





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()
SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     YolosConfig:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		YolosConfig()

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

      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(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""			)				,		"""r"""			)			)
      SCREAMING_SNAKE_CASE	=		{int(_SCREAMING_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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE = False			)				->     List[str]:


      '''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      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     dict:


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE	=		orig_state_dict.pop(_SCREAMING_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      __lowercase     (   )				->     torch.Tensor:


      '''simple docstring'''


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



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		get_yolos_config(_SCREAMING_SNAKE_CASE			)

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

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

      # Check outputs on an image, prepared by YolosImageProcessor
      SCREAMING_SNAKE_CASE	=		8_00 if yolos_name != """yolos_ti""" else 5_12
      SCREAMING_SNAKE_CASE	=		YolosImageProcessor(format="""coco_detection"""				,		size=_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		image_processor(images=prepare_img()				,		return_tensors="""pt"""			)
      SCREAMING_SNAKE_CASE	=		model(**_SCREAMING_SNAKE_CASE			)
      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(
                [[-39.5_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]]			)
            SCREAMING_SNAKE_CASE	=		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":
            SCREAMING_SNAKE_CASE	=		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]]			)
            SCREAMING_SNAKE_CASE	=		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":
            SCREAMING_SNAKE_CASE	=		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]]			)
            SCREAMING_SNAKE_CASE	=		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":
            SCREAMING_SNAKE_CASE	=		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]]			)
            SCREAMING_SNAKE_CASE	=		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":
            SCREAMING_SNAKE_CASE	=		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]]			)
            SCREAMING_SNAKE_CASE	=		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]				,		_SCREAMING_SNAKE_CASE				,		atol=1E-4			)
      assert torch.allclose(pred_boxes[0, :3, :3]				,		_SCREAMING_SNAKE_CASE				,		atol=1E-4			)

      Path(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
      print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""			)
      model.save_pretrained(_SCREAMING_SNAKE_CASE			)
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
      image_processor.save_pretrained(_SCREAMING_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(_SCREAMING_SNAKE_CASE				,		organization="""hustvl"""			)
            model.push_to_hub(_SCREAMING_SNAKE_CASE				,		organization="""hustvl"""			)


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

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


