



'''simple docstring'''





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 						_a       (    lowerCamelCase_	,		lowerCamelCase_=0.9_99	,		lowerCamelCase_="cosine"	,		):


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowerCamelCase_				):
         return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

   elif alpha_transform_type == "exp":

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

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

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





class 				lowerCAmelCase_						(			__a    ,     __a		):
    __UpperCAmelCase				=			[e.name for e in KarrasDiffusionSchedulers]
    __UpperCAmelCase				=			2





    @register_to_config
    def __init__(							self							:							Union[str, Any],							_snake_case							:							int = 1_000,							_snake_case							:							float = 0.0_0085,							_snake_case							:							float = 0.012,							_snake_case							:							str = "linear",							_snake_case							:							Optional[Union[np.ndarray, List[float]]] = None,							_snake_case							:							str = "epsilon",							_snake_case							:							Optional[bool] = False,							_snake_case							:							Optional[bool] = False,							_snake_case							:							float = 1.0,							_snake_case							:							str = "linspace",							_snake_case							:							int = 0,							):



       '''simple docstring'''







       if trained_betas is not None:
          snake_case       :				Optional[Any]		    =torch.tensor(lowerCAmelCase__,							dtype=torch.floataa      )
       elif beta_schedule == "linear":
          snake_case       :				Optional[int]		    =torch.linspace(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          snake_case       :				Optional[Any]		    =(
              torch.linspace(beta_start**0.5,							beta_end**0.5,							lowerCAmelCase__,							dtype=torch.floataa      ) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          snake_case       :				str		    =betas_for_alpha_bar(lowerCAmelCase__,							alpha_transform_type='''cosine'''      )
       elif beta_schedule == "exp":
          snake_case       :				List[Any]		    =betas_for_alpha_bar(lowerCAmelCase__,							alpha_transform_type='''exp'''      )
       else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''      )

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

       #  set all values
       self.set_timesteps(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__      )
       snake_case       :				Optional[int]		    =use_karras_sigmas





    def 	__snake_case  (							self							:							List[Any],							_snake_case							:							List[Any],							_snake_case							:							Optional[int]=None      ):



       '''simple docstring'''







       if schedule_timesteps is None:
          snake_case       :				List[str]		    =self.timesteps

       snake_case       :				List[Any]		    =(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:
          snake_case       :				Any		    =1 if len(lowerCAmelCase__      ) > 1 else 0
       else:
          snake_case       :				Union[str, Any]		    =timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__      ) else timestep
          snake_case       :				int		    =self._index_counter[timestep_int]

       return indices[pos].item()





    @property
    def 	__snake_case  (							self							:							Tuple      ):



       '''simple docstring'''







       if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

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





    def 	__snake_case  (							self							:							str,							_snake_case							:							torch.FloatTensor,							_snake_case							:							Union[float, torch.FloatTensor],							):



       '''simple docstring'''







       snake_case       :				Any		    =self.index_for_timestep(lowerCAmelCase__      )

       snake_case       :				List[Any]		    =self.sigmas[step_index]
       snake_case       :				List[str]		    =sample / ((sigma**2 + 1) ** 0.5)
       return sample





    def 	__snake_case  (							self							:							Union[str, Any],							_snake_case							:							int,							_snake_case							:							Union[str, torch.device] = None,							_snake_case							:							Optional[int] = None,							):



       '''simple docstring'''







       snake_case       :				int		    =num_inference_steps

       snake_case       :				int		    =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":
          snake_case       :				Union[str, Any]		    =np.linspace(0,							num_train_timesteps - 1,							lowerCAmelCase__,							dtype=lowerCAmelCase__      )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
          snake_case       :				Tuple		    =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       :				str		    =(np.arange(0,							lowerCAmelCase__      ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__      )
          timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
          snake_case       :				Dict		    =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       :				List[str]		    =(np.arange(lowerCAmelCase__,							0,							-step_ratio      )).round().copy().astype(lowerCAmelCase__      )
          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       :				int		    =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
       snake_case       :				int		    =np.log(lowerCAmelCase__      )
       snake_case       :				Optional[int]		    =np.interp(lowerCAmelCase__,							np.arange(0,							len(lowerCAmelCase__      )      ),							lowerCAmelCase__      )

