





'''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__       : List[str]         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	YolosConfig:
     __A		:					Any						=			YolosConfig()

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

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

     return config






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






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

     return name






def 			_lowerCAmelCase     (    __snake_case    :					dict					,				__snake_case    :					YolosForObjectDetection     )	->	dict:
     for key in orig_state_dict.copy().keys():
          __A		:					List[str]						=			orig_state_dict.pop(_snake_case     )

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

     return orig_state_dict






def 			_lowerCAmelCase     (    )	->	torch.Tensor:
     __A		:					Union[str, Any]						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A		:					Tuple						=			Image.open(requests.get(_snake_case					,				stream=_snake_case     ).raw     )
     return im






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str					,				__snake_case    :					str					,				__snake_case    :					bool = False     )	->	Dict:
     __A		:					Optional[Any]						=			get_yolos_config(_snake_case     )

     # load original state_dict
     __A		:					List[Any]						=			torch.load(_snake_case					,				map_location='cpu'     )['model']

     # load 🤗 model
     __A		:					Any						=			YolosForObjectDetection(_snake_case     )
     model.eval()
     __A		:					Any						=			convert_state_dict(_snake_case					,				_snake_case     )
     model.load_state_dict(_snake_case     )

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

     __A					,__A		:					Any						=			None, None
     if yolos_name == "yolos_ti":
          __A		:					Dict						=			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]]     )
          __A		:					Optional[Any]						=			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":
          __A		:					Tuple						=			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]]     )
          __A		:					Optional[Any]						=			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":
          __A		:					Tuple						=			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]]     )
          __A		:					str						=			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":
          __A		:					Union[str, Any]						=			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]]     )
          __A		:					Optional[int]						=			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":
          __A		:					Optional[int]						=			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]]     )
          __A		:					Any						=			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]					,				_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		:					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...'     )
          __A		:					str						=			model_mapping[yolos_name]
          image_processor.push_to_hub(_snake_case					,				organization='hustvl'     )
          model.push_to_hub(_snake_case					,				organization='hustvl'     )


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

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




UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   if p_len > t_len:
      return False

   _A      =       0
   _A      =       0
   _A      =       1

   # Calculating the hash of pattern and substring of text
   for i in range(_snake_case				):
      _A      =       (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      _A      =       (ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _A      =       (modulus_power * alphabet_size) % modulus

   for i in range(0	,   t_len - p_len + 1				):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      _A      =       (
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False

def 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



