
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:Optional[Any]							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_		,   a_=False						)		->	List[str]:







				"""simple docstring"""



				__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"),
								    ]						)

								# 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						)		->	str:







				"""simple docstring"""



				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_						)		->	Optional[int]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= dct.pop(a_						)
				__A   			= val


def 						UpperCAmelCase			(	)		->	str:







				"""simple docstring"""



				__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_=True						)		->	List[Any]:







				"""simple docstring"""



				__A   			= ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								__A   			= 8
				# set labels if required
				if not base_model:
								__A   			= 1_0_0_0
								__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()}
				# size of the architecture
				if model_name in ["dino_vits8", "dino_vits16"]:
								__A   			= 3_8_4
								__A   			= 1_5_3_6
								__A   			= 1_2
								__A   			= 6

				# load original model from torch hub
				__A   			= torch.hub.load("facebookresearch/dino:main"		,   a_						)
				original_model.eval()

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

				# load HuggingFace model
				if base_model:
								__A   			= ViTModel(a_		,   add_pooling_layer=a_						).eval()
				else:
								__A   			= ViTForImageClassification(a_						).eval()
				model.load_state_dict(a_						)

				# Check outputs on an image, prepared by ViTImageProcessor
				__A   			= ViTImageProcessor()
				__A   			= image_processor(images=prepare_img()		,   return_tensors="pt"						)
				__A   			= encoding["pixel_values"]
				__A   			= model(a_						)

				if base_model:
								__A   			= original_model(a_						)
								assert torch.allclose(a_		,   outputs.last_hidden_state[:, 0, :]		,   atol=1E-1						)
				else:
								__A   			= original_model(a_						)
								assert logits.shape == outputs.logits.shape
								assert torch.allclose(a_		,   outputs.logits		,   atol=1E-3						)

				Path(a_						).mkdir(exist_ok=a_						)
				print(F'''Saving model {model_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__":
					SCREAMING_SNAKE_CASE							:List[str]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='dino_vitb16',
					    type=str,
					    help='Name of the model trained with DINO you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--base_model',
					    action='store_true',
					    help='Whether to only convert the base model (no projection head weights).',
					)

					parser.set_defaults(base_model=True)
					SCREAMING_SNAKE_CASE							:Tuple							 =				parser.parse_args()
					convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]						):





  """simple docstring"""



  a__	:					Optional[Any]										=len(SCREAMING_SNAKE_CASE						)
  while cur > 1:
    # Find the maximum number in arr
    a__	:					List[Any]										=arr.index(max(arr[0:cur]						)						)
    # Reverse from 0 to mi
    a__	:					int										=arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE						)]
    # Reverse whole list
    a__	:					List[str]										=arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE						)]
    cur -= 1
  return arr


if __name__ == "__main__":
     UpperCAmelCase	:	int		     =       input("""Enter numbers separated by a comma:\n""").strip()
     UpperCAmelCase	:	Optional[int]		     =       [int(item) for item in user_input.split(""",""")]
     print(pancake_sort(unsorted))






