"""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()
SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=False			):






	"""simple docstring"""





	_lowercase :    int							=							[]
	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"
		_lowercase :    Optional[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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=False			):






	"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[Any]							=							['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(__UpperCAmelCase     ,__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							dct.pop(__UpperCAmelCase			)
	_lowercase :    Dict							=							val


def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    List[Any]							=							'http://images.cocodataset.org/val2017/000000039769.jpg'
	_lowercase :    Tuple							=							Image.open(requests.get(__UpperCAmelCase     ,stream=__UpperCAmelCase			).raw			)
	return im


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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

    # load original model from timm
	_lowercase :    str							=							timm.create_model(__UpperCAmelCase     ,pretrained=__UpperCAmelCase			)
	timm_model.eval()

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		_lowercase :    Optional[int]							=							ViTModel(__UpperCAmelCase			).eval()
	else:
		_lowercase :    List[str]							=							ViTForImageClassification(__UpperCAmelCase			).eval()
	model.load_state_dict(__UpperCAmelCase			)

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

	if base_model:
		_lowercase :    str							=							timm_model.forward_features(__UpperCAmelCase			)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(__UpperCAmelCase     ,outputs.pooler_output     ,atol=1E-3			)
	else:
		_lowercase :    List[Any]							=							timm_model(__UpperCAmelCase			)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__UpperCAmelCase     ,outputs.logits     ,atol=1E-3			)

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						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.'
						)

						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




"""simple docstring"""







from decimal import Decimal, getcontext
from math import ceil, factorial


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			):
		raise TypeError('Undefined for non-integers'			)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'			)

	_lowercase :    Optional[Any]							=							precision
	_lowercase :    Dict							=							ceil(precision / 14			)
	_lowercase :    int							=							426_880 * Decimal(10_005			).sqrt()
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Union[str, Any]							=							13_591_409
	_lowercase :    Optional[int]							=							Decimal(__UpperCAmelCase			)
	for k in range(1     ,__UpperCAmelCase			):
		_lowercase :    List[str]							=							factorial(6 * k			) // (factorial(3 * k			) * factorial(__UpperCAmelCase			) ** 3)
		linear_term += 545_140_134
		exponential_term *= -262_537_412_640_768_000
		partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
	return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						50
						print(f"""The first {n} digits of pi is: {pi(n)}""")



