
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase       :				Tuple       =			logging.get_logger(__name__)






def        a_		(       __lowercase   :	Dict ) ->  Dict:
			_snake_case		=			SwinConfig(
			    embed_dim=192       ,					depths=(2, 2, 18, 2)       ,					num_heads=(6, 12, 24, 48)       ,					window_size=12       ,					out_features=['stage2', 'stage3', 'stage4']       ,					)

			_snake_case		=			DetaConfig(
			    backbone_config=__lowercase       ,					num_queries=900       ,					encoder_ffn_dim=2_048       ,					decoder_ffn_dim=2_048       ,					num_feature_levels=5       ,					assign_first_stage=__lowercase       ,					with_box_refine=__lowercase       ,					two_stage=__lowercase       ,					)

			# set labels
			_snake_case		=			'huggingface/label-files'
			if "o365" in model_name:
						_snake_case		=			366
						_snake_case		=			'object365-id2label.json'
			else:
						_snake_case		=			91
						_snake_case		=			'coco-detection-id2label.json'

			_snake_case		=			num_labels
			_snake_case		=			json.load(open(cached_download(hf_hub_url(__lowercase       ,					__lowercase       ,					repo_type='dataset' ) )       ,					'r' ) )
			_snake_case		=			{int(__lowercase ): v for k, v in idalabel.items()}
			_snake_case		=			idalabel
			_snake_case		=			{v: k for k, v in idalabel.items()}

			return config






def        a_		(       __lowercase   :	List[Any] ) ->  List[Any]:
			_snake_case		=			[]

			# stem
			# fmt: off
			rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight') )
			rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias') )
			rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight') )
			rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias') )
			# stages
			for i in range(len(config.backbone_config.depths ) ):
						for j in range(config.backbone_config.depths[i] ):
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )

						if i < 3:
									rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''') )
									rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''') )

			rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight') )
			rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias') )
			rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight') )
			rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias') )
			rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight') )
			rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias') )

			# transformer encoder
			for i in range(config.encoder_layers ):
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''') )
						rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''') )

			# transformer decoder
			for i in range(config.decoder_layers ):
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''') )
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''') )

			# fmt: on

			return rename_keys






def        a_		(       __lowercase   :	Union[str, Any]       ,					__lowercase   :	int       ,					__lowercase   :	Any ) ->  Optional[Any]:
			_snake_case		=			dct.pop(__lowercase )
			_snake_case		=			val






def        a_		(       __lowercase   :	int       ,					__lowercase   :	Optional[Any] ) ->  Optional[int]:
			_snake_case		=			[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
			for i in range(len(backbone_config.depths ) ):
						_snake_case		=			num_features[i]
						for j in range(backbone_config.depths[i] ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									_snake_case		=			state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight''' )
									_snake_case		=			state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias''' )
									# next, add query, keys and values (in that order) to the state dict
									_snake_case		=			in_proj_weight[:dim, :]
									_snake_case		=			in_proj_bias[: dim]
									_snake_case		=			in_proj_weight[
									    dim : dim * 2, :
									]
									_snake_case		=			in_proj_bias[
									    dim : dim * 2
									]
									_snake_case		=			in_proj_weight[
									    -dim :, :
									]
									_snake_case		=			in_proj_bias[-dim :]
									# fmt: on






def        a_		(       __lowercase   :	Optional[int]       ,					__lowercase   :	Optional[int] ) ->  Union[str, Any]:
			# transformer decoder self-attention layers
			_snake_case		=			config.d_model
			for i in range(config.decoder_layers ):
						# read in weights + bias of input projection layer of self-attention
						_snake_case		=			state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
						_snake_case		=			state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
						# next, add query, keys and values (in that order) to the state dict
						_snake_case		=			in_proj_weight[:hidden_size, :]
						_snake_case		=			in_proj_bias[:hidden_size]
						_snake_case		=			in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						_snake_case		=			in_proj_bias[hidden_size : hidden_size * 2]
						_snake_case		=			in_proj_weight[-hidden_size:, :]
						_snake_case		=			in_proj_bias[-hidden_size:]






def        a_		(       ) ->  Tuple:
			_snake_case		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case		=			Image.open(requests.get(__lowercase       ,					stream=__lowercase ).raw )

			return im






@torch.no_grad()
def        a_		(       __lowercase   :	Optional[Any]       ,					__lowercase   :	Optional[int]       ,					__lowercase   :	Dict ) ->  List[str]:
			_snake_case		=			get_deta_config(__lowercase )

			# load original state dict
			if model_name == "deta-swin-large":
						_snake_case		=			hf_hub_download(repo_id='nielsr/deta-checkpoints'       ,					filename='adet_swin_ft.pth' )
			elif model_name == "deta-swin-large-o365":
						_snake_case		=			hf_hub_download(repo_id='jozhang97/deta-swin-l-o365'       ,					filename='deta_swin_pt_o365.pth' )
			else:
						raise ValueError(f'''Model name {model_name} not supported''' )

			_snake_case		=			torch.load(__lowercase       ,					map_location='cpu' )['model']

			# original state dict
			for name, param in state_dict.items():
						print(__lowercase       ,					param.shape )

			# rename keys
			_snake_case		=			create_rename_keys(__lowercase )
			for src, dest in rename_keys:
						rename_key(__lowercase       ,					__lowercase       ,					__lowercase )
			read_in_swin_q_k_v(__lowercase       ,					config.backbone_config )
			read_in_decoder_q_k_v(__lowercase       ,					__lowercase )

