.split(latents.shape[1]      ,			dim=1     )

             # compute the previous noisy sample x_t -> x_t-1
             SCREAMING_SNAKE_CASE_     =	self.scheduler.step(
                 SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_      ,			generator=SCREAMING_SNAKE_CASE_      ,			).prev_sample
          # post-processing
          SCREAMING_SNAKE_CASE_     =	self.movq.decode(SCREAMING_SNAKE_CASE_      ,			force_not_quantize=SCREAMING_SNAKE_CASE_     )['sample']

          if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''     )

          if output_type in ["np", "pil"]:
             SCREAMING_SNAKE_CASE_     =	image * 0.5 + 0.5
             SCREAMING_SNAKE_CASE_     =	image.clamp(0      ,			1     )
             SCREAMING_SNAKE_CASE_     =	image.cpu().permute(0      ,			2      ,			3      ,			1     ).float().numpy()

          if output_type == "pil":
             SCREAMING_SNAKE_CASE_     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE_     )

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_     )







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any=0.999       ,     snake_case__       :    List[Any]="cosine"       ,     )	->     Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case__       :    Optional[Any]	):
         return math.exp(t * -12.0	)

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

   UpperCamelCase    :  List[Any]							=	[]
   for i in range(snake_case__	):
      UpperCamelCase    :  Optional[Any]							=	i / num_diffusion_timesteps
      UpperCamelCase    :  Union[str, Any]							=	(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	)       ,     snake_case__	)	)
   return torch.tensor(snake_case__       ,     dtype=torch.floataa	)



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[Any]        =  [e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase__				:					List[str]        =  2
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = 0.0_00_85,			SCREAMING_SNAKE_CASE_ = 0.0_12,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "linspace",			SCREAMING_SNAKE_CASE_ = 0,			)     ->		List[str]:
          if trained_betas is not None:
             UpperCamelCase    :  Union[str, Any]							=	torch.tensor(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "linear":
             UpperCamelCase    :  List[Any]							=	torch.linspace(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase    :  int							=	(
                 torch.linspace(beta_start**0.5,			beta_end**0.5,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       ) ** 2
             )
          elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase    :  Optional[int]							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )
          else:
             raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

          UpperCamelCase    :  Optional[int]							=	1.0 - self.betas
          UpperCamelCase    :  int							=	torch.cumprod(self.alphas,			dim=0       )

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		str:
          if schedule_timesteps is None:
             UpperCamelCase    :  Union[str, Any]							=	self.timesteps

          UpperCamelCase    :  Dict							=	(schedule_timesteps == timestep).nonzero()

          # The sigma index that is taken for the **very** first `step`
          # is always the second index (or the last index if there is only 1)
          # This way we can ensure we don't accidentally skip a sigma in
          # case we start in the middle of the denoising schedule (e.g. for image-to-image)
          if len(self._index_counter       ) == 0:
             UpperCamelCase    :  Optional[int]							=	1 if len(SCREAMING_SNAKE_CASE_       ) > 1 else 0
          else:
             UpperCamelCase    :  str							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
             UpperCamelCase    :  int							=	self._index_counter[timestep_int]

          return indices[pos].item()
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          # 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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          UpperCamelCase    :  Optional[int]							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          if self.state_in_first_order:
             UpperCamelCase    :  Dict							=	self.sigmas[step_index]
          else:
             UpperCamelCase    :  int							=	self.sigmas_interpol[step_index]

          UpperCamelCase    :  Any							=	sample / ((sigma**2 + 1) ** 0.5)
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	num_inference_steps

          UpperCamelCase    :  List[Any]							=	num_train_timesteps or self.config.num_train_timesteps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
             UpperCamelCase    :  int							=	np.linspace(0,			num_train_timesteps - 1,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             UpperCamelCase    :  List[Any]							=	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
             UpperCamelCase    :  Optional[Any]							=	(np.arange(0,			SCREAMING_SNAKE_CASE_       ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_       )
             timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
             UpperCamelCase    :  Optional[int]							=	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
             UpperCamelCase    :  Any							=	(np.arange(SCREAMING_SNAKE_CASE_,			0,			-step_ratio       )).round().copy().astype(SCREAMING_SNAKE_CASE_       )
             timesteps -= 1
          else:
             raise ValueError(
                 F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""       )

          UpperCamelCase    :  Tuple							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	np.interp(SCREAMING_SNAKE_CASE_,			np.arange(0,			len(SCREAMING_SNAKE_CASE_       )       ),			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )

