


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DetrImageProcessor


class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       Any				,					_snake_case					:       Union[str, Any]=7				,					_snake_case					:       List[Any]=3				,					_snake_case					:       Any=30				,					_snake_case					:       Tuple=4_00				,					_snake_case					:       str=True				,					_snake_case					:       List[str]=None				,					_snake_case					:       Any=True				,					_snake_case					:       List[str]=1 / 2_55				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       str=[0.5, 0.5, 0.5]				,					_snake_case					:       int=[0.5, 0.5, 0.5]				,					_snake_case					:       Optional[Any]=True				,					):






								"""simple docstring"""


								A__       =    size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
								A__       =    parent
								A__       =    batch_size
								A__       =    num_channels
								A__       =    min_resolution
								A__       =    max_resolution
								A__       =    do_resize
								A__       =    size
								A__       =    do_rescale
								A__       =    rescale_factor
								A__       =    do_normalize
								A__       =    image_mean
								A__       =    image_std
								A__       =    do_pad



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_pad": self.do_pad,
								}



					def     _a							(     self					:       Tuple				,					_snake_case					:       Optional[Any]				,					_snake_case					:       List[Any]=False					):






								"""simple docstring"""


								if not batched:
											A__       =    image_inputs[0]
											if isinstance(_snake_case				,					Image.Image					):
														A__       =    image.size
											else:
														A__       =    image.shape[1], image.shape[2]
											if w < h:
														A__       =    int(self.size['shortest_edge'] * h / w					)
														A__       =    self.size["shortest_edge"]
											elif w > h:
														A__       =    self.size["shortest_edge"]
														A__       =    int(self.size['shortest_edge'] * w / h					)
											else:
														A__       =    self.size["shortest_edge"]
														A__       =    self.size["shortest_edge"]

								else:
											A__       =    []
											for image in image_inputs:
														A__       =    self.get_expected_values([image]					)
														expected_values.append((expected_height, expected_width)					)
											A__       =    max(_snake_case				,					key=lambda _snake_case					: item[0]					)[0]
											A__       =    max(_snake_case				,					key=lambda _snake_case					: item[1]					)[1]

								return expected_height, expected_width







@require_torch
@require_vision
class      __lowerCAmelCase     (		_UpperCamelCase							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    DetrImageProcessor if is_vision_available() else None



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    DetrImageProcessingTester(self					)



					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(_snake_case				,					'image_mean'					)					)
								self.assertTrue(hasattr(_snake_case				,					'image_std'					)					)
								self.assertTrue(hasattr(_snake_case				,					'do_normalize'					)					)
								self.assertTrue(hasattr(_snake_case				,					'do_rescale'					)					)
								self.assertTrue(hasattr(_snake_case				,					'rescale_factor'					)					)
								self.assertTrue(hasattr(_snake_case				,					'do_resize'					)					)
								self.assertTrue(hasattr(_snake_case				,					'size'					)					)
								self.assertTrue(hasattr(_snake_case				,					'do_pad'					)					)



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






								"""simple docstring"""


								A__       =    self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size				,					{'shortest_edge': 18, 'longest_edge': 13_33}					)
								self.assertEqual(image_processor.do_pad				,					_snake_case					)

								A__       =    self.image_processing_class.from_dict(
								    self.image_processor_dict				,					size=42				,					max_size=84				,					pad_and_return_pixel_mask=_snake_case					)
								self.assertEqual(image_processor.size				,					{'shortest_edge': 42, 'longest_edge': 84}					)
								self.assertEqual(image_processor.do_pad				,					_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								A__       =    prepare_image_inputs(self.image_processor_tester				,					equal_resolution=_snake_case					)
								for image in image_inputs:
											self.assertIsInstance(_snake_case				,					Image.Image					)

								# Test not batched input
								A__       =    image_processing(image_inputs[0]				,					return_tensors='pt'					).pixel_values

								A__       =    self.image_processor_tester.get_expected_values(_snake_case					)

								self.assertEqual(
								    encoded_images.shape				,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,					)

								# Test batched
								A__       =    self.image_processor_tester.get_expected_values(_snake_case				,					batched=_snake_case					)

								A__       =    image_processing(_snake_case				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								A__       =    prepare_image_inputs(self.image_processor_tester				,					equal_resolution=_snake_case				,					numpify=_snake_case					)
								for image in image_inputs:
											self.assertIsInstance(_snake_case				,					np.ndarray					)

								# Test not batched input
								A__       =    image_processing(image_inputs[0]				,					return_tensors='pt'					).pixel_values

								A__       =    self.image_processor_tester.get_expected_values(_snake_case					)

								self.assertEqual(
								    encoded_images.shape				,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,					)

								# Test batched
								A__       =    image_processing(_snake_case				,					return_tensors='pt'					).pixel_values

								A__       =    self.image_processor_tester.get_expected_values(_snake_case				,					batched=_snake_case					)

								self.assertEqual(
								    encoded_images.shape				,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								A__       =    prepare_image_inputs(self.image_processor_tester				,					equal_resolution=_snake_case				,					torchify=_snake_case					)
								for image in image_inputs:
											self.assertIsInstance(_snake_case				,					torch.Tensor					)

								# Test not batched input
								A__       =    image_processing(image_inputs[0]				,					return_tensors='pt'					).pixel_values

								A__       =    self.image_processor_tester.get_expected_values(_snake_case					)

								self.assertEqual(
								    encoded_images.shape				,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,					)

								# Test batched
								A__       =    image_processing(_snake_case				,					return_tensors='pt'					).pixel_values

								A__       =    self.image_processor_tester.get_expected_values(_snake_case				,					batched=_snake_case					)

								self.assertEqual(
								    encoded_images.shape				,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,					)



