






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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_		:						Union[str, Any]             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase=False):
			SCREAMING_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'''deit.encoder.layer.{i}.layernorm_before.weight'''))
						rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias'''))
						rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight'''))
						rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias'''))
						rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight'''))
						rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias'''))
						rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight'''))
						rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias'''))
						rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight'''))
						rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias'''))

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ('cls_token', 'deit.embeddings.cls_token'),
			        ('dist_token', 'deit.embeddings.distillation_token'),
			        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
			        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
			        ('pos_embed', 'deit.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 "deit" from all keys that start with "deit"
						SCREAMING_SNAKE_CASE						= [(pair[0], pair[1][4:]) if pair[1].startswith('deit') else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ('norm.weight', 'deit.layernorm.weight'),
						        ('norm.bias', 'deit.layernorm.bias'),
						        ('head.weight', 'cls_classifier.weight'),
						        ('head.bias', 'cls_classifier.bias'),
						        ('head_dist.weight', 'distillation_classifier.weight'),
						        ('head_dist.bias', 'distillation_classifier.bias'),
						    ])

			return rename_keys






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






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= dct.pop(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= val






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE						= Image.open(requests.get(_UpperCAmelCase							,       stream=_UpperCAmelCase).raw)
			return im






@torch.no_grad()
def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= DeiTConfig()
			# all deit models have fine-tuned heads
			SCREAMING_SNAKE_CASE						= False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			SCREAMING_SNAKE_CASE						= 1000
			SCREAMING_SNAKE_CASE						= 'huggingface/label-files'
			SCREAMING_SNAKE_CASE						= 'imagenet-1k-id2label.json'
			SCREAMING_SNAKE_CASE						= json.load(open(hf_hub_download(_UpperCAmelCase							,       _UpperCAmelCase							,       repo_type='dataset')							,       'r'))
			SCREAMING_SNAKE_CASE						= {int(_UpperCAmelCase): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE						= idalabel
			SCREAMING_SNAKE_CASE						= {v: k for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE						= int(deit_name[-6:-4])
			SCREAMING_SNAKE_CASE						= int(deit_name[-3:])
			# size of the architecture
			if deit_name[9:].startswith('tiny'):
						SCREAMING_SNAKE_CASE						= 192
						SCREAMING_SNAKE_CASE						= 768
						SCREAMING_SNAKE_CASE						= 12
						SCREAMING_SNAKE_CASE						= 3
			elif deit_name[9:].startswith('small'):
						SCREAMING_SNAKE_CASE						= 384
						SCREAMING_SNAKE_CASE						= 1536
						SCREAMING_SNAKE_CASE						= 12
						SCREAMING_SNAKE_CASE						= 6
			if deit_name[9:].startswith('base'):
						pass
			elif deit_name[4:].startswith('large'):
						SCREAMING_SNAKE_CASE						= 1024
						SCREAMING_SNAKE_CASE						= 4096
						SCREAMING_SNAKE_CASE						= 24
						SCREAMING_SNAKE_CASE						= 16

			# load original model from timm
			SCREAMING_SNAKE_CASE						= timm.create_model(_UpperCAmelCase							,       pretrained=_UpperCAmelCase)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			SCREAMING_SNAKE_CASE						= timm_model.state_dict()
			SCREAMING_SNAKE_CASE						= 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
			SCREAMING_SNAKE_CASE						= DeiTForImageClassificationWithTeacher(_UpperCAmelCase).eval()
			model.load_state_dict(_UpperCAmelCase)

			# Check outputs on an image, prepared by DeiTImageProcessor
			SCREAMING_SNAKE_CASE						= int(
			    (256 / 224) * config.image_size)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			SCREAMING_SNAKE_CASE						= DeiTImageProcessor(size=_UpperCAmelCase							,       crop_size=config.image_size)
			SCREAMING_SNAKE_CASE						= image_processor(images=prepare_img()							,       return_tensors='pt')
			SCREAMING_SNAKE_CASE						= encoding['pixel_values']
			SCREAMING_SNAKE_CASE						= model(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= 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 {deit_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__":
				a_		:						Union[str, Any]             =    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--deit_name',
				    default='vit_deit_base_distilled_patch16_224',
				    type=str,
				    help='Name of the DeiT 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.'
				)

				a_		:						int             =    parser.parse_args()
				convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












import os

# Precomputes a list of the 100 first triangular numbers
a_		:						str             =    [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= os.path.dirname(os.path.realpath(_UpperCAmelCase))
			SCREAMING_SNAKE_CASE						= os.path.join(_UpperCAmelCase							,       'words.txt')

			SCREAMING_SNAKE_CASE						= ''
			with open(_UpperCAmelCase) as f:
						SCREAMING_SNAKE_CASE						= f.readline()

			SCREAMING_SNAKE_CASE						= [word.strip('"') for word in words.strip('\r\n').split(',')]
			SCREAMING_SNAKE_CASE						= [
			    word
			    for word in [sum(ord(_UpperCAmelCase) - 64 for x in word) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase)


if __name__ == "__main__":
				print(solution())




