
'''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__											=logging.get_logger(__name__)






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






def      UpperCamelCase_				(      A__						):
	a_      				=						["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(A__  ,      A__						)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	a_      				=						dct.pop(A__						)
	a_      				=						val






def      UpperCamelCase_				(      ):
	a_      				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a_      				=						Image.open(requests.get(A__  ,      stream=A__						).raw						)
	return im






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						ViTConfig()
	a_      				=						False
	# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
	if vit_name[-5:] == "in21k":
		a_      				=						True
		a_      				=						int(vit_name[-12:-10]						)
		a_      				=						int(vit_name[-9:-6]						)
	else:
		a_      				=						10_00
		a_      				=						"""huggingface/label-files"""
		a_      				=						"""imagenet-1k-id2label.json"""
		a_      				=						json.load(open(hf_hub_download(A__  ,      A__  ,      repo_type="""dataset"""						)  ,      """r"""						)						)
		a_      				=						{int(A__						): v for k, v in idalabel.items()}
		a_      				=						idalabel
		a_      				=						{v: k for k, v in idalabel.items()}
		a_      				=						int(vit_name[-6:-4]						)
		a_      				=						int(vit_name[-3:]						)
	# size of the architecture
	if "deit" in vit_name:
		if vit_name[9:].startswith("""tiny"""						):
			a_      				=						1_92
			a_      				=						7_68
			a_      				=						12
			a_      				=						3
		elif vit_name[9:].startswith("""small"""						):
			a_      				=						3_84
			a_      				=						15_36
			a_      				=						12
			a_      				=						6
		else:
			pass
	else:
		if vit_name[4:].startswith("""small"""						):
			a_      				=						7_68
			a_      				=						23_04
			a_      				=						8
			a_      				=						8
		elif vit_name[4:].startswith("""base"""						):
			pass
		elif vit_name[4:].startswith("""large"""						):
			a_      				=						10_24
			a_      				=						40_96
			a_      				=						24
			a_      				=						16
		elif vit_name[4:].startswith("""huge"""						):
			a_      				=						12_80
			a_      				=						51_20
			a_      				=						32
			a_      				=						16

    # load original model from timm
	a_      				=						timm.create_model(A__  ,      pretrained=A__						)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	a_      				=						timm_model.state_dict()
	if base_model:
		remove_classification_head_(A__						)
	a_      				=						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":
		a_      				=						ViTModel(A__						).eval()
	else:
		a_      				=						ViTForImageClassification(A__						).eval()
	model.load_state_dict(A__						)

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

	if base_model:
		a_      				=						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:
		a_      				=						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__											=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__											=parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)


'''simple docstring'''
from __future__ import annotations






def      UpperCamelCase_				(      A__						):
	a_      				=						[True] * limit
	a_      				=						False
	a_      				=						False
	a_      				=						True

	for i in range(3  ,      int(limit**0.5 + 1						)  ,      2						):
		a_      				=						i * 2
		while index < limit:
			a_      				=						False
			a_      				=						index + i

	a_      				=						[2]

	for i in range(3  ,      A__  ,      2						):
		if is_prime[i]:
			primes.append(A__						)

	return primes






def      UpperCamelCase_				(      A__ = 1_00_00_00						):
	a_      				=						prime_sieve(A__						)
	a_      				=						0
	a_      				=						0

	for i in range(len(A__						)						):
		for j in range(i + length  ,      len(A__						)						):
			a_      				=						sum(primes[i:j]						)
			if sol >= ceiling:
				break

			if sol in primes:
				a_      				=						j - i
				a_      				=						sol

	return largest


if __name__ == "__main__":
		print(F"""{solution() = }""")
