



"""simple docstring"""
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

UpperCAmelCase						:		Dict			= logging.get_logger(__name__)


@add_end_docstrings(__UpperCAmelCase   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):

    def __init__(				self			:				Dict ,  **lowerCAmelCase_			:				Any):







      """simple docstring"""







      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 					_UpperCAmelCase	(				self			:				str ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







      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			:				Union[str, Any] ,  lowerCAmelCase_			:				List[str] ,  *lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Optional[int]=None ,  lowerCAmelCase_			:				List[str]=None ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







      return super().__call__(lowerCAmelCase_ ,  *lowerCAmelCase_ ,  num_workers=lowerCAmelCase_ ,  batch_size=lowerCAmelCase_ ,  **lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[Any]=6_4 ,  lowerCAmelCase_			:				int = 0 ,  lowerCAmelCase_			:				float = 5_1_2 / 1_5_0_0 ,  lowerCAmelCase_			:				Optional[int] = 3_2 ,  lowerCAmelCase_			:				Optional[int] = 1 ,  ):







      """simple docstring"""







      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 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				List[Any] ,  lowerCAmelCase_			:				Any=0.88 ,  lowerCAmelCase_			:				Tuple=0.95 ,  lowerCAmelCase_			:				str=0 ,  lowerCAmelCase_			:				Dict=1 ,  ):







      """simple docstring"""







      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 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Any=False ,  lowerCAmelCase_			:				List[Any]=False ,  lowerCAmelCase_			:				Tuple=0.7 ,  ):







      """simple docstring"""







      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}






"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      raise NotImplementedError()

