

'''simple docstring'''




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()
_a					:			List[Any]							=    logging.get_logger(__name__)


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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any    ):
  UpperCAmelCase    				=						['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : str    ):
  UpperCAmelCase    				=						dct.pop(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						val


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


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Union[str, Any]=True    ):
  UpperCAmelCase    				=						ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    UpperCAmelCase    				=						8
  # set labels if required
  if not base_model:
    UpperCAmelCase    				=						1000
    UpperCAmelCase    				=						'huggingface/label-files'
    UpperCAmelCase    				=						'imagenet-1k-id2label.json'
    UpperCAmelCase    				=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, repo_type='dataset'    )							, 'r'    )    )
    UpperCAmelCase    				=						{int(SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
    UpperCAmelCase    				=						idalabel
    UpperCAmelCase    				=						{v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    UpperCAmelCase    				=						384
    UpperCAmelCase    				=						1536
    UpperCAmelCase    				=						12
    UpperCAmelCase    				=						6

  # load original model from torch hub
  UpperCAmelCase    				=						torch.hub.load('facebookresearch/dino:main'							, SCREAMING_SNAKE_CASE    )
  original_model.eval()

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

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

  # Check outputs on an image, prepared by ViTImageProcessor
  UpperCAmelCase    				=						ViTImageProcessor()
  UpperCAmelCase    				=						image_processor(images=prepare_img()							, return_tensors='pt'    )
  UpperCAmelCase    				=						encoding['pixel_values']
  UpperCAmelCase    				=						model(SCREAMING_SNAKE_CASE    )

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

  Path(SCREAMING_SNAKE_CASE    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE    )
  print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''    )
  model.save_pretrained(SCREAMING_SNAKE_CASE    )
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
  image_processor.save_pretrained(SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  _a					:			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)
  _a					:			str							=    parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





'''simple docstring'''




from __future__ import annotations


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str							, SCREAMING_SNAKE_CASE   : int | float | str    ):
  if nth_term == "":
    return [""]
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						[]
  for temp in range(int(SCREAMING_SNAKE_CASE    )    ):
    series.append(f'''1 / {pow(temp + 1							, int(SCREAMING_SNAKE_CASE    )    )}''' if series else '1'    )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  _a					:			List[Any]							=    int(input('Enter the last number (nth term) of the P-Series'))
  _a					:			Tuple							=    int(input('Enter the power for  P-Series'))
  print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
  print(p_series(nth_term, power))