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

       snake_case       :				List[Any]		    =np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
       snake_case       :				List[str]		    =torch.from_numpy(lowerCAmelCase__      ).to(device=lowerCAmelCase__      )
       snake_case       :				Union[str, Any]		    =torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

       snake_case       :				str		    =torch.from_numpy(lowerCAmelCase__      )
       snake_case       :				List[Any]		    =torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

       if str(lowerCAmelCase__      ).startswith('''mps'''      ):
          # mps does not support float64
          snake_case       :				Optional[int]		    =timesteps.to(lowerCAmelCase__,							dtype=torch.floataa      )
       else:
          snake_case       :				Union[str, Any]		    =timesteps.to(device=lowerCAmelCase__      )

       # empty dt and derivative
       snake_case       :				Any		    =None
       snake_case       :				Any		    =None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       snake_case       :				List[str]		    =defaultdict(lowerCAmelCase__      )





    def 	__snake_case  (							self							:							List[str],							_snake_case							:							Union[str, Any],							_snake_case							:							Dict      ):



       '''simple docstring'''







       snake_case       :				Tuple		    =np.log(lowerCAmelCase__      )

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

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

       snake_case       :				Tuple		    =log_sigmas[low_idx]
       snake_case       :				Any		    =log_sigmas[high_idx]

       # interpolate sigmas
       snake_case       :				Any		    =(low - log_sigma) / (low - high)
       snake_case       :				Tuple		    =np.clip(lowerCAmelCase__,							0,							1      )

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





    def 	__snake_case  (							self							:							Optional[int],							_snake_case							:							torch.FloatTensor,							_snake_case							:							Tuple      ):



       '''simple docstring'''







       snake_case       :				Dict		    =in_sigmas[-1].item()
       snake_case       :				Any		    =in_sigmas[0].item()

       snake_case       :				int		    =7.0  # 7.0 is the value used in the paper
       snake_case       :				Tuple		    =np.linspace(0,							1,							lowerCAmelCase__      )
       snake_case       :				Optional[int]		    =sigma_min ** (1 / rho)
       snake_case       :				Dict		    =sigma_max ** (1 / rho)
       snake_case       :				List[Any]		    =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas





    @property
    def 	__snake_case  (							self							:							Dict      ):



       '''simple docstring'''







       return self.dt is None





    def 	__snake_case  (							self							:							List[Any],							_snake_case							:							Union[torch.FloatTensor, np.ndarray],							_snake_case							:							Union[float, torch.FloatTensor],							_snake_case							:							Union[torch.FloatTensor, np.ndarray],							_snake_case							:							bool = True,							):



       '''simple docstring'''







       snake_case       :				str		    =self.index_for_timestep(lowerCAmelCase__      )

       # advance index counter by 1
       snake_case       :				List[Any]		    =timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__      ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
          snake_case       :				List[Any]		    =self.sigmas[step_index]
          snake_case       :				Tuple		    =self.sigmas[step_index + 1]
       else:
          # 2nd order / Heun's method
          snake_case       :				Any		    =self.sigmas[step_index - 1]
          snake_case       :				List[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
       snake_case       :				Union[str, Any]		    =0
       snake_case       :				int		    =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":
          snake_case       :				Optional[Any]		    =sigma_hat if self.state_in_first_order else sigma_next
          snake_case       :				Optional[int]		    =sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
          snake_case       :				Any		    =sigma_hat if self.state_in_first_order else sigma_next
          snake_case       :				List[Any]		    =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
              sample / (sigma_input**2 + 1)
          )
       elif self.config.prediction_type == "sample":
          snake_case       :				Tuple		    =model_output
       else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''      )

       if self.config.clip_sample:
          snake_case       :				Union[str, Any]		    =pred_original_sample.clamp(
              -self.config.clip_sample_range,							self.config.clip_sample_range      )

       if self.state_in_first_order:
          # 2. Convert to an ODE derivative for 1st order
          snake_case       :				Optional[Any]		    =(sample - pred_original_sample) / sigma_hat
          # 3. delta timestep
          snake_case       :				Tuple		    =sigma_next - sigma_hat

