



"""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  :						Optional[int]    							=      logging.get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       YolosConfig:
				_lowerCamelCase        =				YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								_lowerCamelCase        =				1_92
								_lowerCamelCase        =				7_68
								_lowerCamelCase        =				12
								_lowerCamelCase        =				3
								_lowerCamelCase        =				[8_00, 13_33]
								_lowerCamelCase        =				False
				elif yolos_name == "yolos_s_dWr":
								_lowerCamelCase        =				3_30
								_lowerCamelCase        =				14
								_lowerCamelCase        =				6
								_lowerCamelCase        =				13_20
				elif "yolos_s" in yolos_name:
								_lowerCamelCase        =				3_84
								_lowerCamelCase        =				15_36
								_lowerCamelCase        =				12
								_lowerCamelCase        =				6
				elif "yolos_b" in yolos_name:
								_lowerCamelCase        =				[8_00, 13_44]

				_lowerCamelCase        =				91
				_lowerCamelCase        =				'''huggingface/label-files'''
				_lowerCamelCase        =				'''coco-detection-id2label.json'''
				_lowerCamelCase        =				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   repo_type='''dataset'''							)						,   '''r'''							)							)
				_lowerCamelCase        =				{int(_SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
				_lowerCamelCase        =				idalabel
				_lowerCamelCase        =				{v: k for k, v in idalabel.items()}

				return config



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



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       str:
				if "backbone" in name:
								_lowerCamelCase        =				name.replace('''backbone'''						,   '''vit'''							)
				if "cls_token" in name:
								_lowerCamelCase        =				name.replace('''cls_token'''						,   '''embeddings.cls_token'''							)
				if "det_token" in name:
								_lowerCamelCase        =				name.replace('''det_token'''						,   '''embeddings.detection_tokens'''							)
				if "mid_pos_embed" in name:
								_lowerCamelCase        =				name.replace('''mid_pos_embed'''						,   '''encoder.mid_position_embeddings'''							)
				if "pos_embed" in name:
								_lowerCamelCase        =				name.replace('''pos_embed'''						,   '''embeddings.position_embeddings'''							)
				if "patch_embed.proj" in name:
								_lowerCamelCase        =				name.replace('''patch_embed.proj'''						,   '''embeddings.patch_embeddings.projection'''							)
				if "blocks" in name:
								_lowerCamelCase        =				name.replace('''blocks'''						,   '''encoder.layer'''							)
				if "attn.proj" in name:
								_lowerCamelCase        =				name.replace('''attn.proj'''						,   '''attention.output.dense'''							)
				if "attn" in name:
								_lowerCamelCase        =				name.replace('''attn'''						,   '''attention.self'''							)
				if "norm1" in name:
								_lowerCamelCase        =				name.replace('''norm1'''						,   '''layernorm_before'''							)
				if "norm2" in name:
								_lowerCamelCase        =				name.replace('''norm2'''						,   '''layernorm_after'''							)
				if "mlp.fc1" in name:
								_lowerCamelCase        =				name.replace('''mlp.fc1'''						,   '''intermediate.dense'''							)
				if "mlp.fc2" in name:
								_lowerCamelCase        =				name.replace('''mlp.fc2'''						,   '''output.dense'''							)
				if "class_embed" in name:
								_lowerCamelCase        =				name.replace('''class_embed'''						,   '''class_labels_classifier'''							)
				if "bbox_embed" in name:
								_lowerCamelCase        =				name.replace('''bbox_embed'''						,   '''bbox_predictor'''							)
				if "vit.norm" in name:
								_lowerCamelCase        =				name.replace('''vit.norm'''						,   '''vit.layernorm'''							)

				return name



def        lowerCAmelCase_(   lowercase_     :							Any						,   lowercase_     :							str							)	->       dict:
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase        =				orig_state_dict.pop(_SCREAMING_SNAKE_CASE							)

								if "qkv" in key:
												_lowerCamelCase        =				key.split('''.'''							)
												_lowerCamelCase        =				int(key_split[2]							)
												_lowerCamelCase        =				model.vit.encoder.layer[layer_num].attention.attention.all_head_size
												if "weight" in key:
																_lowerCamelCase        =				val[:dim, :]
																_lowerCamelCase        =				val[
																    dim : dim * 2, :
																]
																_lowerCamelCase        =				val[-dim:, :]
												else:
																_lowerCamelCase        =				val[:dim]
																_lowerCamelCase        =				val[dim : dim * 2]
																_lowerCamelCase        =				val[-dim:]
								else:
												_lowerCamelCase        =				val

				return orig_state_dict



def        lowerCAmelCase_(   )	->       torch.Tensor:
				_lowerCamelCase        =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_lowerCamelCase        =				Image.open(requests.get(_SCREAMING_SNAKE_CASE						,   stream=_SCREAMING_SNAKE_CASE							).raw							)
				return im



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Any						,   lowercase_     :							int						,   lowercase_     :							int = False							)	->       Any:
				_lowerCamelCase        =				get_yolos_config(_SCREAMING_SNAKE_CASE							)

				# load original state_dict
				_lowerCamelCase        =				torch.load(_SCREAMING_SNAKE_CASE						,   map_location='''cpu'''							)['''model''']

				# load 🤗 model
				_lowerCamelCase        =				YolosForObjectDetection(_SCREAMING_SNAKE_CASE							)
				model.eval()
				_lowerCamelCase        =				convert_state_dict(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE							)
				model.load_state_dict(_SCREAMING_SNAKE_CASE							)

				# Check outputs on an image, prepared by YolosImageProcessor
				_lowerCamelCase        =				8_00 if yolos_name != '''yolos_ti''' else 5_12
				_lowerCamelCase        =				YolosImageProcessor(format='''coco_detection'''						,   size=_SCREAMING_SNAKE_CASE							)
				_lowerCamelCase        =				image_processor(images=prepare_img()						,   return_tensors='''pt'''							)
				_lowerCamelCase        =				model(**_SCREAMING_SNAKE_CASE							)
				_lowerCamelCase  ,     _lowerCamelCase        =				outputs.logits, outputs.pred_boxes

				_lowerCamelCase  ,     _lowerCamelCase        =				None, None
				if yolos_name == "yolos_ti":
								_lowerCamelCase        =				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]]							)
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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]]							)
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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]]							)
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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]]							)
								_lowerCamelCase        =				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":
								_lowerCamelCase        =				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]]							)
								_lowerCamelCase        =				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]						,   _SCREAMING_SNAKE_CASE						,   atol=1e-4							)
				assert torch.allclose(pred_boxes[0, :3, :3]						,   _SCREAMING_SNAKE_CASE						,   atol=1e-4							)

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

				if push_to_hub:
								_lowerCamelCase        =				{
								    '''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...'''							)
								_lowerCamelCase        =				model_mapping[yolos_name]
								image_processor.push_to_hub(_SCREAMING_SNAKE_CASE						,   organization='''hustvl'''							)
								model.push_to_hub(_SCREAMING_SNAKE_CASE						,   organization='''hustvl'''							)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE  :						Tuple    							=      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  :						List[Any]    							=      parser.parse_args()
					convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def        lowerCAmelCase_(   lowercase_     :							List[Any]							)	->       Optional[Any]:
				from diffusers.utils.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowercase_							)




def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       List[str]:
				from diffusers.utils.testing_utils import pytest_terminal_summary_main

				_lowerCamelCase        =				terminalreporter.config.getoption('''--make-reports'''							)
				if make_reports:
								pytest_terminal_summary_main(lowercase_						,   id=lowercase_							)
