






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__UpperCAmelCase     							=						Lock()




def 					lowercase__				(					__snake_case   :							int					,	__snake_case   :							Optional[Any]					,	__snake_case   :							List[str]					,	__snake_case   :							Dict					,	__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							List[str]  ):


					'''simple docstring'''


					global process_lock

					# we perform n swaps since after n swaps we know we are sorted
					# we *could* stop early if we are sorted already, but it takes as long to
					# find out we are sorted as it does to sort the list with this algorithm
					for i in range(0					,	10  ):
										if (i + position) % 2 == 0 and r_send is not None:
															# send your value to your right neighbor
															process_lock.acquire()
															r_send[1].send(__snake_case  )
															process_lock.release()

															# receive your right neighbor's value
															process_lock.acquire()
															UpperCAmelCase_   :				List[Any]						=						rr_cv[0].recv()
															process_lock.release()

															# take the lower value since you are on the left
															UpperCAmelCase_   :				Union[str, Any]						=						min(__snake_case					,	__snake_case  )
										elif (i + position) % 2 != 0 and l_send is not None:
															# send your value to your left neighbor
															process_lock.acquire()
															l_send[1].send(__snake_case  )
															process_lock.release()

															# receive your left neighbor's value
															process_lock.acquire()
															UpperCAmelCase_   :				List[Any]						=						lr_cv[0].recv()
															process_lock.release()

															# take the higher value since you are on the right
															UpperCAmelCase_   :				int						=						max(__snake_case					,	__snake_case  )
    # after all swaps are performed, send the values back to main
					result_pipe[1].send(__snake_case  )




def 					lowercase__				(					__snake_case   :							Tuple  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						[]
					UpperCAmelCase_   :				Union[str, Any]						=						[]
					# initialize the list of pipes where the values will be retrieved
					for _ in arr:
										result_pipe.append(Pipe()  )
					# creates the processes
					# the first and last process only have one neighbor so they are made outside
					# of the loop
					UpperCAmelCase_   :				List[Any]						=						Pipe()
					UpperCAmelCase_   :				Dict						=						Pipe()
					process_array_.append(
					    Process(
					        target=__snake_case					,	args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,	)  )
					UpperCAmelCase_   :				Optional[int]						=						temp_rs
					UpperCAmelCase_   :				Union[str, Any]						=						temp_rr

					for i in range(1					,	len(__snake_case  ) - 1  ):
										UpperCAmelCase_   :				List[Any]						=						Pipe()
										UpperCAmelCase_   :				Tuple						=						Pipe()
										process_array_.append(
										    Process(
										        target=__snake_case					,	args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,	)  )
										UpperCAmelCase_   :				Tuple						=						temp_rs
										UpperCAmelCase_   :				Optional[Any]						=						temp_rr

					process_array_.append(
					    Process(
					        target=__snake_case					,	args=(
					            len(__snake_case  ) - 1,
					            arr[len(__snake_case  ) - 1],
					            temp_ls,
					            None,
					            temp_lr,
					            None,
					            result_pipe[len(__snake_case  ) - 1],
					        )					,	)  )

					# start the processes
					for p in process_array_:
										p.start()

					# wait for the processes to end and write their values to the list
					for p in range(0					,	len(__snake_case  )  ):
										UpperCAmelCase_   :				Dict						=						result_pipe[p][0].recv()
										process_array_[p].join()
					return arr




def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						list(range(10					,	0					,	-1  )  )
					print('Initial List'  )
					print(*__snake_case  )
					UpperCAmelCase_   :				str						=						odd_even_transposition(__snake_case  )
					print('Sorted List\n'  )
					print(*__snake_case  )


if __name__ == "__main__":
		main()














from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from ..image_utils import load_image


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase     							=						logging.get_logger(__name__)


