





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()
lowerCAmelCase_    =				logging.get_logger(__name__)






def      lowerCamelCase_						(				_UpperCamelCase )		->						YolosConfig:






				"""simple docstring"""


				snake_case_						:    int				=  YolosConfig()

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

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

				return config






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = False )		->						List[Any]:






				"""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)
								snake_case_						:    Any				=  state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
								snake_case_						:    Dict				=  state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
								# next, add query, keys and values (in that order) to the state dict
								snake_case_						:    List[Any]				=  in_proj_weight[: config.hidden_size, :]
								snake_case_						:    Optional[int]				=  in_proj_bias[: config.hidden_size]
								snake_case_						:    List[Any]				=  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case_						:    Tuple				=  in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								snake_case_						:    str				=  in_proj_weight[-config.hidden_size :, :]
								snake_case_						:    Optional[Any]				=  in_proj_bias[-config.hidden_size :]






def      lowerCamelCase_						(				_UpperCamelCase )		->						str:






				"""simple docstring"""


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

				return name






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						dict:






				"""simple docstring"""


				for key in orig_state_dict.copy().keys():
								snake_case_						:    List[str]				=  orig_state_dict.pop(_UpperCamelCase )

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

				return orig_state_dict






def      lowerCamelCase_						(				)		->						torch.Tensor:






				"""simple docstring"""


				snake_case_						:    Optional[Any]				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:    int				=  Image.open(requests.get(_UpperCamelCase ,				stream=_UpperCamelCase ).raw )
				return im






@torch.no_grad()
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = False )		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    Optional[int]				=  get_yolos_config(_UpperCamelCase )

				# load original state_dict
				snake_case_						:    Union[str, Any]				=  torch.load(_UpperCamelCase ,				map_location='''cpu''' )['''model''']

				# load 🤗 model
				snake_case_						:    int				=  YolosForObjectDetection(_UpperCamelCase )
				model.eval()
				snake_case_						:    str				=  convert_state_dict(_UpperCamelCase ,				_UpperCamelCase )
				model.load_state_dict(_UpperCamelCase )

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

				snake_case_			,							snake_case_						:    List[Any]				=  None, None
				if yolos_name == "yolos_ti":
								snake_case_						:    Optional[int]				=  torch.tensor(
								    [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]] )
								snake_case_						:    Union[str, Any]				=  torch.tensor(
								    [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]] )
				elif yolos_name == "yolos_s_200_pre":
								snake_case_						:    str				=  torch.tensor(
								    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] )
								snake_case_						:    int				=  torch.tensor(
								    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] )
				elif yolos_name == "yolos_s_300_pre":
								snake_case_						:    str				=  torch.tensor(
								    [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]] )
								snake_case_						:    int				=  torch.tensor(
								    [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]] )
				elif yolos_name == "yolos_s_dWr":
								snake_case_						:    List[str]				=  torch.tensor(
								    [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]] )
								snake_case_						:    Optional[int]				=  torch.tensor(
								    [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]] )
				elif yolos_name == "yolos_base":
								snake_case_						:    Optional[int]				=  torch.tensor(
								    [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]] )
								snake_case_						:    Optional[Any]				=  torch.tensor(
								    [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]] )
				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:
								snake_case_						:    Optional[Any]				=  {
								    '''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...''' )
								snake_case_						:    List[str]				=  model_mapping[yolos_name]
								image_processor.push_to_hub(_UpperCamelCase ,				organization='''hustvl''' )
								model.push_to_hub(_UpperCamelCase ,				organization='''hustvl''' )


if __name__ == "__main__":
	lowerCAmelCase_    =				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.'''
	)

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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
