


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




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__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(snake_case__  ,						snake_case__  ,						repo_type="dataset"   )  ,						"r"   )   )
					__SCREAMING_SNAKE_CASE						   =				{int(snake_case__   ): 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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = 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)
										__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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										__SCREAMING_SNAKE_CASE						   =				orig_state_dict.pop(snake_case__   )

										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    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					__SCREAMING_SNAKE_CASE						   =				Image.open(requests.get(snake_case__  ,						stream=snake_case__   ).raw   )
					return im




@torch.no_grad()
def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				get_yolos_config(snake_case__   )

					# load original state_dict
					__SCREAMING_SNAKE_CASE						   =				torch.load(snake_case__  ,						map_location="cpu"   )["""model"""]

					# load 🤗 model
					__SCREAMING_SNAKE_CASE						   =				YolosForObjectDetection(snake_case__   )
					model.eval()
					__SCREAMING_SNAKE_CASE						   =				convert_state_dict(snake_case__  ,						snake_case__   )
					model.load_state_dict(snake_case__   )

					# 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=snake_case__   )
					__SCREAMING_SNAKE_CASE						   =				image_processor(images=prepare_img()  ,						return_tensors="pt"   )
					__SCREAMING_SNAKE_CASE						   =				model(**snake_case__   )
					__SCREAMING_SNAKE_CASE						   =				outputs.logits, outputs.pred_boxes

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

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

					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(snake_case__  ,						organization="hustvl"   )
										model.push_to_hub(snake_case__  ,						organization="hustvl"   )


if __name__ == "__main__":
		a__				:							Any						=     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.'''
		)

		a__				:							int						=     parser.parse_args()
		convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""


from ..utils import DummyObject, requires_backends






class 					lowercase(     metaclass=__a ):







 '''simple docstring'''





 lowercase__ 					=					["note_seq"]


 def __init__(				self:		Dict,      *a_:		Union[str, Any],      **a_:		List[str]		):




   '''simple docstring'''



   requires_backends(self,      ["""note_seq"""]		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Optional[int],      *a_:		Any,      **a_:		Optional[Any]		):




   '''simple docstring'''



   requires_backends(cls,      ["""note_seq"""]		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple,      *a_:		Optional[Any],      **a_:		List[str]		):




   '''simple docstring'''



   requires_backends(cls,      ["""note_seq"""]		)


