



"""simple docstring"""






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


logging.set_verbosity_info()
__snake_case							:     Optional[int]   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case=False			)		->     Optional[Any]:
			__lowerCAmelCase   :						str				= []
			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"),
						        ("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   :						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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=False			)		->     int:
			for i in range(config.num_hidden_layers			):
						if base_model:
									__lowerCAmelCase   :						str				= ""
						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   :						Optional[Any]				= in_proj_weight[
						    : config.hidden_size, :
						]
						__lowerCAmelCase   :						Optional[int]				= in_proj_bias[: config.hidden_size]
						__lowerCAmelCase   :						List[Any]				= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__lowerCAmelCase   :						Tuple				= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__lowerCAmelCase   :						Optional[int]				= in_proj_weight[
						    -config.hidden_size :, :
						]
						__lowerCAmelCase   :						str				= in_proj_bias[-config.hidden_size :]


def     _lowercase       (						__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						Any				= ["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[Any]:
			__lowerCAmelCase   :						int				= dct.pop(__snake_case			)
			__lowerCAmelCase   :						str				= val


def     _lowercase       (						)		->     str:
			__lowerCAmelCase   :						Dict				= "http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase   :						Any				= Image.open(requests.get(__snake_case      ,stream=__snake_case			).raw			)
			return im


@torch.no_grad()
def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Tuple				= ViTConfig()
			__lowerCAmelCase   :						Tuple				= False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						__lowerCAmelCase   :						Optional[int]				= True
						__lowerCAmelCase   :						Dict				= int(vit_name[-12:-10]			)
						__lowerCAmelCase   :						Tuple				= int(vit_name[-9:-6]			)
			else:
						__lowerCAmelCase   :						str				= 1_000
						__lowerCAmelCase   :						Tuple				= "huggingface/label-files"
						__lowerCAmelCase   :						Optional[int]				= "imagenet-1k-id2label.json"
						__lowerCAmelCase   :						Tuple				= json.load(open(hf_hub_download(__snake_case      ,__snake_case      ,repo_type="dataset"			)      ,"r"			)			)
						__lowerCAmelCase   :						Optional[Any]				= {int(__snake_case			): v for k, v in idalabel.items()}
						__lowerCAmelCase   :						Union[str, Any]				= idalabel
						__lowerCAmelCase   :						int				= {v: k for k, v in idalabel.items()}
						__lowerCAmelCase   :						Optional[Any]				= int(vit_name[-6:-4]			)
						__lowerCAmelCase   :						Any				= int(vit_name[-3:]			)
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith("tiny"			):
									__lowerCAmelCase   :						str				= 192
									__lowerCAmelCase   :						str				= 768
									__lowerCAmelCase   :						Dict				= 12
									__lowerCAmelCase   :						str				= 3
						elif vit_name[9:].startswith("small"			):
									__lowerCAmelCase   :						Tuple				= 384
									__lowerCAmelCase   :						List[Any]				= 1_536
									__lowerCAmelCase   :						Any				= 12
									__lowerCAmelCase   :						str				= 6
						else:
									pass
			else:
						if vit_name[4:].startswith("small"			):
									__lowerCAmelCase   :						Union[str, Any]				= 768
									__lowerCAmelCase   :						Union[str, Any]				= 2_304
									__lowerCAmelCase   :						str				= 8
									__lowerCAmelCase   :						List[Any]				= 8
						elif vit_name[4:].startswith("base"			):
									pass
						elif vit_name[4:].startswith("large"			):
									__lowerCAmelCase   :						str				= 1_024
									__lowerCAmelCase   :						List[str]				= 4_096
									__lowerCAmelCase   :						Any				= 24
									__lowerCAmelCase   :						List[Any]				= 16
						elif vit_name[4:].startswith("huge"			):
									__lowerCAmelCase   :						Union[str, Any]				= 1_280
									__lowerCAmelCase   :						List[Any]				= 5_120
									__lowerCAmelCase   :						str				= 32
									__lowerCAmelCase   :						Tuple				= 16

    # load original model from timm
			__lowerCAmelCase   :						List[str]				= timm.create_model(__snake_case      ,pretrained=__snake_case			)
			timm_model.eval()

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

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						__lowerCAmelCase   :						Tuple				= ViTModel(__snake_case			).eval()
			else:
						__lowerCAmelCase   :						List[str]				= ViTForImageClassification(__snake_case			).eval()
			model.load_state_dict(__snake_case			)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						__lowerCAmelCase   :						Tuple				= DeiTImageProcessor(size=config.image_size			)
			else:
						__lowerCAmelCase   :						Optional[int]				= ViTImageProcessor(size=config.image_size			)
			__lowerCAmelCase   :						Dict				= image_processor(images=prepare_img()      ,return_tensors="pt"			)
			__lowerCAmelCase   :						Optional[int]				= encoding["pixel_values"]
			__lowerCAmelCase   :						int				= model(__snake_case			)

			if base_model:
						__lowerCAmelCase   :						Tuple				= timm_model.forward_features(__snake_case			)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(__snake_case      ,outputs.pooler_output      ,atol=1e-3			)
			else:
						__lowerCAmelCase   :						Optional[int]				= timm_model(__snake_case			)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(__snake_case      ,outputs.logits      ,atol=1e-3			)

			Path(__snake_case			).mkdir(exist_ok=__snake_case			)
			print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
			model.save_pretrained(__snake_case			)
			print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
			image_processor.save_pretrained(__snake_case			)


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--vit_name',
	    default='vit_base_patch16_224',
	    type=str,
	    help='Name of the 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.'
	)

	__snake_case							:     Any   =					parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




"""simple docstring"""






import math


def     _lowercase       (						__snake_case      ,__snake_case			)		->     float:
			return math.pow(__snake_case      ,2			) - a


def     _lowercase       (						__snake_case			)		->     float:
			return 2 * x


def     _lowercase       (						__snake_case			)		->     float:
			__lowerCAmelCase   :						List[str]				= 2.0

			while start <= a:
						__lowerCAmelCase   :						Union[str, Any]				= math.pow(__snake_case      ,2			)

			return start


def     _lowercase       (						__snake_case      ,__snake_case = 9_999      ,__snake_case = 0.00000000000001			)		->     float:

			if a < 0:
						raise ValueError("math domain error"			)

			__lowerCAmelCase   :						List[Any]				= get_initial_point(__snake_case			)

			for _ in range(__snake_case			):
						__lowerCAmelCase   :						Union[str, Any]				= value
						__lowerCAmelCase   :						List[str]				= value - fx(__snake_case      ,__snake_case			) / fx_derivative(__snake_case			)
						if abs(prev_value - value			) < tolerance:
									return value

			return value


if __name__ == "__main__":
	from doctest import testmod

	testmod()