
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

lowercase__							:							List[str]									=			logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE_    )
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)
													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(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			{}
													__UpperCamelCase       =			{}
													__UpperCamelCase       =			{}
													# preprocess args
													if "points_per_batch" in kwargs:
																			__UpperCamelCase       =			kwargs['''points_per_batch''']
													if "points_per_crop" in kwargs:
																			__UpperCamelCase       =			kwargs['''points_per_crop''']
													if "crops_n_layers" in kwargs:
																			__UpperCamelCase       =			kwargs['''crops_n_layers''']
													if "crop_overlap_ratio" in kwargs:
																			__UpperCamelCase       =			kwargs['''crop_overlap_ratio''']
													if "crop_n_points_downscale_factor" in kwargs:
																			__UpperCamelCase       =			kwargs['''crop_n_points_downscale_factor''']
													# postprocess args
													if "pred_iou_thresh" in kwargs:
																			__UpperCamelCase       =			kwargs['''pred_iou_thresh''']
													if "stability_score_offset" in kwargs:
																			__UpperCamelCase       =			kwargs['''stability_score_offset''']
													if "mask_threshold" in kwargs:
																			__UpperCamelCase       =			kwargs['''mask_threshold''']
													if "stability_score_thresh" in kwargs:
																			__UpperCamelCase       =			kwargs['''stability_score_thresh''']
													if "crops_nms_thresh" in kwargs:
																			__UpperCamelCase       =			kwargs['''crops_nms_thresh''']
													if "output_rle_mask" in kwargs:
																			__UpperCamelCase       =			kwargs['''output_rle_mask''']
													if "output_bboxes_mask" in kwargs:
																			__UpperCamelCase       =			kwargs['''output_bboxes_mask''']
													return preprocess_kwargs, forward_params, postprocess_kwargs



							def __call__(  self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=64     ,	SCREAMING_SNAKE_CASE_ = 0     ,	SCREAMING_SNAKE_CASE_ = 512 / 1500     ,	SCREAMING_SNAKE_CASE_ = 32     ,	SCREAMING_SNAKE_CASE_ = 1     ,	)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			load_image(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.image_processor.size['''longest_edge''']
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.image_processor.generate_crop_boxes(
													    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			)

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

													__UpperCamelCase       =			grid_points.shape[1]
													__UpperCamelCase       =			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     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			grid_points[:, i : i + points_per_batch, :, :]
																			__UpperCamelCase       =			input_labels[:, i : i + points_per_batch]
																			__UpperCamelCase       =			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     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=0.8_8     ,	SCREAMING_SNAKE_CASE_=0.9_5     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=1     ,	)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			model_inputs.pop('''input_boxes'''			)
													__UpperCamelCase       =			model_inputs.pop('''is_last'''			)
													__UpperCamelCase       =			model_inputs.pop('''original_sizes'''			).tolist()
													__UpperCamelCase       =			model_inputs.pop('''reshaped_input_sizes'''			).tolist()

													__UpperCamelCase       =			self.model(**SCREAMING_SNAKE_CASE_			)

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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=0.7     ,	)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			[]
													__UpperCamelCase       =			[]
													__UpperCamelCase       =			[]
													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'''			)			)

													__UpperCamelCase       =			torch.cat(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			torch.cat(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.image_processor.post_process_for_mask_generation(
													    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

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

													__UpperCamelCase       =			{}
													if output_rle_mask:
																			__UpperCamelCase       =			rle_mask

													if output_bboxes_mask:
																			__UpperCamelCase       =			bounding_boxes

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






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						def is_in_circle(snake_case							:   float ,      snake_case							:   float ) -> bool:
												__UpperCamelCase       =			sqrt((x**2) + (y**2) )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						__UpperCamelCase       =			mean(
						    int(is_in_circle(uniform(-1.0 ,      1.0 ) ,      uniform(-1.0 ,      1.0 ) ) )
						    for _ in range(snake_case ) )
						# The ratio of the area for circle to square is pi/4.
						__UpperCamelCase       =			proportion * 4
						print(f"The estimated value of pi is {pi_estimate}" )
						print(f"The numpy value of pi is {pi}" )
						print(f"The total error is {abs(pi - pi_estimate )}" )




def 	A_ (							snake_case							:   int ,      snake_case							:   Callable[[float], float] ,      snake_case							:   float = 0.0 ,      snake_case							:   float = 1.0 ,      )      ->    float:



						'''simple docstring'''

						return mean(
						    function_to_integrate(uniform(snake_case ,      snake_case ) ) for _ in range(snake_case ) ) * (max_value - min_value)




def 	A_ (							snake_case							:   int ,      snake_case							:   float = 0.0 ,      snake_case							:   float = 1.0 )      ->    None:



						'''simple docstring'''

						def identity_function(snake_case							:   float ) -> float:
												return x

						__UpperCamelCase       =			area_under_curve_estimator(
						    snake_case ,      snake_case ,      snake_case ,      snake_case )
						__UpperCamelCase       =			(max_value * max_value - min_value * min_value) / 2

						print('''******************''' )
						print(f"Estimating area under y=x where x varies from {min_value} to {max_value}" )
						print(f"Estimated value is {estimated_value}" )
						print(f"Expected value is {expected_value}" )
						print(f"Total error is {abs(estimated_value - expected_value )}" )
						print('''******************''' )




def 	A_ (							snake_case							:   int )      ->    None:



						'''simple docstring'''

						def function_to_integrate(snake_case							:   float ) -> float:
												return sqrt(4.0 - x * x )

						__UpperCamelCase       =			area_under_curve_estimator(
						    snake_case ,      snake_case ,      0.0 ,      2.0 )

						print('''******************''' )
						print('''Estimating pi using area_under_curve_estimator''' )
						print(f"Estimated value is {estimated_value}" )
						print(f"Expected value is {pi}" )
						print(f"Total error is {abs(estimated_value - pi )}" )
						print('''******************''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




