



'''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__ :			Union[str, Any]												=				logging.get_logger(__name__)






@add_end_docstrings(lowerCamelCase__					)
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				str							,  **lowerCAmelCase__  :				List[str]		):





					"""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  :				Optional[Any]							,  **lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






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

			def __call__(							self  :				Tuple							,  lowerCAmelCase__  :				int							,  *lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				Optional[int]=None							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Any							,  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"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	load_image(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.image_processor.size["""longest_edge"""]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.image_processor.generate_crop_boxes(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.image_processor(images=lowerCAmelCase__							,  return_tensors="""pt"""		)

					with self.device_placement():
							if self.framework == "pt":
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_inference_context()
									with inference_context():
											__SCREAMING_SNAKE_CASE :	Any					 =	self._ensure_tensor_on_device(lowerCAmelCase__							,  device=self.device		)
											__SCREAMING_SNAKE_CASE :	int					 =	self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	image_embeddings

					__SCREAMING_SNAKE_CASE :	List[str]					 =	grid_points.shape[1]
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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 :	List[Any]					 =	grid_points[:, i : i + points_per_batch, :, :]
							__SCREAMING_SNAKE_CASE :	Dict					 =	input_labels[:, i : i + points_per_batch]
							__SCREAMING_SNAKE_CASE :	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 UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]=0.88							,  lowerCAmelCase__  :				Dict=0.95							,  lowerCAmelCase__  :				List[str]=0							,  lowerCAmelCase__  :				Optional[int]=1							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	model_inputs.pop("""input_boxes"""		)
					__SCREAMING_SNAKE_CASE :	int					 =	model_inputs.pop("""is_last"""		)
					__SCREAMING_SNAKE_CASE :	Any					 =	model_inputs.pop("""original_sizes"""		).tolist()
					__SCREAMING_SNAKE_CASE :	str					 =	model_inputs.pop("""reshaped_input_sizes"""		).tolist()

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.model(**lowerCAmelCase__		)

					# post processing happens here in order to avoid CPU GPU copies of ALL the masks
					__SCREAMING_SNAKE_CASE :	Any					 =	model_outputs["""pred_masks"""]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.image_processor.post_process_masks(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  binarize=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	model_outputs["""iou_scores"""]
					__SCREAMING_SNAKE_CASE :	Union[str, 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 UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				int=False							,  lowerCAmelCase__  :				List[str]=False							,  lowerCAmelCase__  :				Optional[Any]=0.7							,  ):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.cat(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	torch.cat(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.image_processor.post_process_for_mask_generation(
					    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)

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

					__SCREAMING_SNAKE_CASE :	str					 =	{}
					if output_rle_mask:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	rle_mask

					if output_bboxes_mask:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	bounding_boxes

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




'''simple docstring'''




import qiskit


def        lowerCAmelCase_     (			_lowerCamelCase:     int = 2      ):
		__SCREAMING_SNAKE_CASE :	Dict					 =	qubits

		# Using Aer's simulator
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	qiskit.Aer.get_backend("""aer_simulator"""      )

		# Creating a Quantum Circuit acting on the q register
		__SCREAMING_SNAKE_CASE :	str					 =	qiskit.QuantumCircuit(_lowerCamelCase ,   _lowerCamelCase      )

		# Adding a H gate on qubit 0 (now q0 in superposition)
		circuit.h(0      )

		for i in range(1 ,   _lowerCamelCase      ):
				# Adding CX (CNOT) gate
				circuit.cx(i - 1 ,   _lowerCamelCase      )

		# Mapping the quantum measurement to the classical bits
		circuit.measure(list(range(_lowerCamelCase      )      ) ,   list(range(_lowerCamelCase      )      )      )

		# Now measuring any one qubit would affect other qubits to collapse
		# their super position and have same state as the measured one.

		# Executing the circuit on the simulator
		__SCREAMING_SNAKE_CASE :	str					 =	qiskit.execute(_lowerCamelCase ,   _lowerCamelCase ,   shots=10_00      )

		return job.result().get_counts(_lowerCamelCase      )


if __name__ == "__main__":
						print(f"Total count for various states are: {quantum_entanglement(3)}")