



'''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[str]			=     logging.get_logger(__name__)


@add_end_docstrings(_lowercase	)
class 	a__     (				_lowercase	):


	def __init__(self : str,					**__UpperCAmelCase : Any      )     ->			Dict:

					"""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 				lowercase__  (self : Tuple,					**__UpperCAmelCase : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

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


	def __call__(self : Optional[Any],					__UpperCAmelCase : List[str],					*__UpperCAmelCase : List[Any],					__UpperCAmelCase : Union[str, Any]=None,					__UpperCAmelCase : Tuple=None,					**__UpperCAmelCase : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Optional[int]=64,					__UpperCAmelCase : List[str] = 0,					__UpperCAmelCase : int = 512 / 1500,					__UpperCAmelCase : List[str] = 32,					__UpperCAmelCase : Tuple = 1,					)     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= load_image(_lowerCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.image_processor.size['''longest_edge''']
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= self.image_processor.generate_crop_boxes(
					    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.image_processor(images=_lowerCAmelCase,					return_tensors='''pt'''      )

					with self.device_placement():
									if self.framework == "pt":
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_inference_context()
													with inference_context():
																	SCREAMING_SNAKE_CASE  :						Any      					= self._ensure_tensor_on_device(_lowerCAmelCase,					device=self.device      )
																	SCREAMING_SNAKE_CASE  :						List[Any]      					= self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''      )      )
																	SCREAMING_SNAKE_CASE  :						List[Any]      					= image_embeddings

					SCREAMING_SNAKE_CASE  :						str      					= grid_points.shape[1]
					SCREAMING_SNAKE_CASE  :						List[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,					_lowerCAmelCase,					_lowerCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= grid_points[:, i : i + points_per_batch, :, :]
									SCREAMING_SNAKE_CASE  :						Tuple      					= input_labels[:, i : i + points_per_batch]
									SCREAMING_SNAKE_CASE  :						Tuple      					= 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 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int]=0.88,					__UpperCAmelCase : Any=0.95,					__UpperCAmelCase : int=0,					__UpperCAmelCase : Optional[Any]=1,					)     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= model_inputs.pop('''input_boxes'''      )
					SCREAMING_SNAKE_CASE  :						int      					= model_inputs.pop('''is_last'''      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= model_inputs.pop('''original_sizes'''      ).tolist()
					SCREAMING_SNAKE_CASE  :						Dict      					= model_inputs.pop('''reshaped_input_sizes'''      ).tolist()

					SCREAMING_SNAKE_CASE  :						str      					= self.model(**_lowerCAmelCase      )

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_outputs['''pred_masks''']
					SCREAMING_SNAKE_CASE  :						int      					= self.image_processor.post_process_masks(
					    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					binarize=_lowerCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= model_outputs['''iou_scores''']
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Any=0.7,					)     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						int      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					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'''      )      )

					SCREAMING_SNAKE_CASE  :						int      					= torch.cat(_lowerCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.cat(_lowerCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.image_processor.post_process_for_mask_generation(
					    _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= defaultdict(_lowerCAmelCase      )
					for output in model_outputs:
									for k, v in output.items():
													extra[k].append(_lowerCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= {}
					if output_rle_mask:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= rle_mask

					if output_bboxes_mask:
									SCREAMING_SNAKE_CASE  :						List[str]      					= bounding_boxes

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









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0
				if start < end:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[end]
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[pivot]
								SCREAMING_SNAKE_CASE  :						Dict      					= temp

								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= _in_place_partition(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	p - 1       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	p + 1    ,	_SCREAMING_SNAKE_CASE       )
				return count






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						Tuple      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[end]
				SCREAMING_SNAKE_CASE  :						int      					= a[pivot]
				SCREAMING_SNAKE_CASE  :						Tuple      					= temp
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= start - 1
				for index in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
								count += 1
								if a[index] < a[end]:  # check if current val is less than pivot value
												SCREAMING_SNAKE_CASE  :						Tuple      					= new_pivot_index + 1
												SCREAMING_SNAKE_CASE  :						Dict      					= a[new_pivot_index]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[index]
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= temp

				SCREAMING_SNAKE_CASE  :						List[Any]      					= a[new_pivot_index + 1]
				SCREAMING_SNAKE_CASE  :						Any      					= a[end]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= temp
				return new_pivot_index + 1, count


snake_case_			=     TemporaryFile()
snake_case_			=     100  # 1000 elements are to be sorted


snake_case_			,				snake_case_			=     0, 1  # mean and standard deviation
snake_case_			=     np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
snake_case_			=     np.load(outfile)
snake_case_			=     len(M) - 1
snake_case_			=     _in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)




print(z)




