






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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase	:		int										=   logging.get_logger(__name__)







def 			A__							(      _a  :					Union[str, Any]	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =MobileNetVaConfig(layer_norm_eps=0.0_0_1	)

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

 snake_case__  :    int							       =re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""		,  _a	)
 if matches:
  snake_case__  :    Union[str, Any]							       =float(matches[1]	)
  snake_case__  :    Any							       =int(matches[2]	)

 # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
 # the usual 1000. The first class (index 0) is "background".
 snake_case__  :    List[str]							       =1001
 snake_case__  :    Any							       ="""imagenet-1k-id2label.json"""
 snake_case__  :    List[str]							       ="""huggingface/label-files"""
 snake_case__  :    Tuple							       =json.load(open(hf_hub_download(_a		,  _a		,  repo_type="""dataset"""	)		,  """r"""	)	)
 snake_case__  :    str							       ={int(_a	) + 1: v for k, v in idalabel.items()}
 snake_case__  :    Optional[Any]							       ="""background"""
 snake_case__  :    Tuple							       =idalabel
 snake_case__  :    List[str]							       ={v: k for k, v in idalabel.items()}

 return config







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    int							       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
 snake_case__  :    List[Any]							       =Image.open(requests.get(_a		,  stream=_a	).raw	)
 return im







@torch.no_grad()
def 			A__							(      _a  :					str		,  _a  :					Any		,  _a  :					int		,  _a  :					Optional[Any]=False	):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =get_mobilenet_va_config(_a	)

 # Load 🤗 model
 snake_case__  :    Any							       =MobileNetVaForImageClassification(_a	).eval()

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

 # Check outputs on an image, prepared by MobileNetV1ImageProcessor
 snake_case__  :    Tuple							       =MobileNetVaImageProcessor(
     crop_size={"""width""": config.image_size, """height""": config.image_size}		,  size={"""shortest_edge""": config.image_size + 32}		,  )
 snake_case__  :    Dict							       =image_processor(images=prepare_img()		,  return_tensors="""pt"""	)
 snake_case__  :    Dict							       =model(**_a	)
 snake_case__  :    List[str]							       =outputs.logits

 assert logits.shape == (1, 1001)

 if model_name == "mobilenet_v1_1.0_224":
  snake_case__  :    Optional[int]							       =torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]	)
 elif model_name == "mobilenet_v1_0.75_192":
  snake_case__  :    List[Any]							       =torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]	)
 else:
  snake_case__  :    Dict							       =None

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

 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 push_to_hub:
  print("""Pushing to the hub..."""	)
  snake_case__  :    str							       ="""google/""" + model_name
  image_processor.push_to_hub(_a	)
  model.push_to_hub(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""mobilenet_v1_1.0_224""",
           type=str,
           help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."""
       )

       __lowerCamelCase	:		Any										=   parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )













from string import ascii_uppercase

__lowerCamelCase	:		List[Any]										=   {str(ord(c) - 55): c for c in ascii_uppercase}







def 			A__							(      _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if isinstance(_a		,  _a	):
  raise TypeError("""int() can't convert non-string with explicit base"""	)
 if num < 0:
  raise ValueError("""parameter must be positive int"""	)
 if isinstance(_a		,  _a	):
  raise TypeError("""'str' object cannot be interpreted as an integer"""	)
 if isinstance(_a		,  _a	):
  raise TypeError("""'float' object cannot be interpreted as an integer"""	)
 if base in (0, 1):
  raise ValueError("""base must be >= 2"""	)
 if base > 36:
  raise ValueError("""base must be <= 36"""	)
 snake_case__  :    Union[str, Any]							       =""""""
 snake_case__  :    List[str]							       =0
 snake_case__  :    List[Any]							       =0
 while div != 1:
  snake_case__       ,   snake_case__  :    Union[str, Any]							       =divmod(_a		,  _a	)
  if base >= 11 and 9 < mod < 36:
   snake_case__  :    Dict							       =ALPHABET_VALUES[str(_a	)]
  else:
   snake_case__  :    Optional[Any]							       =str(_a	)
  new_value += actual_value
  snake_case__  :    Dict							       =num // base
  snake_case__  :    Tuple							       =div
  if div == 0:
   return str(new_value[::-1]	)
  elif div == 1:
   new_value += str(_a	)
   return str(new_value[::-1]	)

 return new_value[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for base in range(2, 37):
              for num in range(10_00):
                     assert int(decimal_to_any(num, base), base) == num, (
                         num,
                         base,
                         decimal_to_any(num, base),
                         int(decimal_to_any(num, base), base),
                     )





