"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							YolosConfig()

	# size of the architecture
	if "yolos_ti" in yolos_name:
		_lowercase :    Union[str, Any]							=							192
		_lowercase :    str							=							768
		_lowercase :    str							=							12
		_lowercase :    int							=							3
		_lowercase :    Tuple							=							[800, 1_333]
		_lowercase :    Union[str, Any]							=							False
	elif yolos_name == "yolos_s_dWr":
		_lowercase :    List[str]							=							330
		_lowercase :    Tuple							=							14
		_lowercase :    Any							=							6
		_lowercase :    Optional[Any]							=							1_320
	elif "yolos_s" in yolos_name:
		_lowercase :    Optional[int]							=							384
		_lowercase :    int							=							1_536
		_lowercase :    Optional[int]							=							12
		_lowercase :    Dict							=							6
	elif "yolos_b" in yolos_name:
		_lowercase :    Tuple							=							[800, 1_344]

	_lowercase :    Any							=							91
	_lowercase :    int							=							'huggingface/label-files'
	_lowercase :    int							=							'coco-detection-id2label.json'
	_lowercase :    int							=							json.load(open(hf_hub_download(__UpperCAmelCase     ,__UpperCAmelCase     ,repo_type='dataset'			)     ,'r'			)			)
	_lowercase :    int							=							{int(__UpperCAmelCase			): v for k, v in idalabel.items()}
	_lowercase :    Optional[int]							=							idalabel
	_lowercase :    List[str]							=							{v: k for k, v in idalabel.items()}

	return config


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = False			):






	"""simple docstring"""





	for i in range(config.num_hidden_layers			):
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		_lowercase :    int							=							state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
		_lowercase :    Any							=							state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
		# next, add query, keys and values (in that order) to the state dict
		_lowercase :    Dict							=							in_proj_weight[: config.hidden_size, :]
		_lowercase :    Any							=							in_proj_bias[: config.hidden_size]
		_lowercase :    Tuple							=							in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		_lowercase :    Union[str, Any]							=							in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		_lowercase :    Any							=							in_proj_weight[-config.hidden_size :, :]
		_lowercase :    int							=							in_proj_bias[-config.hidden_size :]


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if "backbone" in name:
		_lowercase :    Any							=							name.replace('backbone'     ,'vit'			)
	if "cls_token" in name:
		_lowercase :    int							=							name.replace('cls_token'     ,'embeddings.cls_token'			)
	if "det_token" in name:
		_lowercase :    int							=							name.replace('det_token'     ,'embeddings.detection_tokens'			)
	if "mid_pos_embed" in name:
		_lowercase :    Dict							=							name.replace('mid_pos_embed'     ,'encoder.mid_position_embeddings'			)
	if "pos_embed" in name:
		_lowercase :    List[Any]							=							name.replace('pos_embed'     ,'embeddings.position_embeddings'			)
	if "patch_embed.proj" in name:
		_lowercase :    Union[str, Any]							=							name.replace('patch_embed.proj'     ,'embeddings.patch_embeddings.projection'			)
	if "blocks" in name:
		_lowercase :    Optional[Any]							=							name.replace('blocks'     ,'encoder.layer'			)
	if "attn.proj" in name:
		_lowercase :    Optional[Any]							=							name.replace('attn.proj'     ,'attention.output.dense'			)
	if "attn" in name:
		_lowercase :    str							=							name.replace('attn'     ,'attention.self'			)
	if "norm1" in name:
		_lowercase :    Any							=							name.replace('norm1'     ,'layernorm_before'			)
	if "norm2" in name:
		_lowercase :    Optional[Any]							=							name.replace('norm2'     ,'layernorm_after'			)
	if "mlp.fc1" in name:
		_lowercase :    str							=							name.replace('mlp.fc1'     ,'intermediate.dense'			)
	if "mlp.fc2" in name:
		_lowercase :    Any							=							name.replace('mlp.fc2'     ,'output.dense'			)
	if "class_embed" in name:
		_lowercase :    List[str]							=							name.replace('class_embed'     ,'class_labels_classifier'			)
	if "bbox_embed" in name:
		_lowercase :    Any							=							name.replace('bbox_embed'     ,'bbox_predictor'			)
	if "vit.norm" in name:
		_lowercase :    List[str]							=							name.replace('vit.norm'     ,'vit.layernorm'			)