					@slow
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
								with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,					'r'					) as f:
											A__       =    json.loads(f.read()					)

								A__       =    {"image_id": 3_97_69, "annotations": target}

								# encode them
								A__       =    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'					)
								A__       =    image_processing(images=_snake_case				,					annotations=_snake_case				,					return_tensors='pt'					)

								# verify pixel values
								A__       =    torch.Size([1, 3, 8_00, 10_66]					)
								self.assertEqual(encoding['pixel_values'].shape				,					_snake_case					)

								A__       =    torch.tensor([0.2796, 0.3138, 0.3481]					)
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,					_snake_case				,					atol=1E-4					)					)

								# verify area
								A__       =    torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,					_snake_case					)					)
								# verify boxes
								A__       =    torch.Size([6, 4]					)
								self.assertEqual(encoding['labels'][0]['boxes'].shape				,					_snake_case					)
								A__       =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,					_snake_case				,					atol=1E-3					)					)
								# verify image_id
								A__       =    torch.tensor([3_97_69]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,					_snake_case					)					)
								# verify is_crowd
								A__       =    torch.tensor([0, 0, 0, 0, 0, 0]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,					_snake_case					)					)
								# verify class_labels
								A__       =    torch.tensor([75, 75, 63, 65, 17, 17]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,					_snake_case					)					)
								# verify orig_size
								A__       =    torch.tensor([4_80, 6_40]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,					_snake_case					)					)
								# verify size
								A__       =    torch.tensor([8_00, 10_66]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,					_snake_case					)					)



					@slow
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
								with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,					'r'					) as f:
											A__       =    json.loads(f.read()					)

								A__       =    {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

								A__       =    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'					)

								# encode them
								A__       =    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'					)
								A__       =    image_processing(images=_snake_case				,					annotations=_snake_case				,					masks_path=_snake_case				,					return_tensors='pt'					)

								# verify pixel values
								A__       =    torch.Size([1, 3, 8_00, 10_66]					)
								self.assertEqual(encoding['pixel_values'].shape				,					_snake_case					)

								A__       =    torch.tensor([0.2796, 0.3138, 0.3481]					)
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]				,					_snake_case				,					atol=1E-4					)					)

								# verify area
								A__       =    torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,					_snake_case					)					)
								# verify boxes
								A__       =    torch.Size([6, 4]					)
								self.assertEqual(encoding['labels'][0]['boxes'].shape				,					_snake_case					)
								A__       =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,					_snake_case				,					atol=1E-3					)					)
								# verify image_id
								A__       =    torch.tensor([3_97_69]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,					_snake_case					)					)
								# verify is_crowd
								A__       =    torch.tensor([0, 0, 0, 0, 0, 0]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,					_snake_case					)					)
								# verify class_labels
								A__       =    torch.tensor([17, 17, 63, 75, 75, 93]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,					_snake_case					)					)
								# verify masks
								A__       =    82_28_73
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,					_snake_case					)
								# verify orig_size
								A__       =    torch.tensor([4_80, 6_40]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,					_snake_case					)					)
								# verify size
								A__       =    torch.tensor([8_00, 10_66]					)
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,					_snake_case					)					)









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 A						(       __UpperCamelCase					)						->      YolosConfig:
			A__       =    YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						A__       =    192
						A__       =    768
						A__       =    12
						A__       =    3
						A__       =    [800, 1_333]
						A__       =    False
			elif yolos_name == "yolos_s_dWr":
						A__       =    330
						A__       =    14
						A__       =    6
						A__       =    1_320
			elif "yolos_s" in yolos_name:
						A__       =    384
						A__       =    1_536
						A__       =    12
						A__       =    6
			elif "yolos_b" in yolos_name:
						A__       =    [800, 1_344]

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

			return config


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


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

			return name


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      dict:
			for key in orig_state_dict.copy().keys():
						A__       =    orig_state_dict.pop(__UpperCamelCase					)

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

			return orig_state_dict


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


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      List[str]:
			A__       =    get_yolos_config(__UpperCamelCase					)

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

			# load 🤗 model
			A__       =    YolosForObjectDetection(__UpperCamelCase					)
			model.eval()
			A__       =    convert_state_dict(__UpperCamelCase ,	__UpperCamelCase					)
			model.load_state_dict(__UpperCamelCase					)

			# Check outputs on an image, prepared by YolosImageProcessor
			A__       =    800 if yolos_name != 'yolos_ti' else 512
			A__       =    YolosImageProcessor(format='coco_detection' ,	size=__UpperCamelCase					)
			A__       =    image_processor(images=prepare_img() ,	return_tensors='pt'					)
			A__       =    model(**__UpperCamelCase					)
			A__     , A__       =    outputs.logits, outputs.pred_boxes

			A__     , A__       =    None, None
			if yolos_name == "yolos_ti":
						A__       =    torch.tensor(
						    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
						A__       =    torch.tensor(
						    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]					)
			elif yolos_name == "yolos_s_200_pre":
						A__       =    torch.tensor(
						    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
						A__       =    torch.tensor(
						    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					)
			elif yolos_name == "yolos_s_300_pre":
						A__       =    torch.tensor(
						    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
						A__       =    torch.tensor(
						    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]					)
			elif yolos_name == "yolos_s_dWr":
						A__       =    torch.tensor(
						    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
						A__       =    torch.tensor(
						    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]					)
			elif yolos_name == "yolos_base":
						A__       =    torch.tensor(
						    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
						A__       =    torch.tensor(
						    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]					)
			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:
						A__       =    {
						    '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...'					)
						A__       =    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)




