


'''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 timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A :  List[str]    							=				logging.get_logger(__name__)







def     UpperCamelCase_      (   A__ :       Optional[int] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       """huggingface/label-files"""
  lowerCAmelCase_      :				Tuple      =       """imagenet-1k-id2label.json"""
  lowerCAmelCase_      :				Dict      =       json.load(open(hf_hub_download(A__							,   A__							,   repo_type="""dataset""" )							,   """r""" ) )
  lowerCAmelCase_      :				Tuple      =       {int(A__ ): v for k, v in idalabel.items()}
  lowerCAmelCase_      :				Dict      =       {v: k for k, v in idalabel.items()}

  lowerCAmelCase_      :				Tuple      =       """std_conv""" if """bit""" in model_name else False

  # note that when using BiT as backbone for ViT-hybrid checkpoints,
  # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
  # config.conv_layer = "std_conv_same"
  lowerCAmelCase_      :				Optional[int]      =       BitConfig(
      conv_layer=A__							,   num_labels=10_00							,   idalabel=A__							,   labelaid=A__							,   )

  return config







def     UpperCamelCase_      (   A__ :       Optional[Any] ):
  '''simple docstring'''





  if "stem.conv" in name:
    lowerCAmelCase_      :				str      =       name.replace("""stem.conv"""							,   """bit.embedder.convolution""" )
  if "blocks" in name:
    lowerCAmelCase_      :				List[Any]      =       name.replace("""blocks"""							,   """layers""" )
  if "head.fc" in name:
    lowerCAmelCase_      :				int      =       name.replace("""head.fc"""							,   """classifier.1""" )
  if name.startswith("""norm""" ):
    lowerCAmelCase_      :				str      =       """bit.""" + name
  if "bit" not in name and "classifier" not in name:
    lowerCAmelCase_      :				List[str]      =       """bit.encoder.""" + name

  return name







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				int      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowerCAmelCase_      :				str      =       Image.open(requests.get(A__							,   stream=A__ ).raw )
  return im







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       str							,   A__ :       List[Any]=False ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       get_config(A__ )

  # load original model from timm
  lowerCAmelCase_      :				Any      =       create_model(A__							,   pretrained=A__ )
  timm_model.eval()

  # load state_dict of original model
  lowerCAmelCase_      :				Dict      =       timm_model.state_dict()
  for key in state_dict.copy().keys():
    lowerCAmelCase_      :				Tuple      =       state_dict.pop(A__ )
    lowerCAmelCase_      :				Optional[int]      =       val.squeeze() if """head""" in key else val

  # load HuggingFace model
  lowerCAmelCase_      :				List[str]      =       BitForImageClassification(A__ )
  model.eval()
  model.load_state_dict(A__ )

  # create image processor
  lowerCAmelCase_      :				Union[str, Any]      =       create_transform(**resolve_data_config({}							,   model=A__ ) )
  lowerCAmelCase_      :				List[Any]      =       transform.transforms

  lowerCAmelCase_      :				Optional[int]      =       {
      """bilinear""": PILImageResampling.BILINEAR,
      """bicubic""": PILImageResampling.BICUBIC,
      """nearest""": PILImageResampling.NEAREST,
  }

  lowerCAmelCase_      :				Any      =       BitImageProcessor(
      do_resize=A__							,   size={"""shortest_edge""": timm_transforms[0].size}							,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]							,   do_center_crop=A__							,   crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}							,   do_normalize=A__							,   image_mean=timm_transforms[-1].mean.tolist()							,   image_std=timm_transforms[-1].std.tolist()							,   )

  lowerCAmelCase_      :				Optional[Any]      =       prepare_img()
  lowerCAmelCase_      :				Union[str, Any]      =       transform(A__ ).unsqueeze(0 )
  lowerCAmelCase_      :				Optional[Any]      =       processor(A__							,   return_tensors="""pt""" ).pixel_values

  # verify pixel values
  assert torch.allclose(A__							,   A__ )

  # verify logits
  with torch.no_grad():
    lowerCAmelCase_      :				int      =       model(A__ )
    lowerCAmelCase_      :				Dict      =       outputs.logits

  print("""Logits:"""							,   logits[0, :3] )
  print("""Predicted class:"""							,   model.config.idalabel[logits.argmax(-1 ).item()] )
  lowerCAmelCase_      :				List[Any]      =       timm_model(A__ )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(A__							,   outputs.logits							,   atol=1E-3 )
  print("""Looks ok!""" )

  if pytorch_dump_folder_path is not None:
    Path(A__ ).mkdir(exist_ok=A__ )
    print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}' )
    model.save_pretrained(A__ )
    processor.save_pretrained(A__ )

  if push_to_hub:
    print(f'Pushing model {model_name} and processor to the hub' )
    model.push_to_hub(f'ybelkada/{model_name}' )
    processor.push_to_hub(f'ybelkada/{model_name}' )


if __name__ == "__main__":
  __A :  str    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="resnetv2_50x1_bitm",
      type=str,
      help="Name of the BiT 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."
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
      help="Whether to push the model to the hub.",
  )

  __A :  Optional[Any]    							=				parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''






from __future__ import annotations







def     UpperCamelCase_      (   A__ :       int | float | str							,   A__ :       int | float | str ):
  '''simple docstring'''





  if nth_term == "":
    return [""]
  lowerCAmelCase_      :				str      =       int(A__ )
  lowerCAmelCase_      :				Tuple      =       int(A__ )
  lowerCAmelCase_      :				list[str]      =       []
  for temp in range(int(A__ ) ):
    series.append(f'1 / {pow(temp + 1							,   int(A__ ) )}' if series else """1""" )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A :  str    							=				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))



