



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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						YolosConfig:
				a__	:							Optional[int]      						= YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								a__	:							Union[str, Any]      						= 1_92
								a__	:							int      						= 7_68
								a__	:							Tuple      						= 12
								a__	:							Dict      						= 3
								a__	:							Dict      						= [8_00, 13_33]
								a__	:							List[str]      						= False
				elif yolos_name == "yolos_s_dWr":
								a__	:							Optional[Any]      						= 3_30
								a__	:							Tuple      						= 14
								a__	:							Union[str, Any]      						= 6
								a__	:							Tuple      						= 13_20
				elif "yolos_s" in yolos_name:
								a__	:							Optional[int]      						= 3_84
								a__	:							List[str]      						= 15_36
								a__	:							Optional[Any]      						= 12
								a__	:							int      						= 6
				elif "yolos_b" in yolos_name:
								a__	:							int      						= [8_00, 13_44]

				a__	:							List[Any]      						= 91
				a__	:							Dict      						= "huggingface/label-files"
				a__	:							Tuple      						= "coco-detection-id2label.json"
				a__	:							List[Any]      						= json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" )	,				"r" ) )
				a__	:							Optional[Any]      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				a__	:							Tuple      						= idalabel
				a__	:							int      						= {v: k for k, v in idalabel.items()}

				return config






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = False )	->						List[Any]:
				for i in range(config.num_hidden_layers ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								a__	:							str      						= state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
								a__	:							str      						= state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
								# next, add query, keys and values (in that order) to the state dict
								a__	:							List[Any]      						= in_proj_weight[: config.hidden_size, :]
								a__	:							List[str]      						= in_proj_bias[: config.hidden_size]
								a__	:							int      						= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a__	:							int      						= in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								a__	:							Optional[Any]      						= in_proj_weight[-config.hidden_size :, :]
								a__	:							Optional[int]      						= in_proj_bias[-config.hidden_size :]






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						str:
				if "backbone" in name:
								a__	:							Union[str, Any]      						= name.replace("backbone"	,				"vit" )
				if "cls_token" in name:
								a__	:							List[str]      						= name.replace("cls_token"	,				"embeddings.cls_token" )
				if "det_token" in name:
								a__	:							Optional[Any]      						= name.replace("det_token"	,				"embeddings.detection_tokens" )
				if "mid_pos_embed" in name:
								a__	:							int      						= name.replace("mid_pos_embed"	,				"encoder.mid_position_embeddings" )
				if "pos_embed" in name:
								a__	:							Union[str, Any]      						= name.replace("pos_embed"	,				"embeddings.position_embeddings" )
				if "patch_embed.proj" in name:
								a__	:							Optional[Any]      						= name.replace("patch_embed.proj"	,				"embeddings.patch_embeddings.projection" )
				if "blocks" in name:
								a__	:							str      						= name.replace("blocks"	,				"encoder.layer" )
				if "attn.proj" in name:
								a__	:							Optional[int]      						= name.replace("attn.proj"	,				"attention.output.dense" )
				if "attn" in name:
								a__	:							int      						= name.replace("attn"	,				"attention.self" )
				if "norm1" in name:
								a__	:							Optional[int]      						= name.replace("norm1"	,				"layernorm_before" )
				if "norm2" in name:
								a__	:							Dict      						= name.replace("norm2"	,				"layernorm_after" )
				if "mlp.fc1" in name:
								a__	:							int      						= name.replace("mlp.fc1"	,				"intermediate.dense" )
				if "mlp.fc2" in name:
								a__	:							Dict      						= name.replace("mlp.fc2"	,				"output.dense" )
				if "class_embed" in name:
								a__	:							List[str]      						= name.replace("class_embed"	,				"class_labels_classifier" )
				if "bbox_embed" in name:
								a__	:							Dict      						= name.replace("bbox_embed"	,				"bbox_predictor" )
				if "vit.norm" in name:
								a__	:							int      						= name.replace("vit.norm"	,				"vit.layernorm" )

				return name






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						dict:
				for key in orig_state_dict.copy().keys():
								a__	:							str      						= orig_state_dict.pop(__UpperCamelCase )

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

				return orig_state_dict






def     SCREAMING_SNAKE_CASE(      )	->						torch.Tensor:
				a__	:							Optional[Any]      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							Any      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )
				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = False )	->						Optional[Any]:
				a__	:							List[str]      						= get_yolos_config(__UpperCamelCase )