__UpperCAmelCase     							=						Dict[str, Any]
__UpperCAmelCase     							=						List[Prediction]



@add_end_docstrings(_snake_case      )
class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		def __init__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Optional[int]:
							super().__init__(*_UpperCamelCase    ,		**_UpperCamelCase       )

							if self.framework == "tf":
												raise ValueError(f"The {self.__class__} is only available in PyTorch."       )

							requires_backends(self    ,		'vision'       )
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()       )       )



		def  __UpperCAmelCase      (							self    ,		**_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						{}
							if "threshold" in kwargs:
												UpperCAmelCase_   :				Tuple						=						kwargs['threshold']
							return {}, {}, postprocess_kwargs



		def __call__(							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[Predictions, List[Prediction]]:
							return super().__call__(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict:
							UpperCAmelCase_   :				Any						=						load_image(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						torch.IntTensor([[image.height, image.width]]       )
							UpperCAmelCase_   :				str						=						self.image_processor(images=[image]    ,		return_tensors='pt'       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						self.tokenizer(text=inputs['words']    ,		boxes=inputs['boxes']    ,		return_tensors='pt'       )
							UpperCAmelCase_   :				Any						=						target_size
							return inputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Any						=						model_inputs.pop('target_size'       )
							UpperCAmelCase_   :				Optional[Any]						=						self.model(**_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						outputs.__class__({'target_size': target_size, **outputs}       )
							if self.tokenizer is not None:
												UpperCAmelCase_   :				List[str]						=						model_inputs['bbox']
							return model_outputs



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase=0.9       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						model_outputs['target_size']
							if self.tokenizer is not None:
												# This is a LayoutLMForTokenClassification variant.
												# The OCR got the boxes and the model classified the words.
												UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						target_size[0].tolist()

												def unnormalize(_UpperCamelCase       ):
																	return self._get_bounding_box(
																	    torch.Tensor(
																	        [
																	            (width * bbox[0] / 1_0_0_0),
																	            (height * bbox[1] / 1_0_0_0),
																	            (width * bbox[2] / 1_0_0_0),
																	            (height * bbox[3] / 1_0_0_0),
																	        ]       )       )

												UpperCAmelCase_	,  UpperCAmelCase_   :				List[Any]						=						model_outputs['logits'].squeeze(0       ).softmax(dim=-1       ).max(dim=-1       )
												UpperCAmelCase_   :				Union[str, Any]						=						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
												UpperCAmelCase_   :				Tuple						=						[unnormalize(_UpperCamelCase       ) for bbox in model_outputs['bbox'].squeeze(0       )]
												UpperCAmelCase_   :				List[str]						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Any						=						[dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       ) for vals in zip(scores.tolist()    ,		_UpperCamelCase    ,		_UpperCamelCase       ) if vals[0] > threshold]
							else:
												# This is a regular ForObjectDetectionModel
												UpperCAmelCase_   :				Union[str, Any]						=						self.image_processor.post_process_object_detection(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
												UpperCAmelCase_   :				str						=						raw_annotations[0]
												UpperCAmelCase_   :				str						=						raw_annotation['scores']
												UpperCAmelCase_   :				Tuple						=						raw_annotation['labels']
												UpperCAmelCase_   :				List[Any]						=						raw_annotation['boxes']

												UpperCAmelCase_   :				Union[str, Any]						=						scores.tolist()
												UpperCAmelCase_   :				int						=						[self.model.config.idalabel[label.item()] for label in labels]
												UpperCAmelCase_   :				Any						=						[self._get_bounding_box(_UpperCamelCase       ) for box in boxes]

												# {"scores": [...], ...} --> [{"score":x, ...}, ...]
												UpperCAmelCase_   :				int						=						['score', 'label', 'box']
												UpperCAmelCase_   :				Dict						=						[
												    dict(zip(_UpperCamelCase    ,		_UpperCamelCase       )       )
												    for vals in zip(raw_annotation['scores']    ,		raw_annotation['labels']    ,		raw_annotation['boxes']       )
												]

							return annotation



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							Dict[str, int]:
							if self.framework != "pt":
												raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'       )
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						box.int().tolist()
							UpperCAmelCase_   :				str						=						{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox






