


'''simple docstring'''




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()
__snake_case       		=logging.get_logger(__name__)





def       a_   (					lowerCamelCase      :	Dict							):
   lowerCAmelCase	=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

   lowerCAmelCase	=     re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'      ,					_lowercase							)
   if matches:
      lowerCAmelCase	=     float(matches[1]							)
      lowerCAmelCase	=     int(matches[2]							)

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

   return config





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





@torch.no_grad()
def       a_   (					lowerCamelCase      :	Union[str, Any]      ,					lowerCamelCase      :	Union[str, Any]      ,					lowerCamelCase      :	int      ,					lowerCamelCase      :	List[Any]=False							):
   lowerCAmelCase	=     get_mobilenet_va_config(_lowercase							)

   # Load 🤗 model
   lowerCAmelCase	=     MobileNetVaForImageClassification(_lowercase							).eval()

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

   # Check outputs on an image, prepared by MobileNetV1ImageProcessor
   lowerCAmelCase	=     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size}      ,					size={'shortest_edge': config.image_size + 32}      ,					)
   lowerCAmelCase	=     image_processor(images=prepare_img()      ,					return_tensors='pt'							)
   lowerCAmelCase	=     model(**_lowercase							)
   lowerCAmelCase	=     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      lowerCAmelCase	=     torch.tensor([-4.1_739, -1.1_233, 3.1_205]							)
   elif model_name == "mobilenet_v1_0.75_192":
      lowerCAmelCase	=     torch.tensor([-3.9_440, -2.3_141, -0.3_333]							)
   else:
      lowerCAmelCase	=     None

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

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

   if push_to_hub:
      print('Pushing to the hub...'							)
      lowerCAmelCase	=     'google/' + model_name
      image_processor.push_to_hub(_lowercase							)
      model.push_to_hub(_lowercase							)


if __name__ == "__main__":
       __snake_case       		=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."""
       )

       __snake_case       		=parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )









from collections.abc import Generator
from math import sin






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     if len(_lowercase ) != 32:
          raise ValueError('Input must be of length 32' )

     __UpperCamelCase =			B''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     if i < 0:
          raise ValueError('Input must be non-negative' )

     __UpperCamelCase =			format(_lowercase					,  '08x' )[-8:]
     __UpperCamelCase =			B''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
     return little_endian_hex






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     __UpperCamelCase =			B''
     for char in message:
          bit_string += format(_lowercase					,  '08b' ).encode('utf-8' )
     __UpperCamelCase =			format(len(_lowercase )					,  '064b' ).encode('utf-8' )

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(_lowercase ) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

     return bit_string






def 		_A		(		_lowercase )					-> Generator[list[int], None, None]:


     """simple docstring"""


     if len(_lowercase ) % 5_12 != 0:
          raise ValueError('Input must have length that\'s a multiple of 512' )

     for pos in range(0					,  len(_lowercase )					,  5_12 ):
          __UpperCamelCase =			bit_string[pos : pos + 5_12]
          __UpperCamelCase =			[]
          for i in range(0					,  5_12					,  32 ):
               block_words.append(int(to_little_endian(block[i : i + 32] )					,  2 ) )
          yield block_words






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     if i < 0:
          raise ValueError('Input must be non-negative' )

     __UpperCamelCase =			format(_lowercase					,  '032b' )
     __UpperCamelCase =			''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(_lowercase					,  2 )






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (a + b) % 2**32






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     if i < 0:
          raise ValueError('Input must be non-negative' )
     if shift < 0:
          raise ValueError('Shift must be non-negative' )
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 		_A		(		_lowercase )					-> bytes:


     """simple docstring"""


     __UpperCamelCase =			preprocess(_lowercase )

     __UpperCamelCase =			[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

     # Starting states
     __UpperCamelCase =			0X67_45_23_01
     __UpperCamelCase =			0Xef_cd_ab_89
     __UpperCamelCase =			0X98_ba_dc_fe
     __UpperCamelCase =			0X10_32_54_76

     __UpperCamelCase =			[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(_lowercase ):
          __UpperCamelCase =			aa
          __UpperCamelCase =			ba
          __UpperCamelCase =			ca
          __UpperCamelCase =			da

          # Hash current chunk
          for i in range(64 ):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    __UpperCamelCase =			d ^ (b & (c ^ d))
                    __UpperCamelCase =			i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    __UpperCamelCase =			c ^ (d & (b ^ c))
                    __UpperCamelCase =			(5 * i + 1) % 16
               elif i <= 47:
                    __UpperCamelCase =			b ^ c ^ d
                    __UpperCamelCase =			(3 * i + 5) % 16
               else:
                    __UpperCamelCase =			c ^ (b | not_aa(_lowercase ))
                    __UpperCamelCase =			(7 * i) % 16
               __UpperCamelCase =			(f + a + added_consts[i] + block_words[g]) % 2**32
               __UpperCamelCase =			d
               __UpperCamelCase =			c
               __UpperCamelCase =			b
               __UpperCamelCase =			sum_aa(_lowercase					,  left_rotate_aa(_lowercase					,  shift_amounts[i] ) )

          # Add hashed chunk to running total
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )
          __UpperCamelCase =			sum_aa(_lowercase					,  _lowercase )

     __UpperCamelCase =			reformat_hex(_lowercase ) + reformat_hex(_lowercase ) + reformat_hex(_lowercase ) + reformat_hex(_lowercase )
     return digest


if __name__ == "__main__":
    import doctest

    doctest.testmod()


