



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()
UpperCAmelCase_		 =					logging.get_logger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     YolosConfig:
   _A      =       YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      _A      =       192
      _A      =       768
      _A      =       12
      _A      =       3
      _A      =       [800, 1_333]
      _A      =       False
   elif yolos_name == "yolos_s_dWr":
      _A      =       330
      _A      =       14
      _A      =       6
      _A      =       1_320
   elif "yolos_s" in yolos_name:
      _A      =       384
      _A      =       1_536
      _A      =       12
      _A      =       6
   elif "yolos_b" in yolos_name:
      _A      =       [800, 1_344]

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

   return config

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:YolosConfig	,   _snake_case	:bool = 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)
      _A      =       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
      _A      =       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
      # next, add query, keys and values (in that order) to the state dict
      _A      =       in_proj_weight[: config.hidden_size, :]
      _A      =       in_proj_bias[: config.hidden_size]
      _A      =       in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _A      =       in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      _A      =       in_proj_weight[-config.hidden_size :, :]
      _A      =       in_proj_bias[-config.hidden_size :]

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

   return name

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:YolosForObjectDetection				)  ->     dict:
   for key in orig_state_dict.copy().keys():
      _A      =       orig_state_dict.pop(_snake_case				)

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

   return orig_state_dict

def 	SCREAMING_SNAKE_CASE_							(	)  ->     torch.Tensor:
   _A      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A      =       Image.open(requests.get(_snake_case	,   stream=_snake_case				).raw				)
   return im

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str	,   _snake_case	:str	,   _snake_case	:bool = False				)  ->     Dict:
   _A      =       get_yolos_config(_snake_case				)

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

   # load 🤗 model
   _A      =       YolosForObjectDetection(_snake_case				)
   model.eval()
   _A      =       convert_state_dict(_snake_case	,   _snake_case				)
   model.load_state_dict(_snake_case				)

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

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


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

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







'''simple docstring'''



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


A__						:   Tuple				=    logging.get_logger(__name__)

A__						:   Dict				=    {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     'rwkv'
    lowerCamelCase    :   Any					=     {'max_position_embeddings': 'context_length'}







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=5_02_77     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=40_96     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						vocab_size
         __lowerCamelCase  :      Tuple														=						context_length
         __lowerCamelCase  :      str														=						hidden_size
         __lowerCamelCase  :      List[str]														=						num_hidden_layers
         __lowerCamelCase  :      Any														=						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __lowerCamelCase  :      Optional[int]														=						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __lowerCamelCase  :      Optional[Any]														=						layer_norm_epsilon
         __lowerCamelCase  :      int														=						rescale_every
         __lowerCamelCase  :      Tuple														=						use_cache

         __lowerCamelCase  :      int														=						bos_token_id
         __lowerCamelCase  :      Optional[Any]														=						eos_token_id

         super().__init__(
             tie_word_embeddings=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )






