



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()
_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    []

							# 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"
														_lowerCAmelCase     :							int    =    [(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 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    ["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    dct.pop(_lowerCamelCase  )
							_lowerCAmelCase     :							str    =    val






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							List[str]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    BitConfig(
							    global_padding="same"				, layer_type="bottleneck"				, depths=(3, 4, 9)				, out_features=["stage3"]				, embedding_dynamic_padding=_lowerCamelCase				, )
							_lowerCAmelCase     :							Optional[int]    =    ViTHybridConfig(backbone_config=_lowerCamelCase				, image_size=384				, num_labels=1_000  )
							_lowerCAmelCase     :							List[str]    =    False

							# load original model from timm
							_lowerCAmelCase     :							Dict    =    timm.create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							_lowerCAmelCase     :							Dict    =    timm_model.state_dict()
							if base_model:
														remove_classification_head_(_lowerCamelCase  )
							_lowerCAmelCase     :							str    =    create_rename_keys(_lowerCamelCase				, _lowerCamelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )
							read_in_q_k_v(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

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

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														_lowerCAmelCase     :							Union[str, Any]    =    ViTHybridModel(_lowerCamelCase  ).eval()
							else:
														_lowerCAmelCase     :							str    =    ViTHybridForImageClassification(_lowerCamelCase  ).eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Any    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[Any]    =    transform.transforms

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

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

							_lowerCAmelCase     :							Dict    =    prepare_img()
							_lowerCAmelCase     :							Tuple    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Dict    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

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

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Dict    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    outputs.logits

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

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

							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__":
						_snake_case		=  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."
						)

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





import argparse
import json
from pathlib import Path

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

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

							_lowerCAmelCase     :							Tuple    =    "std_conv" if "bit" in model_name else False

							# note that when using BiT as backbone for ViT-hybrid checkpoints,
							# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
							# config.conv_layer = "std_conv_same"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "stem.conv" in name:
														_lowerCAmelCase     :							List[str]    =    name.replace("stem.conv"				, "bit.embedder.convolution"  )
							if "blocks" in name:
														_lowerCAmelCase     :							Any    =    name.replace("blocks"				, "layers"  )
							if "head.fc" in name:
														_lowerCAmelCase     :							Optional[Any]    =    name.replace("head.fc"				, "classifier.1"  )
							if name.startswith("norm"  ):
														_lowerCAmelCase     :							Any    =    "bit." + name
							if "bit" not in name and "classifier" not in name:
														_lowerCAmelCase     :							Dict    =    "bit.encoder." + name

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

							# load HuggingFace model
							_lowerCAmelCase     :							Optional[Any]    =    BitForImageClassification(_lowerCamelCase  )
							model.eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							_lowerCAmelCase     :							Tuple    =    BitImageProcessor(
							    do_resize=_lowerCamelCase				, size={"shortest_edge": timm_transforms[0].size}				, resample=pillow_resamplings[timm_transforms[0].interpolation.value]				, do_center_crop=_lowerCamelCase				, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				, do_normalize=_lowerCamelCase				, image_mean=timm_transforms[-1].mean.tolist()				, image_std=timm_transforms[-1].std.tolist()				, )

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

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

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
														print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"  )
														model.save_pretrained(_lowerCamelCase  )
														processor.save_pretrained(_lowerCamelCase  )

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


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="resnetv2_50x1_bitm",
						    type=str,
						    help="Name of the BiT 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 push the model to the hub.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
