



'''simple docstring'''




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

lowerCamelCase							:int       = logging.get_logger(__name__)


lowerCamelCase							:str       = Dict[str, Any]
lowerCamelCase							:List[str]       = List[Prediction]







@add_end_docstrings(__UpperCAmelCase			)
class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				*lowercase	,				**lowercase							):
				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    _a     (self	,				**lowercase							):
				A_     :						str     =					{}
				if "threshold" in kwargs:
							A_     :						Dict     =					kwargs["""threshold"""]
				return {}, {}, postprocess_kwargs


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)


	def    _a     (self	,				lowercase							):
				A_     :						List[str]     =					load_image(lowercase							)
				A_     :						List[Any]     =					torch.IntTensor([[image.height, image.width]]							)
				A_     :						int     =					self.image_processor(images=[image]	,				return_tensors="""pt"""							)
				if self.tokenizer is not None:
							A_     :						Dict     =					self.tokenizer(text=inputs["""words"""]	,				boxes=inputs["""boxes"""]	,				return_tensors="""pt"""							)
				A_     :						List[str]     =					target_size
				return inputs


	def    _a     (self	,				lowercase							):
				A_     :						Optional[Any]     =					model_inputs.pop("""target_size"""							)
				A_     :						Union[str, Any]     =					self.model(**lowercase							)
				A_     :						List[str]     =					outputs.__class__({"""target_size""": target_size, **outputs}							)
				if self.tokenizer is not None:
							A_     :						Any     =					model_inputs["""bbox"""]
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=0.9							):
				A_     :						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.
							A_,   A_     :						Optional[Any]     =					target_size[0].tolist()

							def unnormalize(lowercase							):
										return self._get_bounding_box(
										    torch.Tensor(
										        [
										            (width * bbox[0] / 1000),
										            (height * bbox[1] / 1000),
										            (width * bbox[2] / 1000),
										            (height * bbox[3] / 1000),
										        ]							)							)

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

							A_     :						Any     =					scores.tolist()
							A_     :						List[str]     =					[self.model.config.idalabel[label.item()] for label in labels]
							A_     :						str     =					[self._get_bounding_box(lowercase							) for box in boxes]

							# {"scores": [...], ...} --> [{"score":x, ...}, ...]
							A_     :						List[str]     =					["""score""", """label""", """box"""]
							A_     :						Optional[Any]     =					[
							    dict(zip(lowercase	,				lowercase							)							)
							    for vals in zip(raw_annotation["""scores"""]	,				raw_annotation["""labels"""]	,				raw_annotation["""boxes"""]							)
							]

				return annotation


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




'''simple docstring'''




from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[Any]     =					prime_factors(lowerCamelCase__   )
			if is_square_free(lowerCamelCase__   ):
						return -1 if len(lowerCamelCase__   ) % 2 else 1
			return 0


if __name__ == "__main__":
						import doctest

						doctest.testmod()