


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()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      YolosConfig()

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

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

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


		"""simple docstring"""




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

		return name
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		for key in orig_state_dict.copy().keys():
				lowercase__            =      orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

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

		return orig_state_dict
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase__            =      Image.open(requests.get(SCREAMING_SNAKE_CASE						,				stream=SCREAMING_SNAKE_CASE     ).raw     )
		return im
@torch.no_grad()
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE = False     ):


		"""simple docstring"""




		lowercase__            =      get_yolos_config(SCREAMING_SNAKE_CASE     )

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

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

		# Check outputs on an image, prepared by YolosImageProcessor
		lowercase__            =      8_00 if yolos_name != '''yolos_ti''' else 5_12
		lowercase__            =      YolosImageProcessor(format='''coco_detection'''						,				size=SCREAMING_SNAKE_CASE     )
		lowercase__            =      image_processor(images=prepare_img()						,				return_tensors='''pt'''     )
		lowercase__            =      model(**SCREAMING_SNAKE_CASE     )
		lowercase__     ,		lowercase__            =      outputs.logits, outputs.pred_boxes

		lowercase__     ,		lowercase__            =      None, None
		if yolos_name == "yolos_ti":
				lowercase__            =      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]]     )
				lowercase__            =      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":
				lowercase__            =      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]]     )
				lowercase__            =      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":
				lowercase__            =      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]]     )
				lowercase__            =      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":
				lowercase__            =      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]]     )
				lowercase__            =      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":
				lowercase__            =      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]]     )
				lowercase__            =      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:
				lowercase__            =      {
				    '''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__            =      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__":
			lowerCAmelCase       =							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.'
			)

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




def     __lowerCAmelCase			(			a__						,    a__  )       ->  float:

				def get_matched_characters(a__						,    a__  ) -> str:
								__a										=    []
								__a										=    min(len(_stra  )						,    len(_stra  )  ) // 2
								for i, l in enumerate(_stra  ):
												__a										=    int(max(0						,    i - limit  )  )
												__a										=    int(min(i + limit + 1						,    len(_stra  )  )  )
												if l in _stra[left:right]:
																matched.append(a__  )
																__a										=    F"""{_stra[0:_stra.index(a__  )]} {_stra[_stra.index(a__  ) + 1:]}"""

								return "".join(a__  )

				# matching characters
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    get_matched_characters(a__						,    a__  )
				__a										=    len(a__  )

				# transposition
				__a										=    (
				    len([(ca, ca) for ca, ca in zip(a__						,    a__  ) if ca != ca]  ) // 2
				)

				if not match_count:
								__a										=    0.0
				else:
								__a										=    (
								    1
								    / 3
								    * (
								        match_count / len(a__  )
								        + match_count / len(a__  )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				__a										=    0
				for ca, ca in zip(stra[:4]						,    stra[:4]  ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(jaro_winkler('hello', 'world'))