				# load original state_dict
				a__	:							int      						= torch.load(__UpperCamelCase	,				map_location="cpu" )["model"]

				# load 🤗 model
				a__	:							List[str]      						= YolosForObjectDetection(__UpperCamelCase )
				model.eval()
				a__	:							Any      						= convert_state_dict(__UpperCamelCase	,				__UpperCamelCase )
				model.load_state_dict(__UpperCamelCase )

				# Check outputs on an image, prepared by YolosImageProcessor
				a__	:							Tuple      						= 8_00 if yolos_name != "yolos_ti" else 5_12
				a__	:							int      						= YolosImageProcessor(format="coco_detection"	,				size=__UpperCamelCase )
				a__	:							List[Any]      						= image_processor(images=prepare_img()	,				return_tensors="pt" )
				a__	:							int      						= model(**__UpperCamelCase )
				a__						,   a__	:							int      						= outputs.logits, outputs.pred_boxes

				a__						,   a__	:							Union[str, Any]      						= None, None
				if yolos_name == "yolos_ti":
								a__	:							Any      						= torch.tensor(
								    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] )
								a__	:							str      						= torch.tensor(
								    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] )
				elif yolos_name == "yolos_s_200_pre":
								a__	:							Dict      						= torch.tensor(
								    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] )
								a__	:							List[str]      						= torch.tensor(
								    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] )
				elif yolos_name == "yolos_s_300_pre":
								a__	:							Union[str, Any]      						= torch.tensor(
								    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] )
								a__	:							Dict      						= torch.tensor(
								    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] )
				elif yolos_name == "yolos_s_dWr":
								a__	:							Union[str, Any]      						= torch.tensor(
								    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] )
								a__	:							str      						= torch.tensor(
								    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] )
				elif yolos_name == "yolos_base":
								a__	:							Any      						= torch.tensor(
								    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] )
								a__	:							Optional[Any]      						= torch.tensor(
								    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] )
				else:
								raise ValueError(F'Unknown yolos_name: {yolos_name}' )

				assert torch.allclose(logits[0, :3, :3]	,				__UpperCamelCase	,				atol=1e-4 )
				assert torch.allclose(pred_boxes[0, :3, :3]	,				__UpperCamelCase	,				atol=1e-4 )

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

				if push_to_hub:
								a__	:							List[Any]      						= {
								    "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..." )
								a__	:							str      						= model_mapping[yolos_name]
								image_processor.push_to_hub(__UpperCamelCase	,				organization="hustvl" )
								model.push_to_hub(__UpperCamelCase	,				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)







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							int      						= []

				# fmt: off
				# stem:
				rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
				rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )

				rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
				rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )

				# backbone
				rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
				rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
				rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )

				for stage_idx in range(len(config.backbone_config.depths ) ):
								for layer_idx in range(config.backbone_config.depths[stage_idx] ):
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias') )

								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight') )
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight') )
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias') )

				# transformer encoder
				for i in range(config.num_hidden_layers ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
								rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
								rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
								rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
								rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
								rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
								rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
								rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
								rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
								rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								        ("pre_logits.fc.weight", "pooler.dense.weight"),
								        ("pre_logits.fc.bias", "pooler.dense.bias"),
								    ] )

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								a__	:							Dict      						= [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ] )
				# fmt: on

				return rename_keys






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						Union[str, Any]:
				for i in range(config.num_hidden_layers ):
								if base_model:
												a__	:							Tuple      						= ""
								else:
												a__	:							Dict      						= "vit."
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								a__	:							int      						= state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
								a__	:							Tuple      						= state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
								# next, add query, keys and values (in that order) to the state dict
								a__	:							Optional[int]      						= in_proj_weight[
								    : config.hidden_size, :
								]
								a__	:							List[str]      						= in_proj_bias[: config.hidden_size]
								a__	:							Any      						= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a__	:							Any      						= in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								a__	:							Union[str, Any]      						= in_proj_weight[
								    -config.hidden_size :, :
								]
								a__	:							Dict      						= in_proj_bias[-config.hidden_size :]