	return name


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		_lowercase :    str							=							orig_state_dict.pop(__UpperCAmelCase			)

		if "qkv" in key:
			_lowercase :    Any							=							key.split('.'			)
			_lowercase :    int							=							int(key_split[2]			)
			_lowercase :    Tuple							=							model.vit.encoder.layer[layer_num].attention.attention.all_head_size
			if "weight" in key:
				_lowercase :    Tuple							=							val[:dim, :]
				_lowercase :    Dict							=							val[
				    dim : dim * 2, :
				]
				_lowercase :    Optional[int]							=							val[-dim:, :]
			else:
				_lowercase :    Dict							=							val[:dim]
				_lowercase :    Optional[Any]							=							val[dim : dim * 2]
				_lowercase :    List[str]							=							val[-dim:]
		else:
			_lowercase :    int							=							val

	return orig_state_dict


def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Any							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
	_lowercase :    Any							=							Image.open(requests.get(__UpperCAmelCase     ,stream=__UpperCAmelCase			).raw			)
	return im


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase = False			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							get_yolos_config(__UpperCAmelCase			)

	# load original state_dict
	_lowercase :    List[Any]							=							torch.load(__UpperCAmelCase     ,map_location='cpu'			)['model']

	# load 🤗 model
	_lowercase :    str							=							YolosForObjectDetection(__UpperCAmelCase			)
	model.eval()
	_lowercase :    Any							=							convert_state_dict(__UpperCAmelCase     ,__UpperCAmelCase			)
	model.load_state_dict(__UpperCAmelCase			)

	# Check outputs on an image, prepared by YolosImageProcessor
	_lowercase :    Optional[int]							=							800 if yolos_name != 'yolos_ti' else 512
	_lowercase :    str							=							YolosImageProcessor(format='coco_detection'     ,size=__UpperCAmelCase			)
	_lowercase :    Optional[int]							=							image_processor(images=prepare_img()     ,return_tensors='pt'			)
	_lowercase :    str							=							model(**__UpperCAmelCase			)
	_lowercase				,      _lowercase :    int							=							outputs.logits, outputs.pred_boxes

	_lowercase				,      _lowercase :    Any							=							None, None
	if yolos_name == "yolos_ti":
		_lowercase :    Optional[int]							=							torch.tensor(
		    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]			)
		_lowercase :    Dict							=							torch.tensor(
		    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]			)
	elif yolos_name == "yolos_s_200_pre":
		_lowercase :    Optional[Any]							=							torch.tensor(
		    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]			)
		_lowercase :    Tuple							=							torch.tensor(
		    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]			)
	elif yolos_name == "yolos_s_300_pre":
		_lowercase :    Optional[Any]							=							torch.tensor(
		    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]			)
		_lowercase :    Optional[Any]							=							torch.tensor(
		    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]			)
	elif yolos_name == "yolos_s_dWr":
		_lowercase :    int							=							torch.tensor(
		    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]			)
		_lowercase :    Dict							=							torch.tensor(
		    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]			)
	elif yolos_name == "yolos_base":
		_lowercase :    Dict							=							torch.tensor(
		    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]			)
		_lowercase :    str							=							torch.tensor(
		    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]			)
	else:
		raise ValueError(F'''Unknown yolos_name: {yolos_name}'''			)

	assert torch.allclose(logits[0, :3, :3]     ,__UpperCAmelCase     ,atol=1E-4			)
	assert torch.allclose(pred_boxes[0, :3, :3]     ,__UpperCAmelCase     ,atol=1E-4			)

