




"""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()
lowercase__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__=False		):
				_lowerCamelCase     :			Union[str, Any]    =    []
				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"
								_lowerCamelCase     :			List[Any]    =    [(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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=False		):
				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     :			List[Any]    =    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
								_lowerCamelCase     :			List[str]    =    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
								# next, add query, keys and values (in that order) to the state dict
								_lowerCamelCase     :			int    =    in_proj_weight[
								    : config.hidden_size, :
								]
								_lowerCamelCase     :			Optional[int]    =    in_proj_bias[: config.hidden_size]
								_lowerCamelCase     :			Any    =    in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_lowerCamelCase     :			Any    =    in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_lowerCamelCase     :			Optional[Any]    =    in_proj_weight[
								    -config.hidden_size :, :
								]
								_lowerCamelCase     :			Union[str, Any]    =    in_proj_bias[-config.hidden_size :]







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			List[Any]    =    ['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(lowercase__							,    lowercase__		)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    dct.pop(lowercase__		)
				_lowerCamelCase     :			str    =    val







def      _snake_case			(				):
				_lowerCamelCase     :			Union[str, Any]    =    'http://images.cocodataset.org/val2017/000000039769.jpg'
				_lowerCamelCase     :			List[Any]    =    Image.open(requests.get(lowercase__							,    stream=lowercase__		).raw		)
				return im







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=True		):
				_lowerCamelCase     :			Optional[int]    =    ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								_lowerCamelCase     :			Union[str, Any]    =    8
				# set labels if required
				if not base_model:
								_lowerCamelCase     :			Tuple    =    1000
								_lowerCamelCase     :			Optional[int]    =    'huggingface/label-files'
								_lowerCamelCase     :			Optional[int]    =    'imagenet-1k-id2label.json'
								_lowerCamelCase     :			Optional[Any]    =    json.load(open(hf_hub_download(lowercase__							,    lowercase__							,    repo_type='dataset'		)							,    'r'		)		)
								_lowerCamelCase     :			List[Any]    =    {int(lowercase__		): v for k, v in idalabel.items()}
								_lowerCamelCase     :			str    =    idalabel
								_lowerCamelCase     :			str    =    {v: k for k, v in idalabel.items()}
				# size of the architecture
				if model_name in ["dino_vits8", "dino_vits16"]:
								_lowerCamelCase     :			int    =    384
								_lowerCamelCase     :			List[Any]    =    1536
								_lowerCamelCase     :			List[Any]    =    12
								_lowerCamelCase     :			Dict    =    6

				# load original model from torch hub
				_lowerCamelCase     :			Optional[Any]    =    torch.hub.load('facebookresearch/dino:main'							,    lowercase__		)
				original_model.eval()

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

				# load HuggingFace model
				if base_model:
								_lowerCamelCase     :			Union[str, Any]    =    ViTModel(lowercase__							,    add_pooling_layer=lowercase__		).eval()
				else:
								_lowerCamelCase     :			Any    =    ViTForImageClassification(lowercase__		).eval()
				model.load_state_dict(lowercase__		)

				# Check outputs on an image, prepared by ViTImageProcessor
				_lowerCamelCase     :			Tuple    =    ViTImageProcessor()
				_lowerCamelCase     :			int    =    image_processor(images=prepare_img()							,    return_tensors='pt'		)
				_lowerCamelCase     :			Optional[int]    =    encoding['pixel_values']
				_lowerCamelCase     :			int    =    model(lowercase__		)

				if base_model:
								_lowerCamelCase     :			str    =    original_model(lowercase__		)
								assert torch.allclose(lowercase__							,    outputs.last_hidden_state[:, 0, :]							,    atol=1E-1		)
				else:
								_lowerCamelCase     :			List[Any]    =    original_model(lowercase__		)
								assert logits.shape == outputs.logits.shape
								assert torch.allclose(lowercase__							,    outputs.logits							,    atol=1E-3		)

				Path(lowercase__		).mkdir(exist_ok=lowercase__		)
				print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(lowercase__		)
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(lowercase__		)


if __name__ == "__main__":
			lowercase__     						=						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)
			lowercase__     						=						parser.parse_args()
			convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()