import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase							   =      logging.get_logger(__name__)







def 	UpperCAmelCase       (		lowerCAmelCase_       )	->	Any:
     a  :    Tuple				=						MobileNetVaConfig(layer_norm_eps=0.0_01       )

     if "quant" in model_name:
          raise ValueError('''Quantized models are not supported.'''       )

     a  :    Any				=						re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''						,      lowerCAmelCase_       )
     if matches:
          a  :    List[Any]				=						float(matches[1]       )
          a  :    int				=						int(matches[2]       )

     if model_name.startswith('''deeplabv3_'''       ):
          a  :    Optional[Any]				=						8
          a  :    str				=						2_1
          a  :    Tuple				=						'''pascal-voc-id2label.json'''
     else:
          # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
          # of the usual 1000. The first class (index 0) is "background".
          a  :    str				=						1_0_0_1
          a  :    Tuple				=						'''imagenet-1k-id2label.json'''

     a  :    int				=						'''huggingface/label-files'''
     a  :    Tuple				=						json.load(open(hf_hub_download(lowerCAmelCase_						,      lowerCAmelCase_						,      repo_type='''dataset'''       )						,      '''r'''       )       )

     if config.num_labels == 1_0_0_1:
          a  :    int				=						{int(lowerCAmelCase_       ) + 1: v for k, v in idalabel.items()}
          a  :    int				=						'''background'''
     else:
          a  :    List[Any]				=						{int(lowerCAmelCase_       ): v for k, v in idalabel.items()}

     a  :    str				=						idalabel
     a  :    Dict				=						{v: k for k, v in idalabel.items()}

     return config







def 	UpperCAmelCase       (		)	->	Union[str, Any]:
     a  :    List[str]				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     a  :    Union[str, Any]				=						Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_       ).raw       )
     return im







@torch.no_grad()
def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False       )	->	List[str]:
     a  :    Any				=						get_mobilenet_va_config(lowerCAmelCase_       )

     # Load 🤗 model
     if model_name.startswith('''deeplabv3_'''       ):
          a  :    Any				=						MobileNetVaForSemanticSegmentation(lowerCAmelCase_       ).eval()
     else:
          a  :    Optional[int]				=						MobileNetVaForImageClassification(lowerCAmelCase_       ).eval()

     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_       )

     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     a  :    Tuple				=						MobileNetVaImageProcessor(
         crop_size={'''width''': config.image_size, '''height''': config.image_size}						,      size={'''shortest_edge''': config.image_size + 3_2}						,      )
     a  :    str				=						image_processor(images=prepare_img()						,      return_tensors='''pt'''       )
     a  :    List[str]				=						model(**lowerCAmelCase_       )
     a  :    Optional[int]				=						outputs.logits

     if model_name.startswith('''deeplabv3_'''       ):
          assert logits.shape == (1, 2_1, 6_5, 6_5)

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               a  :    str				=						torch.tensor(
                   [
                       [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                       [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
                       [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
                   ]       )

          else:
               raise ValueError(f"""Unknown model name: {model_name}"""       )

          assert torch.allclose(logits[0, :3, :3, :3]						,      lowerCAmelCase_						,      atol=1e-4       )
     else:
          assert logits.shape == (1, 1_0_0_1)

          if model_name == "mobilenet_v2_1.4_224":
               a  :    Dict				=						torch.tensor([0.01_81, -1.00_15, 0.46_88]       )
          elif model_name == "mobilenet_v2_1.0_224":
               a  :    str				=						torch.tensor([0.24_45, -1.19_93, 0.19_05]       )
          elif model_name == "mobilenet_v2_0.75_160":
               a  :    Union[str, Any]				=						torch.tensor([0.24_82, 0.41_36, 0.66_69]       )
          elif model_name == "mobilenet_v2_0.35_96":
               a  :    Union[str, Any]				=						torch.tensor([0.14_51, -0.46_24, 0.71_92]       )
          else:
               a  :    int				=						None

          if expected_logits is not None:
               assert torch.allclose(logits[0, :3]						,      lowerCAmelCase_						,      atol=1e-4       )

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

     if push_to_hub:
          print('''Pushing to the hub...'''       )
          a  :    Union[str, Any]				=						'''google/''' + model_name
          image_processor.push_to_hub(lowerCAmelCase_       )
          model.push_to_hub(lowerCAmelCase_       )


if __name__ == "__main__":
  UpperCAmelCase							   =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""mobilenet_v2_1.0_224""",
      type=str,
      help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.""",
  )
  parser.add_argument(
      """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  UpperCAmelCase							   =      parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
from __future__ import annotations







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )	->	list[float | int]:
     if not all((nth_term, start_term_a, common_ratio_r)       ):
          return []
     a  :    list[float | int]				=						[]
     a  :    str				=						1
     a  :    Dict				=						common_ratio_r
     for _ in range(int(lowerCAmelCase_       )       ):
          if not series:
               series.append(lowerCAmelCase_       )
          else:
               power += 1
               series.append(float(start_term_a * multiple       )       )
               a  :    Dict				=						pow(float(lowerCAmelCase_       )						,      lowerCAmelCase_       )
     return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase							   =      float(input("""Enter the last number (n term) of the Geometric Series"""))
  UpperCAmelCase							   =      float(input("""Enter the starting term (a) of the Geometric Series"""))
  UpperCAmelCase							   =      float(
      input("""Enter the common ratio between two terms (r) of the Geometric Series""")
  )
  print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
  print(geometric_series(nth_term, start_term_a, common_ratio_r))