


"""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()
__SCREAMING_SNAKE_CASE							:			Any						=			logging.get_logger(__name__)







def   _a (					_SCREAMING_SNAKE_CASE )			-> YolosConfig:
							snake_case_							=						YolosConfig()

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

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

							return config







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE = False )			-> Tuple:
							for i in range(config.num_hidden_layers ):
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														snake_case_							=						state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
														snake_case_							=						state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
														# next, add query, keys and values (in that order) to the state dict
														snake_case_							=						in_proj_weight[: config.hidden_size, :]
														snake_case_							=						in_proj_bias[: config.hidden_size]
														snake_case_							=						in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														snake_case_							=						in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														snake_case_							=						in_proj_weight[-config.hidden_size :, :]
														snake_case_							=						in_proj_bias[-config.hidden_size :]







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

							return name







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> dict:
							for key in orig_state_dict.copy().keys():
														snake_case_							=						orig_state_dict.pop(_lowercase )

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

							return orig_state_dict







def   _a (					)			-> torch.Tensor:
							snake_case_							=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							snake_case_							=						Image.open(requests.get(_lowercase ,       stream=_lowercase ).raw )
							return im







@torch.no_grad()
def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE = False )			-> Optional[int]:
							snake_case_							=						get_yolos_config(_lowercase )

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

							# load 🤗 model
							snake_case_							=						YolosForObjectDetection(_lowercase )
							model.eval()
							snake_case_							=						convert_state_dict(_lowercase ,       _lowercase )
							model.load_state_dict(_lowercase )

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

							snake_case_							=						None, None
							if yolos_name == "yolos_ti":
														snake_case_							=						torch.tensor(
														    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
														snake_case_							=						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":
														snake_case_							=						torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
														snake_case_							=						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":
														snake_case_							=						torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
														snake_case_							=						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":
														snake_case_							=						torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
														snake_case_							=						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":
														snake_case_							=						torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
														snake_case_							=						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] ,       _lowercase ,       atol=1E-4 )
							assert torch.allclose(pred_boxes[0, :3, :3] ,       _lowercase ,       atol=1E-4 )

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

							if push_to_hub:
														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...""" )
														snake_case_							=						model_mapping[yolos_name]
														image_processor.push_to_hub(_lowercase ,       organization="""hustvl""" )
														model.push_to_hub(_lowercase ,       organization="""hustvl""" )


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:			Optional[int]						=			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							:			Tuple						=			parser.parse_args()
				convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''




def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			int:
							if b == 0:
														return 1
							if (b % 2) == 0:
														return actual_power(_lowercase	,				int(b / 2		)		) * actual_power(_lowercase	,				int(b / 2		)		)
							else:
														return a * actual_power(_lowercase	,				int(b / 2		)		) * actual_power(_lowercase	,				int(b / 2		)		)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			float:
							if b < 0:
														return 1 / actual_power(_lowercase	,				_lowercase		)
							return actual_power(_lowercase	,				_lowercase		)


if __name__ == "__main__":
							print(power(-2, -3))