          # interpolate sigmas
          UpperCamelCase    :  Union[str, Any]							=	sigmas.log().lerp(sigmas.roll(1       ).log(),			0.5       ).exp()

          UpperCamelCase    :  Any							=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          UpperCamelCase    :  Optional[Any]							=	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             # mps does not support float64
             UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )

          # interpolate timesteps
          UpperCamelCase    :  int							=	self.sigma_to_t(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=timesteps.dtype       )
          UpperCamelCase    :  List[str]							=	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),			dim=-1       ).flatten()

          UpperCamelCase    :  Optional[Any]							=	torch.cat([timesteps[:1], interleaved_timesteps]       )

          UpperCamelCase    :  Optional[Any]							=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase    :  Dict							=	defaultdict(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # get log sigma
          UpperCamelCase    :  List[Any]							=	sigma.log()

          # get distribution
          UpperCamelCase    :  Optional[int]							=	log_sigma - self.log_sigmas[:, None]

          # get sigmas range
          UpperCamelCase    :  Optional[int]							=	dists.ge(0       ).cumsum(dim=0       ).argmax(dim=0       ).clamp(max=self.log_sigmas.shape[0] - 2       )
          UpperCamelCase    :  Tuple							=	low_idx + 1

          UpperCamelCase    :  List[str]							=	self.log_sigmas[low_idx]
          UpperCamelCase    :  Optional[Any]							=	self.log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase    :  int							=	(low - log_sigma) / (low - high)
          UpperCamelCase    :  Tuple							=	w.clamp(0,			1       )

          # transform interpolation to time range
          UpperCamelCase    :  List[str]							=	(1 - w) * low_idx + w * high_idx
          UpperCamelCase    :  Dict							=	t.view(sigma.shape       )
          return t
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self.sample is None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[SchedulerOutput, Tuple]:
          UpperCamelCase    :  str							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          # advance index counter by 1
          UpperCamelCase    :  Optional[int]							=	timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_       ) else timestep
          self._index_counter[timestep_int] += 1

          if self.state_in_first_order:
             UpperCamelCase    :  Tuple							=	self.sigmas[step_index]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index + 1]
             UpperCamelCase    :  Optional[int]							=	self.sigmas[step_index + 1]
          else:
             # 2nd order / KDPM2's method
             UpperCamelCase    :  str							=	self.sigmas[step_index - 1]
             UpperCamelCase    :  Dict							=	self.sigmas_interpol[step_index]
             UpperCamelCase    :  Any							=	self.sigmas[step_index]

          # currently only gamma=0 is supported. This usually works best anyways.
          # We can support gamma in the future but then need to scale the timestep before
          # passing it to the model which requires a change in API
          UpperCamelCase    :  Dict							=	0
          UpperCamelCase    :  Any							=	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
             UpperCamelCase    :  Any							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  List[Any]							=	sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
             UpperCamelCase    :  Union[str, Any]							=	sigma_hat if self.state_in_first_order else sigma_interpol
             UpperCamelCase    :  Optional[int]							=	model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
          elif self.config.prediction_type == "sample":
             raise NotImplementedError('prediction_type not implemented yet: sample'       )
          else:
             raise ValueError(
                 F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

          if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             UpperCamelCase    :  int							=	(sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             UpperCamelCase    :  Any							=	sigma_interpol - sigma_hat

             # store for 2nd order step
             UpperCamelCase    :  Tuple							=	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase    :  Union[str, Any]							=	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase    :  Dict							=	sigma_next - sigma_hat

             UpperCamelCase    :  Any							=	self.sample
             UpperCamelCase    :  str							=	None

          UpperCamelCase    :  Any							=	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase    :  Optional[Any]							=	self.sigmas.to(device=original_samples.device,			dtype=original_samples.dtype       )
          if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_       ):
             # mps does not support float64
             UpperCamelCase    :  List[str]							=	self.timesteps.to(original_samples.device,			dtype=torch.floataa       )
             UpperCamelCase    :  str							=	timesteps.to(original_samples.device,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	self.timesteps.to(original_samples.device       )
             UpperCamelCase    :  int							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  str							=	[self.index_for_timestep(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) for t in timesteps]

          UpperCamelCase    :  List[Any]							=	sigmas[step_indices].flatten()
          while len(sigma.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  int							=	sigma.unsqueeze(-1       )

          UpperCamelCase    :  Any							=	original_samples + noise * sigma
          return noisy_samples






       def __len__(			self       )     ->		Optional[int]:
          return self.config.num_train_timesteps



