"""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()
_UpperCamelCase   :						List[Any]      =    logging.get_logger(__name__)





def 				a_     (      _lowerCAmelCase  : str     ):





       '''simple docstring'''

       lowercase__			:		str       =	YolosConfig()

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

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

       return config





def 				a_     (      _lowerCAmelCase  : dict   , _lowerCAmelCase  : YolosConfig   , _lowerCAmelCase  : bool = 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)
              lowercase__			:		Tuple       =	state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""     )
              lowercase__			:		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
              lowercase__			:		str       =	in_proj_weight[: config.hidden_size, :]
              lowercase__			:		int       =	in_proj_bias[: config.hidden_size]
              lowercase__			:		str       =	in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase__			:		List[Any]       =	in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase__			:		str       =	in_proj_weight[-config.hidden_size :, :]
              lowercase__			:		List[str]       =	in_proj_bias[-config.hidden_size :]





def 				a_     (      _lowerCAmelCase  : str     ):





       '''simple docstring'''

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

       return name





def 				a_     (      _lowerCAmelCase  : dict   , _lowerCAmelCase  : YolosForObjectDetection     ):





       '''simple docstring'''

       for key in orig_state_dict.copy().keys():
              lowercase__			:		Optional[int]       =	orig_state_dict.pop(_UpperCamelCase     )

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

       return orig_state_dict





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Tuple       =	'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__			:		str       =	Image.open(requests.get(_UpperCamelCase   , stream=_UpperCamelCase     ).raw     )
       return im





@torch.no_grad()
def 				a_     (      _lowerCAmelCase  : str   , _lowerCAmelCase  : str   , _lowerCAmelCase  : str   , _lowerCAmelCase  : bool = False     ):





       '''simple docstring'''

       lowercase__			:		int       =	get_yolos_config(_UpperCamelCase     )

       # load original state_dict
       lowercase__			:		Optional[int]       =	torch.load(_UpperCamelCase   , map_location='cpu'     )['model']

       # load 🤗 model
       lowercase__			:		int       =	YolosForObjectDetection(_UpperCamelCase     )
       model.eval()
       lowercase__			:		str       =	convert_state_dict(_UpperCamelCase   , _UpperCamelCase     )
       model.load_state_dict(_UpperCamelCase     )

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

       lowercase__    ,      lowercase__			:		Dict       =	None, None
       if yolos_name == "yolos_ti":
              lowercase__			:		Any       =	torch.tensor(
                  [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]     )
              lowercase__			:		List[Any]       =	torch.tensor(
                  [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]     )
       elif yolos_name == "yolos_s_200_pre":
              lowercase__			:		str       =	torch.tensor(
                  [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]     )
              lowercase__			:		List[Any]       =	torch.tensor(
                  [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]     )
       elif yolos_name == "yolos_s_300_pre":
              lowercase__			:		Any       =	torch.tensor(
                  [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]     )
              lowercase__			:		Dict       =	torch.tensor(
                  [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]     )
       elif yolos_name == "yolos_s_dWr":
              lowercase__			:		Union[str, Any]       =	torch.tensor(
                  [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]     )
              lowercase__			:		str       =	torch.tensor(
                  [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]     )
       elif yolos_name == "yolos_base":
              lowercase__			:		Dict       =	torch.tensor(
                  [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]     )
              lowercase__			:		str       =	torch.tensor(
                  [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]     )
       else:
              raise ValueError(f"""Unknown yolos_name: {yolos_name}"""     )

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

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

       if push_to_hub:
              lowercase__			:		Union[str, Any]       =	{
                  '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...'     )
              lowercase__			:		Optional[Any]       =	model_mapping[yolos_name]
              image_processor.push_to_hub(_UpperCamelCase   , organization='hustvl'     )
              model.push_to_hub(_UpperCamelCase   , organization='hustvl'     )


if __name__ == "__main__":
       _UpperCamelCase   :						Optional[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."
       )

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




'''simple docstring'''
import copy
import re







class 							A__    :
  A__		 =				'hp'
  A__		 =				{}
  A__		 =				None





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	Optional[Any]							,					_a      :	Any )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prefix
       _SCREAMING_SNAKE_CASE							    =defaults
       cls.build_naming_info()





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	List[Any] )	->    Any:





       '''simple docstring'''






       if len(_a ) == 0:
            return ""
       _SCREAMING_SNAKE_CASE							    =None
       if any(char.isdigit() for char in word ):
            raise Exception(f"Parameters should not contain numbers: '{word}' contains a number" )
       if word in info["short_word"]:
            return info["short_word"][word]
       for prefix_len in range(1							,					len(_a ) + 1 ):
            _SCREAMING_SNAKE_CASE							    =word[:prefix_len]
            if prefix in info["reverse_short_word"]:
                 continue
            else:
                 _SCREAMING_SNAKE_CASE							    =prefix
                 break

       if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(_a      :	str ):
                 _SCREAMING_SNAKE_CASE							    =''
                 while integer != 0:
                      _SCREAMING_SNAKE_CASE							    =chr(ord('A' ) + integer % 10 ) + s
                      integer //= 10
                 return s

            _SCREAMING_SNAKE_CASE							    =0
            while True:
                 _SCREAMING_SNAKE_CASE							    =word + '#' + int_to_alphabetic(_a )
                 if sword in info["reverse_short_word"]:
                      continue
                 else:
                      _SCREAMING_SNAKE_CASE							    =sword
                      break

       _SCREAMING_SNAKE_CASE							    =short_word
       _SCREAMING_SNAKE_CASE							    =word
       return short_word





  @staticmethod
  def 						A  (		_a      :	Optional[Any]							,					_a      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =param_name.split('_' )

       _SCREAMING_SNAKE_CASE							    =[TrialShortNamer.shortname_for_word(_a							,					_a ) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       _SCREAMING_SNAKE_CASE							    =['', '_']

       for separator in separators:
            _SCREAMING_SNAKE_CASE							    =separator.join(_a )
            if shortname not in info["reverse_short_param"]:
                 _SCREAMING_SNAKE_CASE							    =shortname
                 _SCREAMING_SNAKE_CASE							    =param_name
                 return shortname

       return param_name





  @staticmethod
  def 						A  (		_a      :	Dict							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =TrialShortNamer.shortname_for_key(_a							,					_a )
       _SCREAMING_SNAKE_CASE							    =short_name
       _SCREAMING_SNAKE_CASE							    =param_name





  @classmethod
  def 						A  (		cls      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       if cls.NAMING_INFO is not None:
            return

       _SCREAMING_SNAKE_CASE							    ={
           'short_word': {},
           'reverse_short_word': {},
           'short_param': {},
           'reverse_short_param': {},
       }

       _SCREAMING_SNAKE_CASE							    =list(cls.DEFAULTS.keys() )

       for k in field_keys:
            cls.add_new_param_name(_a							,					_a )

       _SCREAMING_SNAKE_CASE							    =info





  @classmethod
  def 						A  (		cls      :	List[Any]							,					_a      :	int )	->    int:





       '''simple docstring'''






       cls.build_naming_info()
       assert cls.PREFIX is not None
       _SCREAMING_SNAKE_CASE							    =[copy.copy(cls.PREFIX )]

       for k, v in params.items():
            if k not in cls.DEFAULTS:
                 raise Exception(f"You should provide a default value for the param name {k} with value {v}" )
            if v == cls.DEFAULTS[k]:
                 # The default value is not added to the name
                 continue

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['short_param'][k]

            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =1 if v else 0

            _SCREAMING_SNAKE_CASE							    ='' if isinstance(_a							,					(int, float) ) else '-'
            _SCREAMING_SNAKE_CASE							    =f"{key}{sep}{v}"
            name.append(_a )

       return "_".join(_a )





  @classmethod
  def 						A  (		cls      :	Optional[Any]							,					_a      :	List[Any] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =repr[len(cls.PREFIX ) + 1 :]
       if repr == "":
            _SCREAMING_SNAKE_CASE							    =[]
       else:
            _SCREAMING_SNAKE_CASE							    =repr.split('_' )

       _SCREAMING_SNAKE_CASE							    ={}

       for value in values:
            if "-" in value:
                 _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =value.split('-' )
            else:
                 _SCREAMING_SNAKE_CASE							    =re.sub('[0-9.]'							,					''							,					_a )
                 _SCREAMING_SNAKE_CASE							    =float(re.sub('[^0-9.]'							,					''							,					_a ) )

            _SCREAMING_SNAKE_CASE							    =cls.NAMING_INFO['reverse_short_param'][p_k]

            _SCREAMING_SNAKE_CASE							    =p_v

       for k in cls.DEFAULTS:
            if k not in parameters:
                 _SCREAMING_SNAKE_CASE							    =cls.DEFAULTS[k]

       return parameters
