
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()
UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=False							):
	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_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=False							):
	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_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    dct.pop(__SCREAMING_SNAKE_CASE							)
	lowercase       =    val






def 							UpperCAmelCase_		(							):
	lowercase       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowercase       =    Image.open(requests.get(__SCREAMING_SNAKE_CASE ,							stream=__SCREAMING_SNAKE_CASE							).raw							)
	return im






@torch.no_grad()
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=True							):
	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(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							repo_type='dataset'							) ,							'r'							)							)
		lowercase       =    {int(__SCREAMING_SNAKE_CASE							): 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' ,							__SCREAMING_SNAKE_CASE							)
	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_(__SCREAMING_SNAKE_CASE							)
	lowercase       =    create_rename_keys(__SCREAMING_SNAKE_CASE ,							base_model=__SCREAMING_SNAKE_CASE							)
	for src, dest in rename_keys:
		rename_key(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	read_in_q_k_v(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

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

	# 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(__SCREAMING_SNAKE_CASE							)

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

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


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



import pprint

import requests

UpperCAmelCase	      = '''https://zenquotes.io/api'''






def 							UpperCAmelCase_		(							):
	return requests.get(API_ENDPOINT_URL + '/today'							).json()






def 							UpperCAmelCase_		(							):
	return requests.get(API_ENDPOINT_URL + '/random'							).json()


if __name__ == "__main__":
	UpperCAmelCase	      = random_quotes()
	pprint.pprint(response)

