

'''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

UpperCamelCase_							=					logging.get_logger(__name__)


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





@add_end_docstrings(lowercase__			)
class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Any		,				*UpperCAmelCase__    :		Any		,				**UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										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    lowerCamelCase_  (					self    :		Dict		,				**UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										lowercase   :       Dict								={}
										if "threshold" in kwargs:
																lowercase   :       str								=kwargs['''threshold''']
										return {}, {}, postprocess_kwargs







				def __call__(					self    :		Tuple		,				*UpperCAmelCase__    :		int		,				**UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										return super().__call__(*UpperCAmelCase__		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=load_image(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=torch.IntTensor([[image.height, image.width]]      )
										lowercase   :       int								=self.image_processor(images=[image]		,				return_tensors='''pt'''      )
										if self.tokenizer is not None:
																lowercase   :       int								=self.tokenizer(text=inputs['''words''']		,				boxes=inputs['''boxes''']		,				return_tensors='''pt'''      )
										lowercase   :       str								=target_size
										return inputs







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Any								=model_inputs.pop('''target_size'''      )
										lowercase   :       Union[str, Any]								=self.model(**UpperCAmelCase__      )
										lowercase   :       Any								=outputs.__class__({'''target_size''': target_size, **outputs}      )
										if self.tokenizer is not None:
																lowercase   :       List[str]								=model_inputs['''bbox''']
										return model_outputs







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Dict=0.9      ):
										'''simple docstring'''
										lowercase   :       Dict								=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.
																lowercase   :       Union[str, Any]								=target_size[0].tolist()

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

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

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

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

										return annotation







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		"torch.Tensor"      ):
										'''simple docstring'''
										if self.framework != "pt":
																raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''      )
										lowercase   :       Union[str, Any]								=box.int().tolist()
										lowercase   :       Optional[Any]								={
										    '''xmin''': xmin,
										    '''ymin''': ymin,
										    '''xmax''': xmax,
										    '''ymax''': ymax,
										}
										return bbox








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]	)							->     str:
						lowercase   :       Optional[Any]								=[0 for i in range(r + 1	)]
						# nc0 = 1
						lowercase   :       Optional[Any]								=1
						for i in range(1      ,   n + 1	):
												# to compute current row from previous row.
												lowercase   :       str								=min(__magic_name__      ,   __magic_name__	)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]







print(binomial_coefficient(n=10, r=5))





