
'''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()
lowercase					:							str   =      logging.get_logger(__name__)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A=False			) ->			Union[str, Any]:
	_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'),
		        ('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"
		_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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A=False			) ->			List[str]:




	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        SCREAMING_SNAKE_CASE__			(     __A			) ->			Union[str, Any]:
	_snake_case     		=	['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__a    ,       __a			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Optional[int]:
	_snake_case     		=	dct.pop(__a			)
	_snake_case     		=	val







def        SCREAMING_SNAKE_CASE__			(     ) ->			List[str]:
	_snake_case     		=	'http://images.cocodataset.org/val2017/000000039769.jpg'
	_snake_case     		=	Image.open(requests.get(__a    ,       stream=__a			).raw			)
	return im







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

    # load original model from timm
	_snake_case     		=	timm.create_model(__a    ,       pretrained=__a			)
	timm_model.eval()

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		_snake_case     		=	ViTModel(__a			).eval()
	else:
		_snake_case     		=	ViTForImageClassification(__a			).eval()
	model.load_state_dict(__a			)

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

	if base_model:
		_snake_case     		=	timm_model.forward_features(__a			)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(__a    ,       outputs.pooler_output    ,       atol=1e-3			)
	else:
		_snake_case     		=	timm_model(__a			)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__a    ,       outputs.logits    ,       atol=1e-3			)

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


if __name__ == "__main__":
					lowercase					:							int   =      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."
					)

					lowercase					:							List[str]   =      parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



'''simple docstring'''







lowercase					:							Tuple   =      {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase					:							Union[str, Any]   =      {value: key for key, value in encode_dict.items()}







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	''
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception('encode() accepts only letters of the alphabet and spaces'			)
	return encoded







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	if set(__A			) - {"A", "B", " "} != set():
		raise Exception('decode() accepts only \'A\', \'B\' and spaces'			)
	_snake_case     		=	''
	for word in coded.split():
		while len(__A			) != 0:
			decoded += decode_dict[word[:5]]
			_snake_case     		=	word[5:]
		decoded += " "
	return decoded.strip()


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

					testmod()

