
'''simple docstring'''

from __future__ import annotations

from typing import Any

class       UpperCAmelCase					:





		"""simple docstring"""





		def __init__(     self	,       _snake_case	,       _snake_case	,       _snake_case = 0  )					->     None:
				_UpperCamelCase,				_UpperCamelCase							:			Tuple     =   row, column
				_UpperCamelCase							:			Tuple     =   [[default_value for c in range(_lowercase  )] for r in range(_lowercase  )]
		def __str__(     self  )					->     str:
				_UpperCamelCase							:			Union[str, 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							:			Optional[int]     =   max(_lowercase	,       len(str(_lowercase  )  )  )
				_UpperCamelCase							:			int     =   F'''%{max_element_length}s'''

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

				s += "\n".join(single_line(_lowercase  ) for row_vector in self.array  )
				return s
		def __repr__(     self  )					->     str:
				return str(self  )
		def _lowercase	(     self	,       _snake_case  )					->     bool:
				if not (isinstance(_lowercase	,       (list, tuple)  ) and len(_lowercase  ) == 2):
						return False
				elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
						return False
				else:
						return True
		def __getitem__(     self	,       _snake_case  )					->     Any:
				assert self.validate_indicies(_lowercase  )
				return self.array[loc[0]][loc[1]]
		def __setitem__(     self	,       _snake_case	,       _snake_case  )					->     None:
				assert self.validate_indicies(_lowercase  )
				_UpperCamelCase							:			Any     =   value
		def __add__(     self	,       _snake_case  )					->     Matrix:
				assert isinstance(_lowercase	,       _lowercase  )
				assert self.row == another.row and self.column == another.column

				# Add
				_UpperCamelCase							:			str     =   Matrix(self.row	,       self.column  )
				for r in range(self.row  ):
						for c in range(self.column  ):
								_UpperCamelCase							:			Optional[Any]     =   self[r, c] + another[r, c]
				return result
		def __neg__(     self  )					->     Matrix:
				_UpperCamelCase							:			Dict     =   Matrix(self.row	,       self.column  )
				for r in range(self.row  ):
						for c in range(self.column  ):
								_UpperCamelCase							:			Optional[Any]     =   -self[r, c]
				return result
		def __sub__(     self	,       _snake_case  )					->     Matrix:
				return self + (-another)
		def __mul__(     self	,       _snake_case  )					->     Matrix:

				if isinstance(_lowercase	,       (int, float)  ):  # Scalar multiplication
						_UpperCamelCase							:			Optional[Any]     =   Matrix(self.row	,       self.column  )
						for r in range(self.row  ):
								for c in range(self.column  ):
										_UpperCamelCase							:			Dict     =   self[r, c] * another
						return result
				elif isinstance(_lowercase	,       _lowercase  ):  # Matrix multiplication
						assert self.column == another.row
						_UpperCamelCase							:			Optional[Any]     =   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							:			List[Any]     =   F'''Unsupported type given for another ({type(_lowercase  )})'''
						raise TypeError(_lowercase  )
		def _lowercase	(     self  )					->     Matrix:
				_UpperCamelCase							:			Any     =   Matrix(self.column	,       self.row  )
				for r in range(self.row  ):
						for c in range(self.column  ):
								_UpperCamelCase							:			Tuple     =   self[r, c]
				return result




		def _lowercase	(     self	,       _snake_case	,       _snake_case  )					->     Any:
				assert isinstance(_lowercase	,       _lowercase  ) and isinstance(_lowercase	,       _lowercase  )
				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							:			Any     =   v.transpose()
				_UpperCamelCase							:			Tuple     =   (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     snake_case__						(   )			->   None:
			# a^(-1)
			_UpperCamelCase							:			Optional[Any]     =   Matrix(3      ,3      ,0						)
			for i in range(3						):
					_UpperCamelCase							:			List[Any]     =   1
			print(f'''a^(-1) is {ainv}'''						)
			# u, v
			_UpperCamelCase							:			List[Any]     =   Matrix(3      ,1      ,0						)
			_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase							:			Union[str, Any]     =   1, 2, -3
			_UpperCamelCase							:			int     =   Matrix(3      ,1      ,0						)
			_UpperCamelCase,				_UpperCamelCase,				_UpperCamelCase							:			Union[str, Any]     =   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(lowerCAmelCase__      ,lowerCAmelCase__						)}'''						)







	def     snake_case__						(   )			->   None:
			import doctest

			doctest.testmod()


	testa()








'''simple docstring'''

import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:






							class    snake_case     :






											"""simple docstring"""



											@staticmethod
											def   a__					(    *_lowercase,     **_lowercase       )     ->							Tuple:
																		pass






@is_pipeline_test
@require_vision
@require_torch
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				[
											    {
											        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											        'candidate_labels': ['cat', 'remote', 'couch'],
											    }
											]
											return object_detector, examples



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				object_detector(examples[0],     threshold=0.0       )

											SCREAMING_SNAKE_CASE_				=				len(_lowercase       )
											self.assertGreater(_lowercase,     0       )
											self.assertEqual(
											    _lowercase,     [
											        {
											            'score': ANY(_lowercase       ),
											            'label': ANY(_lowercase       ),
											            'box': {'xmin': ANY(_lowercase       ), 'ymin': ANY(_lowercase       ), 'xmax': ANY(_lowercase       ), 'ymax': ANY(_lowercase       )},
											        }
											        for i in range(_lowercase       )
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Union[str, Any]:
											pass



				@require_torch
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    './tests/fixtures/tests_samples/COCO/000000039769.png',     candidate_labels=['cat', 'remote', 'couch'],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        }
											    ],     threshold=0.64,     )

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
											            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        ]
											    ],     )



				@require_torch
				@slow
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											    ],     )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    [
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											        {
											            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
											            'candidate_labels': ['cat', 'remote', 'couch'],
											        },
											    ],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											        [
											            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											    ],     )



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Optional[Any]:
											pass



				@require_torch
				@slow
				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				0.2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     threshold=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											    ],     )





				@require_torch
				@slow
				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     top_k=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
											        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
											    ],     )





