





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      : int 				=				logging.get_logger(__name__)


__a      : List[Any] 				=				Dict[str, Any]
__a      : Optional[int] 				=				List[Prediction]


@add_end_docstrings(__snake_case   )
class 							__UpperCAmelCase							(						__snake_case   ):



						"""simple docstring"""







						def __init__(   self	,		*SCREAMING_SNAKE_CASE	,		**SCREAMING_SNAKE_CASE				) ->      Any:





												"""simple docstring"""
												super().__init__(*SCREAMING_SNAKE_CASE	,		**SCREAMING_SNAKE_CASE				)

												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	,		**SCREAMING_SNAKE_CASE				) ->      Any:





												"""simple docstring"""
												UpperCamelCase  							= {}
												if "threshold" in kwargs:
																		UpperCamelCase  							= kwargs["threshold"]
												return {}, {}, postprocess_kwargs




						def __call__(   self	,		*SCREAMING_SNAKE_CASE	,		**SCREAMING_SNAKE_CASE				) ->      Union[Predictions, List[Prediction]]:





												"""simple docstring"""
												return super().__call__(*SCREAMING_SNAKE_CASE	,		**SCREAMING_SNAKE_CASE				)




						def 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE				) ->      Dict:





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




						def 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE				) ->      str:





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




						def 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE	,		SCREAMING_SNAKE_CASE=0.9				) ->      List[str]:





												"""simple docstring"""
												UpperCamelCase  							= 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  							= target_size[0].tolist()

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

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

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

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

												return annotation




						def 		__lowerCAmelCase			(   self	,		SCREAMING_SNAKE_CASE				) ->      Dict[str, int]:





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






import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   0.0
							for i in range(len(snake_case    )    ):
													da += math.pow((sample[i] - weights[0][i])      ,	2    )
													da += math.pow((sample[i] - weights[1][i])      ,	2    )
													return 0 if da > da else 1
							return 0






	def       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						_lowercase       :	Tuple           =   [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""  )
						print(f"""Weights that have been trained : {weights}"""  )


# running the main() function
if __name__ == "__main__":
							main()




