
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[Any]					=     logging.get_logger(__name__)

@add_end_docstrings(__A    )
class 	__lowercase				(  __A    ):

			def __init__(self			,   **A      ):
								super().__init__(**UpperCamelCase__      )
								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(UpperCamelCase__      )

			def        UpperCAmelCase__ (self			,   **A      ):
								lowerCamelCase_					:      Dict     =		{}
								lowerCamelCase_					:      Any     =		{}
								lowerCamelCase_					:      Optional[int]     =		{}
								# preprocess args
								if "points_per_batch" in kwargs:
													lowerCamelCase_					:      Dict     =		kwargs['points_per_batch']
								if "points_per_crop" in kwargs:
													lowerCamelCase_					:      int     =		kwargs['points_per_crop']
								if "crops_n_layers" in kwargs:
													lowerCamelCase_					:      Optional[int]     =		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_					:      int     =		kwargs['crop_n_points_downscale_factor']
								# postprocess args
								if "pred_iou_thresh" in kwargs:
													lowerCamelCase_					:      List[str]     =		kwargs['pred_iou_thresh']
								if "stability_score_offset" in kwargs:
													lowerCamelCase_					:      List[str]     =		kwargs['stability_score_offset']
								if "mask_threshold" in kwargs:
													lowerCamelCase_					:      Dict     =		kwargs['mask_threshold']
								if "stability_score_thresh" in kwargs:
													lowerCamelCase_					:      Optional[Any]     =		kwargs['stability_score_thresh']
								if "crops_nms_thresh" in kwargs:
													lowerCamelCase_					:      Dict     =		kwargs['crops_nms_thresh']
								if "output_rle_mask" in kwargs:
													lowerCamelCase_					:      Optional[Any]     =		kwargs['output_rle_mask']
								if "output_bboxes_mask" in kwargs:
													lowerCamelCase_					:      Tuple     =		kwargs['output_bboxes_mask']
								return preprocess_kwargs, forward_params, postprocess_kwargs

			def __call__(self			,   A			,   *A			,   A=None			,   A=None			,   **A      ):
								return super().__call__(UpperCamelCase__			,   *UpperCamelCase__			,   num_workers=UpperCamelCase__			,   batch_size=UpperCamelCase__			,   **UpperCamelCase__      )

			def        UpperCAmelCase__ (self			,   A			,   A=6_4			,   A = 0			,   A = 5_1_2 / 1_5_0_0			,   A = 3_2			,   A = 1			,   ):
								lowerCamelCase_					:      Dict     =		load_image(UpperCamelCase__      )
								lowerCamelCase_					:      Any     =		self.image_processor.size['longest_edge']
								lowerCamelCase_					:      Any     =		self.image_processor.generate_crop_boxes(
								    UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__      )
								lowerCamelCase_					:      List[str]     =		self.image_processor(images=UpperCamelCase__			,   return_tensors='''pt'''      )

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

								lowerCamelCase_					:      Union[str, Any]     =		grid_points.shape[1]
								lowerCamelCase_					:      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			,   UpperCamelCase__			,   UpperCamelCase__      ):
													lowerCamelCase_					:      Tuple     =		grid_points[:, i : i + points_per_batch, :, :]
													lowerCamelCase_					:      Optional[Any]     =		input_labels[:, i : i + points_per_batch]
													lowerCamelCase_					:      Optional[int]     =		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			,   A			,   A=0.88			,   A=0.95			,   A=0			,   A=1			,   ):
								lowerCamelCase_					:      Any     =		model_inputs.pop('''input_boxes'''      )
								lowerCamelCase_					:      int     =		model_inputs.pop('''is_last'''      )
								lowerCamelCase_					:      Optional[int]     =		model_inputs.pop('''original_sizes'''      ).tolist()
								lowerCamelCase_					:      Optional[int]     =		model_inputs.pop('''reshaped_input_sizes'''      ).tolist()

								lowerCamelCase_					:      List[Any]     =		self.model(**UpperCamelCase__      )

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






			def        UpperCAmelCase__ (self			,   A			,   A=False			,   A=False			,   A=0.7			,   ):
								lowerCamelCase_					:      Union[str, Any]     =		[]
								lowerCamelCase_					:      Optional[Any]     =		[]
								lowerCamelCase_					:      Union[str, 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[str]     =		torch.cat(UpperCamelCase__      )
								lowerCamelCase_					:      Tuple     =		torch.cat(UpperCamelCase__      )
								lowerCamelCase_					:      str     =		self.image_processor.post_process_for_mask_generation(
								    UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__			,   UpperCamelCase__      )

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

								lowerCamelCase_					:      Optional[int]     =		{}
								if output_rle_mask:
													lowerCamelCase_					:      Tuple     =		rle_mask

								if output_bboxes_mask:
													lowerCamelCase_					:      Optional[Any]     =		bounding_boxes

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




'''simple docstring'''


import qiskit




def lowercase_	(   _lowercase			,       _lowercase				)     ->							qiskit.result.counts.Counts:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		qiskit.Aer.get_backend('''aer_simulator'''				)

					# Create a Quantum Circuit acting on the q register
					lowerCamelCase_					:      Optional[int]     =		qiskit.QuantumCircuit(_lowercase			,       _lowercase				)

					# Apply X (NOT) Gate to Qubits 0 & 1
					circuit.x(0				)
					circuit.x(1				)

					# Map the quantum measurement to the classical bits
					circuit.measure([0, 1]			,       [0, 1]				)

					# Execute the circuit on the qasm simulator
					lowerCamelCase_					:      Optional[int]     =		qiskit.execute(_lowercase			,       _lowercase			,       shots=1_000				)

					# Return the histogram data of the results of the experiment.
					return job.result().get_counts(_lowercase				)


if __name__ == "__main__":
					__lowercase							:   List[Any]					=     single_qubit_measure(2, 2)
					print(f'Total count for various states are: {counts}')