          # store for 2nd order step
          snake_case       :				int		    =derivative
          snake_case       :				List[str]		    =dt
          snake_case       :				Dict		    =sample
       else:
          # 2. 2nd order / Heun's method
          snake_case       :				Optional[Any]		    =(sample - pred_original_sample) / sigma_next
          snake_case       :				Optional[int]		    =(self.prev_derivative + derivative) / 2

          # 3. take prev timestep & sample
          snake_case       :				Tuple		    =self.dt
          snake_case       :				Any		    =self.sample

          # free dt and derivative
          # Note, this puts the scheduler in "first order mode"
          snake_case       :				Union[str, Any]		    =None
          snake_case       :				List[Any]		    =None
          snake_case       :				int		    =None

       snake_case       :				int		    =sample + derivative * dt

       if not return_dict:
          return (prev_sample,)

       return SchedulerOutput(prev_sample=lowerCAmelCase__      )





    def 	__snake_case  (							self							:							List[str],							_snake_case							:							torch.FloatTensor,							_snake_case							:							torch.FloatTensor,							_snake_case							:							torch.FloatTensor,							):



       '''simple docstring'''







       snake_case       :				int		    =self.sigmas.to(device=original_samples.device,							dtype=original_samples.dtype      )
       if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__      ):
          # mps does not support float64
          snake_case       :				List[Any]		    =self.timesteps.to(original_samples.device,							dtype=torch.floataa      )
          snake_case       :				Optional[Any]		    =timesteps.to(original_samples.device,							dtype=torch.floataa      )
       else:
          snake_case       :				Union[str, Any]		    =self.timesteps.to(original_samples.device      )
          snake_case       :				List[Any]		    =timesteps.to(original_samples.device      )

       snake_case       :				str		    =[self.index_for_timestep(lowerCAmelCase__,							lowerCAmelCase__      ) for t in timesteps]

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

       snake_case       :				Tuple		    =original_samples + noise * sigma
       return noisy_samples






    def __len__(							self							:							Dict      ):



       '''simple docstring'''







       return self.config.num_train_timesteps







from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_snake_case      :							List[Any]     	=    logging.get_logger(__name__)





@add_end_docstrings(__a		)
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    **lowerCAmelCase__							:Dict     )  ->int:
										super().__init__(**lowerCAmelCase__     )
										requires_backends(self		,    "vision"     )
										requires_backends(self		,    "torch"     )

										if self.framework != "pt":
														raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''     )

										self.check_model_type(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    **lowerCAmelCase__							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      {}
										lowercase         =      {}
										lowercase         =      {}
										# preprocess args
										if "points_per_batch" in kwargs:
														lowercase         =      kwargs["points_per_batch"]
										if "points_per_crop" in kwargs:
														lowercase         =      kwargs["points_per_crop"]
										if "crops_n_layers" in kwargs:
														lowercase         =      kwargs["crops_n_layers"]
										if "crop_overlap_ratio" in kwargs:
														lowercase         =      kwargs["crop_overlap_ratio"]
										if "crop_n_points_downscale_factor" in kwargs:
														lowercase         =      kwargs["crop_n_points_downscale_factor"]
										# postprocess args
										if "pred_iou_thresh" in kwargs:
														lowercase         =      kwargs["pred_iou_thresh"]
										if "stability_score_offset" in kwargs:
														lowercase         =      kwargs["stability_score_offset"]
										if "mask_threshold" in kwargs:
														lowercase         =      kwargs["mask_threshold"]
										if "stability_score_thresh" in kwargs:
														lowercase         =      kwargs["stability_score_thresh"]
										if "crops_nms_thresh" in kwargs:
														lowercase         =      kwargs["crops_nms_thresh"]
										if "output_rle_mask" in kwargs:
														lowercase         =      kwargs["output_rle_mask"]
										if "output_bboxes_mask" in kwargs:
														lowercase         =      kwargs["output_bboxes_mask"]
										return preprocess_kwargs, forward_params, postprocess_kwargs





