
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()
_A     =  logging.get_logger(__name__)






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						lowerCAmelCase_      			=			192
						lowerCAmelCase_      			=			768
						lowerCAmelCase_      			=			12
						lowerCAmelCase_      			=			3
						lowerCAmelCase_      			=			[800, 1333]
						lowerCAmelCase_      			=			False
			elif yolos_name == "yolos_s_dWr":
						lowerCAmelCase_      			=			330
						lowerCAmelCase_      			=			14
						lowerCAmelCase_      			=			6
						lowerCAmelCase_      			=			1320
			elif "yolos_s" in yolos_name:
						lowerCAmelCase_      			=			384
						lowerCAmelCase_      			=			1536
						lowerCAmelCase_      			=			12
						lowerCAmelCase_      			=			6
			elif "yolos_b" in yolos_name:
						lowerCAmelCase_      			=			[800, 1344]

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

			return config






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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


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

			return name






def      lowerCamelCase__       (       __lowerCAmelCase						:						dict					,							__lowerCAmelCase						:						YolosForObjectDetection       ):




			"""simple docstring"""


			for key in orig_state_dict.copy().keys():
						lowerCAmelCase_      			=			orig_state_dict.pop(__lowerCAmelCase       )

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

			return orig_state_dict






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_      			=			Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase       ).raw       )
			return im






@torch.no_grad()
def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						bool = False       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			get_yolos_config(__lowerCAmelCase       )

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

			# load 🤗 model
			lowerCAmelCase_      			=			YolosForObjectDetection(__lowerCAmelCase       )
			model.eval()
			lowerCAmelCase_      			=			convert_state_dict(__lowerCAmelCase					,							__lowerCAmelCase       )
			model.load_state_dict(__lowerCAmelCase       )

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

			lowerCAmelCase_			,						lowerCAmelCase_      			=			None, None
			if yolos_name == "yolos_ti":
						lowerCAmelCase_      			=			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]]       )
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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]]       )
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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]]       )
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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]]       )
						lowerCAmelCase_      			=			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":
						lowerCAmelCase_      			=			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]]       )
						lowerCAmelCase_      			=			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]					,							__lowerCAmelCase					,							atol=1e-4       )
			assert torch.allclose(pred_boxes[0, :3, :3]					,							__lowerCAmelCase					,							atol=1e-4       )

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

			if push_to_hub:
						lowerCAmelCase_      			=			{
						    "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..."       )
						lowerCAmelCase_      			=			model_mapping[yolos_name]
						image_processor.push_to_hub(__lowerCAmelCase					,							organization="hustvl"       )
						model.push_to_hub(__lowerCAmelCase					,							organization="hustvl"       )


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

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





import functools






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			len(__lowerCAmelCase       )
			lowerCAmelCase_      			=			len(__lowerCAmelCase       )

			@functools.cache
			def min_distance(__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int       ) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_      			=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1					,							__lowerCAmelCase       )					,							1 + min_distance(__lowerCAmelCase					,							indexa + 1       )					,							diff + min_distance(indexa + 1					,							indexa + 1       )					,							)

			return min_distance(0					,							0       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



