






"""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()
__snake_case      :      Optional[int]             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Optional[int]:

						"""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(A__      ,    A__      ,    repo_type='dataset'						)      ,    'r'						)						)
						lowerCAmelCase__        =  {int(A__						): v for k, v in idalabel.items()}
						lowerCAmelCase__        =  idalabel
						lowerCAmelCase__        =  {v: k for k, v in idalabel.items()}

						return config







def 		_UpperCamelCase				(      UpperCamelCase_    :   dict      ,    UpperCamelCase_    :   YolosConfig      ,    UpperCamelCase_    :   bool = False						)					->				Union[str, Any]:

						"""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 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Optional[Any]:

						"""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 		_UpperCamelCase				(      UpperCamelCase_    :   dict      ,    UpperCamelCase_    :   YolosForObjectDetection						)					->				Union[str, Any]:

						"""simple docstring"""



						for key in orig_state_dict.copy().keys():
												lowerCAmelCase__        =  orig_state_dict.pop(A__						)

												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 		_UpperCamelCase				(      )					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowerCAmelCase__        =  Image.open(requests.get(A__      ,    stream=A__						).raw						)
						return im







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   bool = False						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  get_yolos_config(A__						)

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

						# load 🤗 model
						lowerCAmelCase__        =  YolosForObjectDetection(A__						)
						model.eval()
						lowerCAmelCase__        =  convert_state_dict(A__      ,    A__						)
						model.load_state_dict(A__						)

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

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

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

						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(A__      ,    organization='hustvl'						)
												model.push_to_hub(A__      ,    organization='hustvl'						)


if __name__ == "__main__":
					__snake_case      :      Dict             =       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."""
					)

					__snake_case      :      Tuple             =       parser.parse_args()
					convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case      :      Dict             =       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      str             =       [
					    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FocalNetForImageClassification""",
					    """FocalNetForMaskedImageModeling""",
					    """FocalNetBackbone""",
					    """FocalNetModel""",
					    """FocalNetPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

					__snake_case      :      List[Any]             =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





