import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowerCAmelCase       =		torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowerCAmelCase       =		torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowerCAmelCase       =		torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowerCAmelCase       =		torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowerCAmelCase       =		torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowerCAmelCase       =		torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowerCAmelCase       =		torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowerCAmelCase       =		torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowerCAmelCase       =		torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowerCAmelCase       =		torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowerCAmelCase       =		torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowerCAmelCase       =		torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowerCAmelCase       =		torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowerCAmelCase       =		torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowerCAmelCase       =		api.list_models(filter="diffusers")
for mod in models:
					if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
										_lowerCAmelCase       =		"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

										print(f'Started running {mod.modelId}!!!')

										if mod.modelId.startswith("CompVis"):
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
										else:
															_lowerCAmelCase       =		UNetaDModel.from_pretrained(local_checkpoint)

										torch.manual_seed(0)
										random.seed(0)

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		model(noise, time_step).sample

										assert torch.allclose(
										    logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
										)
										print(f'{mod.modelId} has passed successfully!!!')




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()
A				:       int      =  logging.get_logger(__name__)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=False			)    ->						Optional[int]:


						'''simple docstring'''






						__snake_case				=		[]
						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"
												__snake_case				=		[(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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=False			)    ->						int:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		dct.pop(_lowerCAmelCase			)
						__snake_case				=		val





def 						_lowerCAmelCase							(     )    ->						Any:


						'''simple docstring'''






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





@torch.no_grad()
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=True			)    ->						int:


						'''simple docstring'''






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

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

						# load state_dict of original model, remove and rename some keys
						__snake_case				=		original_model.state_dict()
						if base_model:
												remove_classification_head_(_lowerCAmelCase			)
						__snake_case				=		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:
												__snake_case				=		ViTModel(_lowerCAmelCase   ,						add_pooling_layer=_lowerCAmelCase			).eval()
						else:
												__snake_case				=		ViTForImageClassification(_lowerCAmelCase			).eval()
						model.load_state_dict(_lowerCAmelCase			)

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

						if base_model:
												__snake_case				=		original_model(_lowerCAmelCase			)
												assert torch.allclose(_lowerCAmelCase   ,						outputs.last_hidden_state[:, 0, :]   ,						atol=1E-1			)
						else:
												__snake_case				=		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__":
	A				:       str      =  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)
	A				:       Any      =  parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