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				a__	:							Optional[int]      						= ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				a__	:							Optional[Any]      						= dct.pop(__UpperCamelCase )
				a__	:							str      						= val






def     SCREAMING_SNAKE_CASE(      )	->						str:
				a__	:							Optional[int]      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							Any      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )
				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							Dict      						= BitConfig(
				    global_padding="same"	,				layer_type="bottleneck"	,				depths=(3, 4, 9)	,				out_features=["stage3"]	,				embedding_dynamic_padding=__UpperCamelCase	,				)
				a__	:							List[Any]      						= ViTHybridConfig(backbone_config=__UpperCamelCase	,				image_size=3_84	,				num_labels=10_00 )
				a__	:							Union[str, Any]      						= False

				# load original model from timm
				a__	:							List[Any]      						= timm.create_model(__UpperCamelCase	,				pretrained=__UpperCamelCase )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				a__	:							Optional[Any]      						= timm_model.state_dict()
				if base_model:
								remove_classification_head_(__UpperCamelCase )
				a__	:							Union[str, Any]      						= create_rename_keys(__UpperCamelCase	,				__UpperCamelCase )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				read_in_q_k_v(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )

				a__	:							str      						= "huggingface/label-files"
				a__	:							Optional[Any]      						= "imagenet-1k-id2label.json"
				a__	:							int      						= json.load(open(hf_hub_download(__UpperCamelCase	,				__UpperCamelCase	,				repo_type="dataset" )	,				"r" ) )
				a__	:							Any      						= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				a__	:							Dict      						= idalabel
				a__	:							List[str]      						= {v: k for k, v in idalabel.items()}

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								a__	:							str      						= ViTHybridModel(__UpperCamelCase ).eval()
				else:
								a__	:							Dict      						= ViTHybridForImageClassification(__UpperCamelCase ).eval()
				model.load_state_dict(__UpperCamelCase )

				# create image processor
				a__	:							Union[str, Any]      						= create_transform(**resolve_data_config({}	,				model=__UpperCamelCase ) )
				a__	:							str      						= transform.transforms

				a__	:							List[Any]      						= {
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

				a__	:							List[Any]      						= ViTHybridImageProcessor(
				    do_resize=__UpperCamelCase	,				size={"shortest_edge": timm_transforms[0].size}	,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]	,				do_center_crop=__UpperCamelCase	,				crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}	,				do_normalize=__UpperCamelCase	,				image_mean=timm_transforms[-1].mean.tolist()	,				image_std=timm_transforms[-1].std.tolist()	,				)

				a__	:							Optional[Any]      						= prepare_img()
				a__	:							List[str]      						= transform(__UpperCamelCase ).unsqueeze(0 )
				a__	:							Optional[Any]      						= processor(__UpperCamelCase	,				return_tensors="pt" ).pixel_values

				# verify pixel values
				assert torch.allclose(__UpperCamelCase	,				__UpperCamelCase )

				# verify logits
				with torch.no_grad():
								a__	:							Dict      						= model(__UpperCamelCase )
								a__	:							Optional[Any]      						= outputs.logits

				print("Predicted class:"	,				logits.argmax(-1 ).item() )
				if base_model:
								a__	:							Optional[int]      						= timm_model.forward_features(__UpperCamelCase )
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(__UpperCamelCase	,				outputs.pooler_output	,				atol=1e-3 )
				else:
								a__	:							List[Any]      						= timm_model(__UpperCamelCase )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(__UpperCamelCase	,				outputs.logits	,				atol=1e-3 )
				print("Looks ok!" )

				if pytorch_dump_folder_path is not None:
								Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
								print(F'Saving model {vit_name} to {pytorch_dump_folder_path}' )
								model.save_pretrained(__UpperCamelCase )
								print(F'Saving processor to {pytorch_dump_folder_path}' )
								processor.save_pretrained(__UpperCamelCase )

				if push_to_hub:
								print(F'Pushing model and processor to the hub {vit_name}' )
								model.push_to_hub(F'ybelkada/{vit_name}' )
								processor.push_to_hub(F'ybelkada/{vit_name}' )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--vit_name""",
						    default="""vit_base_r50_s16_384""",
						    type=str,
						    help="""Name of the hybrid ViT timm model you'd like to convert.""",
						)
						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 to upload the model to the HuggingFace hub."""
						)

						lowerCamelCase		       =      parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)


