






"""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()
lowercase__          = logging.get_logger(__name__)

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    YolosConfig:

		"""simple docstring"""







		lowerCAmelCase_   :							Any									=			YolosConfig()

		# size of the architecture
		if "yolos_ti" in yolos_name:
				lowerCAmelCase_   :							List[str]									=			192
				lowerCAmelCase_   :							int									=			768
				lowerCAmelCase_   :							Dict									=			12
				lowerCAmelCase_   :							Any									=			3
				lowerCAmelCase_   :							List[Any]									=			[800, 1333]
				lowerCAmelCase_   :							List[str]									=			False
		elif yolos_name == "yolos_s_dWr":
				lowerCAmelCase_   :							str									=			330
				lowerCAmelCase_   :							Optional[int]									=			14
				lowerCAmelCase_   :							Dict									=			6
				lowerCAmelCase_   :							Union[str, Any]									=			1320
		elif "yolos_s" in yolos_name:
				lowerCAmelCase_   :							Any									=			384
				lowerCAmelCase_   :							List[Any]									=			1536
				lowerCAmelCase_   :							Any									=			12
				lowerCAmelCase_   :							List[Any]									=			6
		elif "yolos_b" in yolos_name:
				lowerCAmelCase_   :							Dict									=			[800, 1344]

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

		return config

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase = False       )						->    Tuple:

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

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    str:

		"""simple docstring"""







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

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

		return orig_state_dict

def 		__lowerCamelCase	(  )						->    torch.Tensor:

		"""simple docstring"""







		lowerCAmelCase_   :							List[str]									=			"http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase_   :							str									=			Image.open(requests.get(A_      ,       stream=A_       ).raw       )
		return im

@torch.no_grad()
def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase = False       )						->    Any:

		"""simple docstring"""







		lowerCAmelCase_   :							str									=			get_yolos_config(A_       )

		# load original state_dict
		lowerCAmelCase_   :							List[str]									=			torch.load(A_      ,       map_location="cpu"       )["model"]

		# load 🤗 model
		lowerCAmelCase_   :							List[str]									=			YolosForObjectDetection(A_       )
		model.eval()
		lowerCAmelCase_   :							Tuple									=			convert_state_dict(A_      ,       A_       )
		model.load_state_dict(A_       )

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

		lowerCAmelCase_   :							Dict									=			None, None
		if yolos_name == "yolos_ti":
				lowerCAmelCase_   :							str									=			torch.tensor(
				    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.97_69, -17.7691], [-42.3281, -20.7200, -30.6294]]       )
				lowerCAmelCase_   :							int									=			torch.tensor(
				    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]       )
		elif yolos_name == "yolos_s_200_pre":
				lowerCAmelCase_   :							Any									=			torch.tensor(
				    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]       )
				lowerCAmelCase_   :							str									=			torch.tensor(
				    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]       )
		elif yolos_name == "yolos_s_300_pre":
				lowerCAmelCase_   :							Optional[Any]									=			torch.tensor(
				    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]       )
				lowerCAmelCase_   :							Optional[int]									=			torch.tensor(
				    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]       )
		elif yolos_name == "yolos_s_dWr":
				lowerCAmelCase_   :							List[str]									=			torch.tensor(
				    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]       )
				lowerCAmelCase_   :							List[Any]									=			torch.tensor(
				    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]       )
		elif yolos_name == "yolos_base":
				lowerCAmelCase_   :							Any									=			torch.tensor(
				    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]       )
				lowerCAmelCase_   :							Union[str, Any]									=			torch.tensor(
				    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]       )
		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:
				lowerCAmelCase_   :							Tuple									=			{
				    "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..."       )
				lowerCAmelCase_   :							Any									=			model_mapping[yolos_name]
				image_processor.push_to_hub(A_      ,       organization="hustvl"       )
				model.push_to_hub(A_      ,       organization="hustvl"       )


if __name__ == "__main__":
					lowercase__          = 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."""
					)

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




"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    int = 0):
      a						:		Tuple      							=			key






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		Dict      							=			key or self.__key or 1

      # make sure key is an appropriate size
      key %= 255

      return [chr(ord(__UpperCAmelCase) ^ key) for ch in content]






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		Optional[Any]      							=			key or self.__key or 1

      # make sure key is an appropriate size
      key %= 255

      return [chr(ord(__UpperCAmelCase) ^ key) for ch in content]






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int = 0):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		List[Any]      							=			key or self.__key or 1

      # make sure key can be any size
      while key > 255:
        key -= 255

      # This will be returned
      a						:		Any      							=			""

      for ch in content:
        ans += chr(ord(__UpperCAmelCase) ^ key)

      return ans






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int = 0):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		Dict      							=			key or self.__key or 1

      # make sure key can be any size
      while key > 255:
        key -= 255

      # This will be returned
      a						:		str      							=			""

      for ch in content:
        ans += chr(ord(__UpperCAmelCase) ^ key)

      return ans






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int = 0):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      try:
        with open(__UpperCAmelCase) as fin, open("encrypt.out"				, "w+") as fout:
          # actual encrypt-process
          for line in fin:
            fout.write(self.encrypt_string(__UpperCAmelCase				, __UpperCAmelCase))

      except OSError:
        return False

      return True






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      try:
        with open(__UpperCAmelCase) as fin, open("decrypt.out"				, "w+") as fout:
          # actual encrypt-process
          for line in fin:
            fout.write(self.decrypt_string(__UpperCAmelCase				, __UpperCAmelCase))

      except OSError:
        return False

      return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")