	Path(__UpperCAmelCase			).mkdir(exist_ok=__UpperCAmelCase			)
	print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
	model.save_pretrained(__UpperCAmelCase			)
	print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
	image_processor.save_pretrained(__UpperCAmelCase			)

	if push_to_hub:
		_lowercase :    Tuple							=							{
		    'yolos_ti': 'yolos-tiny',
		    'yolos_s_200_pre': 'yolos-small',
		    'yolos_s_300_pre': 'yolos-small-300',
		    'yolos_s_dWr': 'yolos-small-dwr',
		    'yolos_base': 'yolos-base',
		}

		print('Pushing to the hub...'			)
		_lowercase :    str							=							model_mapping[yolos_name]
		image_processor.push_to_hub(__UpperCAmelCase     ,organization='hustvl'			)
		model.push_to_hub(__UpperCAmelCase     ,organization='hustvl'			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--yolos_name',
						    default='yolos_s_200_pre',
						    type=str,
						    help=(
						        'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
						        ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
						    ),
						)
						parser.add_argument(
						    '--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""







from __future__ import annotations



class  _lowerCamelCase			:







				def __init__( self    :       Any			,					lowerCamelCase_    :       list[list[int]]						):

					"""simple docstring"""






					_lowercase :    Tuple							=							TypeError(
					    'Matrices must be formed from a list of zero or more lists containing at '
					    'least one and the same number of values, each of which must be of type '
					    'int or float.'						)
					if len(lowerCamelCase_						) != 0:
						_lowercase :    Dict							=							len(rows[0]						)
						if cols == 0:
							raise error
						for row in rows:
							if len(lowerCamelCase_						) != cols:
								raise error
							for value in row:
								if not isinstance(lowerCamelCase_			,					(int, float)						):
									raise error
						_lowercase :    int							=							rows
					else:
						_lowercase :    Optional[Any]							=							[]







				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					return [[row[i] for row in self.rows] for i in range(len(self.rows[0]						)						)]







				@property
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					return len(self.rows						)







				@property
				def 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					return len(self.rows[0]						)







				@property
				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					return (self.num_rows, self.num_columns)







				@property
				def 							__UpperCAmelCase					( self    :       List[str]						):

					"""simple docstring"""






					return self.order[0] == self.order[1]







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							[
					    [0 if column_num != row_num else 1 for column_num in range(self.num_rows						)]
					    for row_num in range(self.num_rows						)
					]
					return Matrix(lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					if not self.is_square:
						return 0
					if self.order == (0, 0):
						return 1
					if self.order == (1, 1):
						return int(self.rows[0][0]						)
					if self.order == (2, 2):
						return int(
						    (self.rows[0][0] * self.rows[1][1])
						    - (self.rows[0][1] * self.rows[1][0])						)
					else:
						return sum(
						    self.rows[0][column] * self.cofactors().rows[0][column]
						    for column in range(self.num_columns						)						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					return bool(self.determinant()						)







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					_lowercase :    List[str]							=							[
					    [
					        self.rows[other_row][other_column]
					        for other_column in range(self.num_columns						)
					        if other_column != column
					    ]
					    for other_row in range(self.num_rows						)
					    if other_row != row
					]
					return Matrix(lowerCamelCase_						).determinant()







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					if (row + column) % 2 == 0:
						return self.get_minor(lowerCamelCase_			,					lowerCamelCase_						)
					return -1 * self.get_minor(lowerCamelCase_			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return Matrix(
					    [
					        [self.get_minor(lowerCamelCase_			,					lowerCamelCase_						) for column in range(self.num_columns						)]
					        for row in range(self.num_rows						)
					    ]						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					return Matrix(
					    [
					        [
					            self.minors().rows[row][column]
					            if (row + column) % 2 == 0
					            else self.minors().rows[row][column] * -1
					            for column in range(self.minors().num_columns						)
					        ]
					        for row in range(self.minors().num_rows						)
					    ]						)







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    str							=							[
					    [self.cofactors().rows[column][row] for column in range(self.num_columns						)]
					    for row in range(self.num_rows						)
					]
					return Matrix(lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Dict							=							self.determinant()
					if not determinant:
						raise TypeError('Only matrices with a non-zero determinant have an inverse'						)
					return self.adjugate() * (1 / determinant)







				def __repr__( self    :       List[str]						):

					"""simple docstring"""






					return str(self.rows						)







				def __str__( self    :       str						):

