





'''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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase : Dict											=			logging.get_logger(__name__)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_=False							):

				"""simple docstring"""






				lowercase         = []
				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')							)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("cls_token", "vit.embeddings.cls_token"),
				        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
				        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
				        ("pos_embed", "vit.embeddings.position_embeddings"),
				    ]							)

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

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								lowercase         = [(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"),
								    ]							)

				return rename_keys







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=False							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase_		,							lowerCAmelCase_							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = dct.pop(lowerCAmelCase_							)
				lowercase         = val







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






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







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_=True							):

				"""simple docstring"""






				lowercase         = ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								lowercase         = 8
				# set labels if required
				if not base_model:
								lowercase         = 1000
								lowercase         = "huggingface/label-files"
								lowercase         = "imagenet-1k-id2label.json"
								lowercase         = json.load(open(hf_hub_download(lowerCAmelCase_		,							lowerCAmelCase_		,							repo_type="dataset"							)		,							"r"							)							)
								lowercase         = {int(lowerCAmelCase_							): v for k, v in idalabel.items()}
								lowercase         = idalabel
								lowercase         = {v: k for k, v in idalabel.items()}
				# size of the architecture
				if model_name in ["dino_vits8", "dino_vits16"]:
								lowercase         = 384
								lowercase         = 1536
								lowercase         = 12
								lowercase         = 6

				# load original model from torch hub
				lowercase         = torch.hub.load("facebookresearch/dino:main"		,							lowerCAmelCase_							)
				original_model.eval()

				# load state_dict of original model, remove and rename some keys
				lowercase         = original_model.state_dict()
				if base_model:
								remove_classification_head_(lowerCAmelCase_							)
				lowercase         = create_rename_keys(lowerCAmelCase_		,							base_model=lowerCAmelCase_							)
				for src, dest in rename_keys:
								rename_key(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)
				read_in_q_k_v(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							)

				# load HuggingFace model
				if base_model:
								lowercase         = ViTModel(lowerCAmelCase_		,							add_pooling_layer=lowerCAmelCase_							).eval()
				else:
								lowercase         = ViTForImageClassification(lowerCAmelCase_							).eval()
				model.load_state_dict(lowerCAmelCase_							)

				# Check outputs on an image, prepared by ViTImageProcessor
				lowercase         = ViTImageProcessor()
				lowercase         = image_processor(images=prepare_img()		,							return_tensors="pt"							)
				lowercase         = encoding["pixel_values"]
				lowercase         = model(lowerCAmelCase_							)

				if base_model:
								lowercase         = original_model(lowerCAmelCase_							)
								assert torch.allclose(lowerCAmelCase_		,							outputs.last_hidden_state[:, 0, :]		,							atol=1E-1							)
				else:
								lowercase         = original_model(lowerCAmelCase_							)
								assert logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCAmelCase_		,							outputs.logits		,							atol=1E-3							)

				Path(lowerCAmelCase_							).mkdir(exist_ok=lowerCAmelCase_							)
				print(f'Saving model {model_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 __name__ == "__main__":
			__lowerCamelCase : Tuple											=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="dino_vitb16",
			    type=str,
			    help="Name of the model trained with DINO 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(
			    "--base_model",
			    action="store_true",
			    help="Whether to only convert the base model (no projection head weights).",
			)

			parser.set_defaults(base_model=True)
			__lowerCamelCase : int											=			parser.parse_args()
			convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=_lowercase  )
class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		field(default='''image-classification'''			,       metadata={'''include_in_asdict_even_if_is_default''': True}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''image''': Image()}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''labels''': ClassLabel}  )
		UpperCAmelCase					:	str 			=		"image"
		UpperCAmelCase					:	str 			=		"labels"




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Union[str, Any]				)					-> Union[str, Any]:
						if self.label_column not in features:
										raise ValueError(f'Column {self.label_column} is not present in features.'				)
						if not isinstance(features[self.label_column]							,			A__				):
										raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
						lowercase         = copy.deepcopy(self				)
						lowercase         = self.label_schema.copy()
						lowercase         = features[self.label_column]
						lowercase         = label_schema
						return task_template





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict[str, str]:
						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}




