



"""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

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



@add_end_docstrings(_lowercase							)
class     A__     (						_lowercase							):




 '''simple docstring'''





 def __init__(  self:	Dict ,    **_SCREAMING_SNAKE_CASE:	Tuple)       -> int:

    """simple docstring"""

    super().__init__(**A_)
    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(A_)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    **_SCREAMING_SNAKE_CASE:	List[Any])       -> Union[str, Any]:

    """simple docstring"""

    __lowerCAmelCase   :						int				= {}
    __lowerCAmelCase   :						Optional[int]				= {}
    __lowerCAmelCase   :						int				= {}
    # preprocess args
    if "points_per_batch" in kwargs:
       __lowerCAmelCase   :						Dict				= kwargs["points_per_batch"]
    if "points_per_crop" in kwargs:
       __lowerCAmelCase   :						str				= kwargs["points_per_crop"]
    if "crops_n_layers" in kwargs:
       __lowerCAmelCase   :						Optional[Any]				= kwargs["crops_n_layers"]
    if "crop_overlap_ratio" in kwargs:
       __lowerCAmelCase   :						List[str]				= kwargs["crop_overlap_ratio"]
    if "crop_n_points_downscale_factor" in kwargs:
       __lowerCAmelCase   :						Dict				= kwargs["crop_n_points_downscale_factor"]
    # postprocess args
    if "pred_iou_thresh" in kwargs:
       __lowerCAmelCase   :						Union[str, Any]				= kwargs["pred_iou_thresh"]
    if "stability_score_offset" in kwargs:
       __lowerCAmelCase   :						Tuple				= kwargs["stability_score_offset"]
    if "mask_threshold" in kwargs:
       __lowerCAmelCase   :						Dict				= kwargs["mask_threshold"]
    if "stability_score_thresh" in kwargs:
       __lowerCAmelCase   :						int				= kwargs["stability_score_thresh"]
    if "crops_nms_thresh" in kwargs:
       __lowerCAmelCase   :						List[Any]				= kwargs["crops_nms_thresh"]
    if "output_rle_mask" in kwargs:
       __lowerCAmelCase   :						Optional[int]				= kwargs["output_rle_mask"]
    if "output_bboxes_mask" in kwargs:
       __lowerCAmelCase   :						Optional[Any]				= kwargs["output_bboxes_mask"]
    return preprocess_kwargs, forward_params, postprocess_kwargs

 def __call__(  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple ,    *_SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple=None ,    _SCREAMING_SNAKE_CASE:	Any=None ,    **_SCREAMING_SNAKE_CASE:	Union[str, Any])       -> Any:

    """simple docstring"""

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

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[int]=64 ,    _SCREAMING_SNAKE_CASE:	int = 0 ,    _SCREAMING_SNAKE_CASE:	float = 512 / 1500 ,    _SCREAMING_SNAKE_CASE:	Optional[int] = 32 ,    _SCREAMING_SNAKE_CASE:	Optional[int] = 1 ,    )       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						List[str]				= load_image(A_)
    __lowerCAmelCase   :						Optional[int]				= self.image_processor.size["longest_edge"]
    __lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.image_processor.generate_crop_boxes(
        A_ ,    A_ ,    A_ ,    A_ ,    A_ ,    A_)
    __lowerCAmelCase   :						Dict				= self.image_processor(images=A_ ,    return_tensors="pt")

    with self.device_placement():
       if self.framework == "pt":
          __lowerCAmelCase   :						int				= self.get_inference_context()
          with inference_context():
             __lowerCAmelCase   :						Tuple				= self._ensure_tensor_on_device(A_ ,    device=self.device)
             __lowerCAmelCase   :						Any				= self.model.get_image_embeddings(model_inputs.pop("pixel_values"))
             __lowerCAmelCase   :						int				= image_embeddings

    __lowerCAmelCase   :						Tuple				= grid_points.shape[1]
    __lowerCAmelCase   :						str				= 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 ,    A_ ,    A_):
       __lowerCAmelCase   :						Tuple				= grid_points[:, i : i + points_per_batch, :, :]
       __lowerCAmelCase   :						Any				= input_labels[:, i : i + points_per_batch]
       __lowerCAmelCase   :						Union[str, Any]				= 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:	Dict ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=0.88 ,    _SCREAMING_SNAKE_CASE:	List[Any]=0.95 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=0 ,    _SCREAMING_SNAKE_CASE:	str=1 ,    )       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						str				= model_inputs.pop("input_boxes")
    __lowerCAmelCase   :						Union[str, Any]				= model_inputs.pop("is_last")
    __lowerCAmelCase   :						Optional[Any]				= model_inputs.pop("original_sizes").tolist()
    __lowerCAmelCase   :						Dict				= model_inputs.pop("reshaped_input_sizes").tolist()

    __lowerCAmelCase   :						int				= self.model(**A_)

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







 def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	List[Any]=False ,    _SCREAMING_SNAKE_CASE:	Any=False ,    _SCREAMING_SNAKE_CASE:	str=0.7 ,    )       -> List[str]:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= []
    __lowerCAmelCase   :						Optional[int]				= []
    __lowerCAmelCase   :						Any				= []
    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"))

    __lowerCAmelCase   :						List[Any]				= torch.cat(A_)
    __lowerCAmelCase   :						Union[str, Any]				= torch.cat(A_)
    __lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Any				= self.image_processor.post_process_for_mask_generation(
        A_ ,    A_ ,    A_ ,    A_)

    __lowerCAmelCase   :						str				= defaultdict(A_)
    for output in model_outputs:
       for k, v in output.items():
          extra[k].append(A_)

    __lowerCAmelCase   :						List[str]				= {}
    if output_rle_mask:
       __lowerCAmelCase   :						str				= rle_mask

    if output_bboxes_mask:
       __lowerCAmelCase   :						Optional[Any]				= bounding_boxes

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




"""simple docstring"""






def     _lowercase       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))