






from __future__ import annotations

from typing import Any



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase = 0       )     ->							None:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						row, column
							UpperCAmelCase_   :				List[Any]						=						[[default_value for c in range(_UpperCamelCase       )] for r in range(_UpperCamelCase       )]



		def __str__(							self       )     ->							str:
							UpperCAmelCase_   :				Any						=						f"Matrix consist of {self.row} rows and {self.column} columns\n"

							# Make string identifier
							UpperCAmelCase_   :				List[Any]						=						0
							for row_vector in self.array:
												for obj in row_vector:
																	UpperCAmelCase_   :				Union[str, Any]						=						max(_UpperCamelCase    ,		len(str(_UpperCamelCase       )       )       )
							UpperCAmelCase_   :				int						=						f"%{max_element_length}s"

							# Make string and return
							def single_line(_UpperCamelCase       ) -> str:
												nonlocal string_format_identifier
												UpperCAmelCase_   :				str						=						'['
												line += ", ".join(string_format_identifier % (obj,) for obj in row_vector       )
												line += "]"
												return line

							s += "\n".join(single_line(_UpperCamelCase       ) for row_vector in self.array       )
							return s



		def __repr__(							self       )     ->							str:
							return str(self       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							bool:
							if not (isinstance(_UpperCamelCase    ,		(list, tuple)       ) and len(_UpperCamelCase       ) == 2):
												return False
							elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
												return False
							else:
												return True



		def __getitem__(							self    ,		_UpperCamelCase       )     ->							Any:
							assert self.validate_indicies(_UpperCamelCase       )
							return self.array[loc[0]][loc[1]]



		def __setitem__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							None:
							assert self.validate_indicies(_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						value



		def __add__(							self    ,		_UpperCamelCase       )     ->							Matrix:
							assert isinstance(_UpperCamelCase    ,		_UpperCamelCase       )
							assert self.row == another.row and self.column == another.column

							# Add
							UpperCAmelCase_   :				Dict						=						Matrix(self.row    ,		self.column       )
							for r in range(self.row       ):
												for c in range(self.column       ):
																	UpperCAmelCase_   :				Optional[int]						=						self[r, c] + another[r, c]
							return result



		def __neg__(							self       )     ->							Matrix:
							UpperCAmelCase_   :				Optional[int]						=						Matrix(self.row    ,		self.column       )
							for r in range(self.row       ):
												for c in range(self.column       ):
																	UpperCAmelCase_   :				List[Any]						=						-self[r, c]
							return result



		def __sub__(							self    ,		_UpperCamelCase       )     ->							Matrix:
							return self + (-another)



		def __mul__(							self    ,		_UpperCamelCase       )     ->							Matrix:

							if isinstance(_UpperCamelCase    ,		(int, float)       ):  # Scalar multiplication
												UpperCAmelCase_   :				Dict						=						Matrix(self.row    ,		self.column       )
												for r in range(self.row       ):
																	for c in range(self.column       ):
																						UpperCAmelCase_   :				str						=						self[r, c] * another
												return result
							elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):  # Matrix multiplication
												assert self.column == another.row
												UpperCAmelCase_   :				List[str]						=						Matrix(self.row    ,		another.column       )
												for r in range(self.row       ):
																	for c in range(another.column       ):
																						for i in range(self.column       ):
																											result[r, c] += self[r, i] * another[i, c]
												return result
							else:
												UpperCAmelCase_   :				Optional[Any]						=						f"Unsupported type given for another ({type(_UpperCamelCase       )})"
												raise TypeError(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Matrix:
							UpperCAmelCase_   :				Tuple						=						Matrix(self.column    ,		self.row       )
							for r in range(self.row       ):
												for c in range(self.column       ):
																	UpperCAmelCase_   :				int						=						self[r, c]
							return result



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Any:
							assert isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) and isinstance(_UpperCamelCase    ,		_UpperCamelCase       )
							assert self.row == self.column == u.row == v.row  # u, v should be column vector
							assert u.column == v.column == 1  # u, v should be column vector

							# Calculate
							UpperCAmelCase_   :				Union[str, Any]						=						v.transpose()
							UpperCAmelCase_   :				int						=						(v_t * self * u)[0, 0] + 1
							if numerator_factor == 0:
												return None  # It's not invertable
							return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




		def 					lowercase__				(					):


							'''simple docstring'''


							UpperCAmelCase_   :				Any						=						Matrix(3					,	3					,	0  )
							for i in range(3  ):
												UpperCAmelCase_   :				Union[str, Any]						=						1
							print(F"a^(-1) is {ainv}"  )
							# u, v
							UpperCAmelCase_   :				List[str]						=						Matrix(3					,	1					,	0  )
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						1, 2, -3
							UpperCAmelCase_   :				Union[str, Any]						=						Matrix(3					,	1					,	0  )
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				str						=						4, -2, 5
							print(F"u is {u}"  )
							print(F"v is {v}"  )
							print(F"uv^T is {u * v.transpose()}"  )
							# Sherman Morrison
							print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(__snake_case					,	__snake_case  )}"  )




		def 					lowercase__				(					):


							'''simple docstring'''


							import doctest

							doctest.testmod()






		testa()














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






