


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

A							:					Optional[int]     						=     logging.get_logger(__name__)


A							:					Optional[Any]     						=     Dict[str, Any]
A							:					Dict     						=     List[Prediction]






@add_end_docstrings(_UpperCAmelCase   )
class     UpperCamelCase(  _UpperCAmelCase   ):


					def __init__(						self	:		List[str]     ,    *SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	int:




											'''simple docstring'''

											super().__init__(*lowercase__     ,    **lowercase__						)

											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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		{}
											if "threshold" in kwargs:
																	__snake_case				=		kwargs["threshold"]
											return {}, {}, postprocess_kwargs


					def __call__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		int						)			->	str:




											'''simple docstring'''

											return super().__call__(*lowercase__     ,    **lowercase__						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		load_image(lowercase__						)
											__snake_case				=		torch.IntTensor([[image.height, image.width]]						)
											__snake_case				=		self.image_processor(images=[image]     ,    return_tensors="pt"						)
											if self.tokenizer is not None:
																	__snake_case				=		self.tokenizer(text=inputs["words"]     ,    boxes=inputs["boxes"]     ,    return_tensors="pt"						)
											__snake_case				=		target_size
											return inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		model_inputs.pop("target_size"						)
											__snake_case				=		self.model(**lowercase__						)
											__snake_case				=		outputs.__class__({"target_size": target_size, **outputs}						)
											if self.tokenizer is not None:
																	__snake_case				=		model_inputs["bbox"]
											return model_outputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int=0.9						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		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.
																	__snake_case				=		target_size[0].tolist()

																	def unnormalize(SCREAMING_SNAKE_CASE	:		List[Any]						):
																							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),
																							        ]						)						)

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

																	__snake_case				=		scores.tolist()
																	__snake_case				=		[self.model.config.idalabel[label.item()] for label in labels]
																	__snake_case				=		[self._get_bounding_box(lowercase__						) for box in boxes]

																	# {"scores": [...], ...} --> [{"score":x, ...}, ...]
																	__snake_case				=		["score", "label", "box"]
																	__snake_case				=		[
																	    dict(zip(lowercase__     ,    lowercase__						)						)
																	    for vals in zip(raw_annotation["scores"]     ,    raw_annotation["labels"]     ,    raw_annotation["boxes"]						)
																	]

											return annotation






					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	Optional[Any]:




											'''simple docstring'''

											if self.framework != "pt":
																	raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."						)
											__snake_case				=		box.int().tolist()
											__snake_case				=		{
											    "xmin": xmin,
											    "ymin": ymin,
											    "xmax": xmax,
											    "ymax": ymax,
											}
											return bbox









from __future__ import annotations





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	list:


						'''simple docstring'''






						if len(_lowerCAmelCase ) == 0:
												return []
						__snake_case		,					__snake_case				=		min(_lowerCAmelCase ), max(_lowerCAmelCase )
						__snake_case				=		int(max_value - min_value ) + 1
						__snake_case				=		[[] for _ in range(_lowerCAmelCase )]

						for i in my_list:
												buckets[int(i - min_value )].append(_lowerCAmelCase )

						return [v for bucket in buckets for v in sorted(_lowerCAmelCase )]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





