


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()
snake_case_						:			List[Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								_SCREAMING_SNAKE_CASE			  =192
								_SCREAMING_SNAKE_CASE			  =768
								_SCREAMING_SNAKE_CASE			  =12
								_SCREAMING_SNAKE_CASE			  =3
								_SCREAMING_SNAKE_CASE			  =[800, 1333]
								_SCREAMING_SNAKE_CASE			  =False
				elif yolos_name == "yolos_s_dWr":
								_SCREAMING_SNAKE_CASE			  =330
								_SCREAMING_SNAKE_CASE			  =14
								_SCREAMING_SNAKE_CASE			  =6
								_SCREAMING_SNAKE_CASE			  =1320
				elif "yolos_s" in yolos_name:
								_SCREAMING_SNAKE_CASE			  =384
								_SCREAMING_SNAKE_CASE			  =1536
								_SCREAMING_SNAKE_CASE			  =12
								_SCREAMING_SNAKE_CASE			  =6
				elif "yolos_b" in yolos_name:
								_SCREAMING_SNAKE_CASE			  =[800, 1344]

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

				return config







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







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

				return name







def 						lowerCamelCase(   a__	,a__):
				for key in orig_state_dict.copy().keys():
								_SCREAMING_SNAKE_CASE			  =orig_state_dict.pop(a__)

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

				return orig_state_dict







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)
				return im







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__	,a__	,a__ = False):
				_SCREAMING_SNAKE_CASE			  =get_yolos_config(a__)

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

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

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

				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =None, None
				if yolos_name == "yolos_ti":
								_SCREAMING_SNAKE_CASE			  =torch.tensor(
								    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]])
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]])
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =torch.tensor(
								    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]])
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =torch.tensor(
								    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]])
								_SCREAMING_SNAKE_CASE			  =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":
								_SCREAMING_SNAKE_CASE			  =torch.tensor(
								    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]])
								_SCREAMING_SNAKE_CASE			  =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]	,a__	,atol=1e-4)
				assert torch.allclose(pred_boxes[0, :3, :3]	,a__	,atol=1e-4)

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

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


if __name__ == "__main__":
							snake_case_						:			str													=							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.'''
							)

							snake_case_						:			List[str]													=							parser.parse_args()
							convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_						:			int													=							logging.get_logger(__name__)

snake_case_						:			Optional[Any]													=							{
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"lilt"




						def __init__(       self					:      int				,    _a					:      Dict=3_0522				,    _a					:      str=768				,    _a					:      Optional[int]=12				,    _a					:      str=12				,    _a					:      List[str]=3072				,    _a					:      Any="gelu"				,    _a					:      int=0.1				,    _a					:      Any=0.1				,    _a					:      Union[str, Any]=512				,    _a					:      str=2				,    _a					:      List[Any]=0.02				,    _a					:      str=1E-12				,    _a					:      str=0				,    _a					:      str="absolute"				,    _a					:      Any=None				,    _a					:      Dict=4				,    _a					:      int=1024				,    **_a					:      List[Any]				,    )							->     Any:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =classifier_dropout
										_SCREAMING_SNAKE_CASE			  =channel_shrink_ratio
										_SCREAMING_SNAKE_CASE			  =max_ad_position_embeddings