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







def 							A__       (    lowerCamelCase    )	->						str:
				UpperCamelCase_:				int						=    YolosConfig()

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

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

				return config







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







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

				return name







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				for key in orig_state_dict.copy().keys():
								UpperCamelCase_:				int						=    orig_state_dict.pop(__snake_case    )

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

				return orig_state_dict







def 							A__       (    )	->						List[Any]:
				UpperCamelCase_:				Tuple						=    """http://images.cocodataset.org/val2017/000000039769.jpg"""
				UpperCamelCase_:				int						=    Image.open(requests.get(__snake_case  ,     stream=__snake_case    ).raw    )
				return im







@torch.no_grad()
def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase = False    )	->						str:
				UpperCamelCase_:				str						=    get_yolos_config(__snake_case    )

				# load original state_dict
				UpperCamelCase_:				int						=    torch.load(__snake_case  ,     map_location="""cpu"""    )["""model"""]

				# load 🤗 model
				UpperCamelCase_:				Union[str, Any]						=    YolosForObjectDetection(__snake_case    )
				model.eval()
				UpperCamelCase_:				Optional[int]						=    convert_state_dict(__snake_case  ,     __snake_case    )
				model.load_state_dict(__snake_case    )

				# Check outputs on an image, prepared by YolosImageProcessor
				UpperCamelCase_:				List[Any]						=    8_00 if yolos_name != """yolos_ti""" else 5_12
				UpperCamelCase_:				str						=    YolosImageProcessor(format="""coco_detection"""  ,     size=__snake_case    )
				UpperCamelCase_:				Optional[int]						=    image_processor(images=prepare_img()  ,     return_tensors="""pt"""    )
				UpperCamelCase_:				Optional[int]						=    model(**__snake_case    )
				UpperCamelCase_, UpperCamelCase_:				Tuple						=    outputs.logits, outputs.pred_boxes

				UpperCamelCase_, UpperCamelCase_:				List[Any]						=    None, None
				if yolos_name == "yolos_ti":
								UpperCamelCase_:				Dict						=    torch.tensor(
								    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]    )
								UpperCamelCase_:				int						=    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":
								UpperCamelCase_:				List[str]						=    torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]    )
								UpperCamelCase_:				Optional[Any]						=    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":
								UpperCamelCase_:				List[Any]						=    torch.tensor(
								    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]    )
								UpperCamelCase_:				Union[str, Any]						=    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":
								UpperCamelCase_:				Any						=    torch.tensor(
								    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]    )
								UpperCamelCase_:				int						=    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":
								UpperCamelCase_:				List[Any]						=    torch.tensor(
								    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]    )
								UpperCamelCase_:				int						=    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:
								UpperCamelCase_:				Optional[int]						=    {
								    """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..."""    )
								UpperCamelCase_:				Any						=    model_mapping[yolos_name]
								image_processor.push_to_hub(__snake_case  ,     organization="""hustvl"""    )
								model.push_to_hub(__snake_case  ,     organization="""hustvl"""    )


if __name__ == "__main__":
							lowerCamelCase_    :			Union[str, 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."""
							)

							lowerCamelCase_    :			Tuple    =	parser.parse_args()
							convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)














lowerCamelCase      :       List[Any]    =						{
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
lowerCamelCase      :       Union[str, Any]    =						{
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       from_type.lower().strip("s"							)
     __lowerCAmelCase             =       to_type.lower().strip("s"							)

     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)

     if from_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'from_type' value: {from_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     if to_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'to_type' value: {to_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     __lowerCAmelCase             =       METRIC_CONVERSION[from_sanitized]
     __lowerCAmelCase             =       METRIC_CONVERSION[to_sanitized]
     __lowerCAmelCase             =       1

     if from_exponent > to_exponent:
          __lowerCAmelCase             =       from_exponent - to_exponent
     else:
          __lowerCAmelCase             =       -(to_exponent - from_exponent)

     return value * pow(10       ,			__snake_case							)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