						def __call__(			self							:List[str]		,    lowerCAmelCase__							:int		,    *lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Any=None		,    **lowerCAmelCase__							:List[str]     )  ->Optional[int]:
										return super().__call__(lowerCAmelCase__		,    *lowerCAmelCase__		,    num_workers=lowerCAmelCase__		,    batch_size=lowerCAmelCase__		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int = 0		,    lowerCAmelCase__							:float = 512 / 1500		,    lowerCAmelCase__							:Optional[int] = 32		,    lowerCAmelCase__							:Optional[int] = 1		,    )  ->Any:
										lowercase         =      load_image(lowerCAmelCase__     )
										lowercase         =      self.image_processor.size["longest_edge"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.generate_crop_boxes(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      self.image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     )

										with self.device_placement():
														if self.framework == "pt":
																		lowercase         =      self.get_inference_context()
																		with inference_context():
																						lowercase         =      self._ensure_tensor_on_device(lowerCAmelCase__		,    device=self.device     )
																						lowercase         =      self.model.get_image_embeddings(model_inputs.pop("pixel_values"     )     )
																						lowercase         =      image_embeddings

										lowercase         =      grid_points.shape[1]
										lowercase         =      points_per_batch if points_per_batch is not None else n_points

										if points_per_batch <= 0:
														raise ValueError(
														    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
														    "To return all points at once, set points_per_batch to None"     )

										for i in range(0		,    lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      grid_points[:, i : i + points_per_batch, :, :]
														lowercase         =      input_labels[:, i : i + points_per_batch]
														lowercase         =      i == n_points - points_per_batch
														yield {
														    "input_points": batched_points,
														    "input_labels": labels,
														    "input_boxes": crop_boxes,
														    "is_last": is_last,
														    **model_inputs,
														}





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict=0.88		,    lowerCAmelCase__							:Dict=0.95		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:int=1		,    )  ->str:
										lowercase         =      model_inputs.pop("input_boxes"     )
										lowercase         =      model_inputs.pop("is_last"     )
										lowercase         =      model_inputs.pop("original_sizes"     ).tolist()
										lowercase         =      model_inputs.pop("reshaped_input_sizes"     ).tolist()

										lowercase         =      self.model(**lowerCAmelCase__     )

										# post processing happens here in order to avoid CPU GPU copies of ALL the masks
										lowercase         =      model_outputs["pred_masks"]
										lowercase         =      self.image_processor.post_process_masks(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    binarize=lowerCAmelCase__     )
										lowercase         =      model_outputs["iou_scores"]
										lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.filter_masks(
										    masks[0]		,    iou_scores[0]		,    original_sizes[0]		,    input_boxes[0]		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    )
										return {
										    "masks": masks,
										    "is_last": is_last,
										    "boxes": boxes,
										    "iou_scores": iou_scores,
										}





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:int=0.7		,    )  ->List[Any]:
										lowercase         =      []
										lowercase         =      []
										lowercase         =      []
										for model_output in model_outputs:
														all_scores.append(model_output.pop("iou_scores"     )     )
														all_masks.extend(model_output.pop("masks"     )     )
														all_boxes.append(model_output.pop("boxes"     )     )

										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.post_process_for_mask_generation(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      defaultdict(lowerCAmelCase__     )
										for output in model_outputs:
														for k, v in output.items():
																		extra[k].append(lowerCAmelCase__     )

										lowercase         =      {}
										if output_rle_mask:
														lowercase         =      rle_mask

										if output_bboxes_mask:
														lowercase         =      bounding_boxes

										return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