					"""simple docstring"""






					if self.num_rows == 0:
						return "[]"
					if self.num_rows == 1:
						return "[[" + ". ".join(str(self.rows[0]						)						) + "]]"
					return (
					    "["
					    + "\n ".join(
					        [
					            '[' + '. '.join([str(lowerCamelCase_						) for value in row]						) + '.]'
					            for row in self.rows
					        ]						)
					    + "]"
					)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					lowerCamelCase_    :       list[int]			,					lowerCamelCase_    :       int | None = None						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							TypeError('Row must be a list containing all ints and/or floats'						)
					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						raise type_error
					for value in row:
						if not isinstance(lowerCamelCase_			,					(int, float)						):
							raise type_error
					if len(lowerCamelCase_						) != self.num_columns:
						raise ValueError(
						    'Row must be equal in length to the other rows in the matrix'						)
					if position is None:
						self.rows.append(lowerCamelCase_						)
					else:
						_lowercase :    Any							=							self.rows[0:position] + [row] + self.rows[position:]







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       list[int]			,					lowerCamelCase_    :       int | None = None						):

					"""simple docstring"""






					_lowercase :    Tuple							=							TypeError(
					    'Column must be a list containing all ints and/or floats'						)
					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						raise type_error
					for value in column:
						if not isinstance(lowerCamelCase_			,					(int, float)						):
							raise type_error
					if len(lowerCamelCase_						) != self.num_rows:
						raise ValueError(
						    'Column must be equal in length to the other columns in the matrix'						)
					if position is None:
						_lowercase :    List[Any]							=							[self.rows[i] + [column[i]] for i in range(self.num_rows						)]
					else:
						_lowercase :    Any							=							[
						    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
						    for i in range(self.num_rows						)
						]







				def __eq__( self    :       Union[str, Any]			,					lowerCamelCase_    :       object						):

					"""simple docstring"""






					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						return NotImplemented
					return self.rows == other.rows







				def __ne__( self    :       Any			,					lowerCamelCase_    :       object						):

					"""simple docstring"""






					return not self == other







				def __neg__( self    :       int						):

					"""simple docstring"""






					return self * -1







				def __add__( self    :       Optional[int]			,					lowerCamelCase_    :       Matrix						):

					"""simple docstring"""






					if self.order != other.order:
						raise ValueError('Addition requires matrices of the same order'						)
					return Matrix(
					    [
					        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns						)]
					        for i in range(self.num_rows						)
					    ]						)







				def __sub__( self    :       str			,					lowerCamelCase_    :       Matrix						):

					"""simple docstring"""






					if self.order != other.order:
						raise ValueError('Subtraction requires matrices of the same order'						)
					return Matrix(
					    [
					        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns						)]
					        for i in range(self.num_rows						)
					    ]						)







				def __mul__( self    :       Optional[Any]			,					lowerCamelCase_    :       Matrix | int | float						):

					"""simple docstring"""






					if isinstance(lowerCamelCase_			,					(int, float)						):
						return Matrix(
						    [[int(element * other						) for element in row] for row in self.rows]						)
					elif isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						if self.num_columns != other.num_rows:
							raise ValueError(
							    'The number of columns in the first matrix must '
							    'be equal to the number of rows in the second'						)
						return Matrix(
						    [
						        [Matrix.dot_product(lowerCamelCase_			,					lowerCamelCase_						) for column in other.columns()]
						        for row in self.rows
						    ]						)
					else:
						raise TypeError(
						    'A Matrix can only be multiplied by an int, float, or another matrix'						)







				def __pow__( self    :       Optional[Any]			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					if not isinstance(lowerCamelCase_			,					lowerCamelCase_						):
						raise TypeError('A Matrix can only be raised to the power of an int'						)
					if not self.is_square:
						raise ValueError('Only square matrices can be raised to a power'						)
					if other == 0:
						return self.identity()
					if other < 0:
						if self.is_invertable():
							return self.inverse() ** (-other)
						raise ValueError(
						    'Only invertable matrices can be raised to a negative power'						)
					_lowercase :    str							=							self
					for _ in range(other - 1						):
						result *= self
					return result







				@classmethod
				def 							__UpperCAmelCase					( cls    :       str			,					lowerCamelCase_    :       list[int]			,					lowerCamelCase_    :       list[int]						):

					"""simple docstring"""






					return sum(row[i] * column[i] for i in range(len(lowerCamelCase_						)						)						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



