




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


def 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														snake_case_      =				1_9_2
														snake_case_      =				7_6_8
														snake_case_      =				1_2
														snake_case_      =				3
														snake_case_      =				[8_0_0, 1_3_3_3]
														snake_case_      =				False
							elif yolos_name == "yolos_s_dWr":
														snake_case_      =				3_3_0
														snake_case_      =				1_4
														snake_case_      =				6
														snake_case_      =				1_3_2_0
							elif "yolos_s" in yolos_name:
														snake_case_      =				3_8_4
														snake_case_      =				1_5_3_6
														snake_case_      =				1_2
														snake_case_      =				6
							elif "yolos_b" in yolos_name:
														snake_case_      =				[8_0_0, 1_3_4_4]

							snake_case_      =				9_1
							snake_case_      =				"huggingface/label-files"
							snake_case_      =				"coco-detection-id2label.json"
							snake_case_      =				json.load(open(hf_hub_download(snake_case							,    snake_case							,    repo_type="dataset"					)							,    "r"					)					)
							snake_case_      =				{int(snake_case					): v for k, v in idalabel.items()}
							snake_case_      =				idalabel
							snake_case_      =				{v: k for k, v in idalabel.items()}

							return config


def 					UpperCamelCase_(     snake_case							:	dict							,    snake_case							:	YolosConfig							,    snake_case							:	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)
														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 					UpperCamelCase_(     snake_case							:	str					):

							'''simple docstring'''






							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 					UpperCamelCase_(     snake_case							:	dict							,    snake_case							:	YolosForObjectDetection					):

							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														snake_case_      =				orig_state_dict.pop(snake_case					)

														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 					UpperCamelCase_(     ):

							'''simple docstring'''






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


@torch.no_grad()
def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str							,    snake_case							:	str							,    snake_case							:	bool = False					):

							'''simple docstring'''






							snake_case_      =				get_yolos_config(snake_case					)

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

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

							# Check outputs on an image, prepared by YolosImageProcessor
							snake_case_      =				8_0_0 if yolos_name != "yolos_ti" else 5_1_2
							snake_case_      =				YolosImageProcessor(format="coco_detection"							,    size=snake_case					)
							snake_case_      =				image_processor(images=prepare_img()							,    return_tensors="pt"					)
							snake_case_      =				model(**snake_case					)
							snake_case_ ,				snake_case_      =				outputs.logits, outputs.pred_boxes

							snake_case_ ,				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]							,    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:
														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(snake_case							,    organization="hustvl"					)
														model.push_to_hub(snake_case							,    organization="hustvl"					)


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










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Dict						=			[
		    "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CanineForMultipleChoice",
		    "CanineForQuestionAnswering",
		    "CanineForSequenceClassification",
		    "CanineForTokenClassification",
		    "CanineLayer",
		    "CanineModel",
		    "CaninePreTrainedModel",
		    "load_tf_weights_in_canine",
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:       int						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