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									_snake_case		=			state_dict.pop(__lowercase )
									_snake_case		=			val
						if "input_proj" in key:
									_snake_case		=			state_dict.pop(__lowercase )
									_snake_case		=			val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									_snake_case		=			state_dict.pop(__lowercase )
									_snake_case		=			val

    # finally, create HuggingFace model and load state dict
			_snake_case		=			DetaForObjectDetection(__lowercase )
			model.load_state_dict(__lowercase )
			model.eval()

			_snake_case		=			'cuda' if torch.cuda.is_available() else 'cpu'
			model.to(__lowercase )

			# load image processor
			_snake_case		=			DetaImageProcessor(format='coco_detection' )

			# verify our conversion on image
			_snake_case		=			prepare_img()
			_snake_case		=			processor(images=__lowercase       ,					return_tensors='pt' )
			_snake_case		=			encoding['pixel_values']
			_snake_case		=			model(pixel_values.to(__lowercase ) )

			# verify logits
			print('Logits:'       ,					outputs.logits[0, :3, :3] )
			print('Boxes:'       ,					outputs.pred_boxes[0, :3, :3] )
			if model_name == "deta-swin-large":
						_snake_case		=			torch.tensor(
						    [[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]] )
						_snake_case		=			torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]] )
			elif model_name == "deta-swin-large-o365":
						_snake_case		=			torch.tensor(
						    [[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]] )
						_snake_case		=			torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]] )
			assert torch.allclose(outputs.logits[0, :3, :3]       ,					expected_logits.to(__lowercase )       ,					atol=1E-4 )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,					expected_boxes.to(__lowercase )       ,					atol=1E-4 )
			print('Everything ok!' )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...''' )
						Path(__lowercase ).mkdir(exist_ok=__lowercase )
						model.save_pretrained(__lowercase )
						processor.save_pretrained(__lowercase )

			# Push to hub
			if push_to_hub:
						print('Pushing model and processor to hub...' )
						model.push_to_hub(f'''jozhang97/{model_name}''' )
						processor.push_to_hub(f'''jozhang97/{model_name}''' )


if __name__ == "__main__":
			_lowerCamelCase       :				Tuple       =			argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    type=str,
			    default='''deta-swin-large''',
			    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
			    help='''Name of the model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=str,
			    help='''Path to the folder to output PyTorch model.''',
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)
			_lowerCamelCase       :				Optional[Any]       =			parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

from __future__ import annotations

from typing import Any




class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    Tuple			,  lowercase	:    int			,  lowercase	:    int			,  lowercase	:    float = 0	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			row, column
				_snake_case		=			[[default_value for c in range(lowercase	)] for r in range(lowercase	)]



	def __str__(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

				# Make string identifier
				_snake_case		=			0
				for row_vector in self.array:
							for obj in row_vector:
										_snake_case		=			max(lowercase			,  len(str(lowercase	)	)	)
				_snake_case		=			f'''%{max_element_length}s'''

				# Make string and return
				def single_line(lowercase	:    list[float]	) -> str:
							nonlocal string_format_identifier
							_snake_case		=			'['
							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	:    Dict	):


				'''simple docstring'''


				return str(self	)



	def  A							(  self	:    str			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				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	:    Dict			,  lowercase	:    tuple[int, int]	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				return self.array[loc[0]][loc[1]]



	def __setitem__(  self	:    str			,  lowercase	:    tuple[int, int]			,  lowercase	:    float	):


				'''simple docstring'''


				assert self.validate_indicies(lowercase	)
				_snake_case		=			value



	def __add__(  self	:    str			,  lowercase	:    Matrix	):


				'''simple docstring'''


				assert isinstance(lowercase			,  lowercase	)
				assert self.row == another.row and self.column == another.column

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



	def __neg__(  self	:    Tuple	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.row			,  self.column	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			-self[r, c]
				return result



	def __sub__(  self	:    List[str]			,  lowercase	:    Matrix	):


				'''simple docstring'''


				return self + (-another)



	def __mul__(  self	:    Dict			,  lowercase	:    int | float | Matrix	):


				'''simple docstring'''


				if isinstance(lowercase			,  (int, float)	):  # Scalar multiplication
							_snake_case		=			Matrix(self.row			,  self.column	)
							for r in range(self.row	):
										for c in range(self.column	):
													_snake_case		=			self[r, c] * another
							return result
				elif isinstance(lowercase			,  lowercase	):  # Matrix multiplication
							assert self.column == another.row
							_snake_case		=			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:
							_snake_case		=			f'''Unsupported type given for another ({type(lowercase	)})'''
							raise TypeError(lowercase	)



	def  A							(  self	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			Matrix(self.column			,  self.row	)
				for r in range(self.row	):
							for c in range(self.column	):
										_snake_case		=			self[r, c]
				return result





	def  A							(  self	:    List[Any]			,  lowercase	:    Matrix			,  lowercase	:    Matrix	):


				'''simple docstring'''


				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
				_snake_case		=			v.transpose()
				_snake_case		=			(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        a_		(       ) ->  None:
						# a^(-1)
						_snake_case		=			Matrix(3       ,					3       ,					0 )
						for i in range(3 ):
									_snake_case		=			1
						print(f'''a^(-1) is {ainv}''' )
						# u, v
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			1, 2, -3
						_snake_case		=			Matrix(3       ,					1       ,					0 )
						_snake_case	,					_snake_case	,					_snake_case		=			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(__lowercase       ,					__lowercase )}''' )






			def        a_		(       ) ->  None:
						import doctest

						doctest.testmod()

			testa()