






'''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 							lowercase_				(    __A :       str					,      __A :       Any=False			)       ->      Dict:



 """simple docstring"""




 lowercase					:   Tuple				  =[]
 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					:   Dict				  =[(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 							lowercase_				(    __A :       Tuple					,      __A :       str					,      __A :       Tuple=False			)       ->      int:



 """simple docstring"""




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


def 							lowercase_				(    __A :       str			)       ->      Dict:



 """simple docstring"""




 lowercase					:   int				  =['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(__A					,      __A			)


def 							lowercase_				(    __A :       Optional[Any]					,      __A :       str					,      __A :       Any			)       ->      Any:



 """simple docstring"""




 lowercase					:   Tuple				  =dct.pop(__A			)
 lowercase					:   Any				  =val


def 							lowercase_				(    )       ->      List[str]:



 """simple docstring"""




 lowercase					:   Union[str, Any]				  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowercase					:   List[Any]				  =Image.open(requests.get(__A					,      stream=__A			).raw			)
 return im


@torch.no_grad()
def 							lowercase_				(    __A :       Tuple					,      __A :       str			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   str				  =ViTConfig()
 lowercase					:   Optional[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					:   Union[str, Any]				  =int(vit_name[-1_2:-1_0]			)
  lowercase					:   Tuple				  =int(vit_name[-9:-6]			)
 else:
  lowercase					:   Dict				  =1_0_0_0
  lowercase					:   int				  ='''huggingface/label-files'''
  lowercase					:   Optional[Any]				  ='''imagenet-1k-id2label.json'''
  lowercase					:   Any				  =json.load(open(hf_hub_download(__A					,      __A					,      repo_type='''dataset'''			)					,      '''r'''			)			)
  lowercase					:   List[str]				  ={int(__A			): v for k, v in idalabel.items()}
  lowercase					:   List[Any]				  =idalabel
  lowercase					:   Tuple				  ={v: k for k, v in idalabel.items()}
  lowercase					:   Optional[int]				  =int(vit_name[-6:-4]			)
  lowercase					:   int				  =int(vit_name[-3:]			)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith('''tiny'''			):
   lowercase					:   str				  =1_9_2
   lowercase					:   List[str]				  =7_6_8
   lowercase					:   int				  =1_2
   lowercase					:   List[str]				  =3
  elif vit_name[9:].startswith('''small'''			):
   lowercase					:   Tuple				  =3_8_4
   lowercase					:   Tuple				  =1_5_3_6
   lowercase					:   Dict				  =1_2
   lowercase					:   int				  =6
  else:
   pass
 else:
  if vit_name[4:].startswith('''small'''			):
   lowercase					:   Union[str, Any]				  =7_6_8
   lowercase					:   Optional[int]				  =2_3_0_4
   lowercase					:   List[Any]				  =8
   lowercase					:   Union[str, Any]				  =8
  elif vit_name[4:].startswith('''base'''			):
   pass
  elif vit_name[4:].startswith('''large'''			):
   lowercase					:   Optional[Any]				  =1_0_2_4
   lowercase					:   List[Any]				  =4_0_9_6
   lowercase					:   Union[str, Any]				  =2_4
   lowercase					:   int				  =1_6
  elif vit_name[4:].startswith('''huge'''			):
   lowercase					:   Dict				  =1_2_8_0
   lowercase					:   int				  =5_1_2_0
   lowercase					:   Optional[int]				  =3_2
   lowercase					:   int				  =1_6

    # load original model from timm
 lowercase					:   int				  =timm.create_model(__A					,      pretrained=__A			)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowercase					:   Optional[int]				  =timm_model.state_dict()
 if base_model:
  remove_classification_head_(__A			)
 lowercase					:   Union[str, Any]				  =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":
  lowercase					:   Tuple				  =ViTModel(__A			).eval()
 else:
  lowercase					:   Union[str, Any]				  =ViTForImageClassification(__A			).eval()
 model.load_state_dict(__A			)

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

 if base_model:
  lowercase					:   str				  =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:
  lowercase					:   str				  =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__":
     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 __future__ import annotations

import math


def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 lowercase					:   str				  =u
 for i in range(1					,      __A			):
  lowercase					:   Any				  =temp * (u - i)
 return temp


def 							lowercase_				(    )       ->      None:



 """simple docstring"""




 lowercase					:   List[str]				  =int(input('''enter the numbers of values: '''			)			)
 lowercase					:   list[list[float]]				  =[]
 for _ in range(__A			):
  y.append([]			)
 for i in range(__A			):
  for j in range(__A			):
   y[i].append(__A			)
   lowercase					:   List[Any]				  =0

 print('''enter the values of parameters in a list: '''			)
 lowercase					:   Optional[int]				  =list(map(__A					,      input().split()			)			)

 print('''enter the values of corresponding parameters: '''			)
 for i in range(__A			):
  lowercase					:   str				  =float(input()			)

 lowercase					:   int				  =int(input('''enter the value to interpolate: '''			)			)
 lowercase					:   Union[str, Any]				  =(value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1					,      __A			):
  for j in range(n - i			):
   lowercase					:   str				  =y[j + 1][i - 1] - y[j][i - 1]

 lowercase					:   Any				  =y[0][0]
 for i in range(1					,      __A			):
  summ += (ucal(__A					,      __A			) * y[0][i]) / math.factorial(__A			)

 print(F'the value at {value} is {summ}'			)


if __name__ == "__main__":
      main()



