code: 
  
  
  '''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,
      MobileNetVaForSemanticSegmentation,
      MobileNetVaImageProcessor,
      load_tf_weights_in_mobilenet_va,
  )
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  __lowercase:		Optional[int]							=     logging.get_logger(__name__)
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						MobileNetVaConfig(layer_norm_eps=0.0_0_1   )
  
     if "quant" in model_name:
        raise ValueError("Quantized models are not supported."   )
  
     UpperCamelCase__ 						=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"    ,		_UpperCamelCase   )
     if matches:
        UpperCamelCase__ 						=						float(matches[1]   )
        UpperCamelCase__ 						=						int(matches[2]   )
  
     if model_name.startswith("deeplabv3_"   ):
        UpperCamelCase__ 						=						8
        UpperCamelCase__ 						=						21
        UpperCamelCase__ 						=						"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".
        UpperCamelCase__ 						=						10_01
        UpperCamelCase__ 						=						"imagenet-1k-id2label.json"
  
     UpperCamelCase__ 						=						"huggingface/label-files"
     UpperCamelCase__ 						=						json.load(open(hf_hub_download(_UpperCamelCase    ,		_UpperCamelCase    ,		repo_type="dataset"   )    ,		"r"   )   )
  
     if config.num_labels == 10_01:
        UpperCamelCase__ 						=						{int(_UpperCamelCase   ) + 1: v for k, v in idalabel.items()}
        UpperCamelCase__ 						=						"background"
     else:
        UpperCamelCase__ 						=						{int(_UpperCamelCase   ): v for k, v in idalabel.items()}
  
     UpperCamelCase__ 						=						idalabel
     UpperCamelCase__ 						=						{v: k for k, v in idalabel.items()}
  
     return config
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					Dict:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__ 						=						Image.open(requests.get(_UpperCamelCase    ,		stream=_UpperCamelCase   ).raw   )
     return im
  
  
  
  @torch.no_grad()
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Optional[int]=False   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						get_mobilenet_va_config(_UpperCamelCase   )
  
     # Load 🤗 model
     if model_name.startswith("deeplabv3_"   ):
        UpperCamelCase__ 						=						MobileNetVaForSemanticSegmentation(_UpperCamelCase   ).eval()
     else:
        UpperCamelCase__ 						=						MobileNetVaForImageClassification(_UpperCamelCase   ).eval()
  
     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     UpperCamelCase__ 						=						MobileNetVaImageProcessor(
         crop_size={"width": config.image_size, "height": config.image_size}    ,		size={"shortest_edge": config.image_size + 32}    ,		)
     UpperCamelCase__ 						=						image_processor(images=prepare_img()    ,		return_tensors="pt"   )
     UpperCamelCase__ 						=						model(**_UpperCamelCase   )
     UpperCamelCase__ 						=						outputs.logits
  
     if model_name.startswith("deeplabv3_"   ):
        assert logits.shape == (1, 21, 65, 65)
  
        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
           UpperCamelCase__ 						=						torch.tensor(
               [
                   [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                   [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                   [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
               ]   )
  
        else:
           raise ValueError(F'Unknown model name: {model_name}'   )
  
        assert torch.allclose(logits[0, :3, :3, :3]    ,		_UpperCamelCase    ,		atol=1e-4   )
     else:
        assert logits.shape == (1, 10_01)
  
        if model_name == "mobilenet_v2_1.4_224":
           UpperCamelCase__ 						=						torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]   )
        elif model_name == "mobilenet_v2_1.0_224":
           UpperCamelCase__ 						=						torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]   )
        elif model_name == "mobilenet_v2_0.75_160":
           UpperCamelCase__ 						=						torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]   )
        elif model_name == "mobilenet_v2_0.35_96":
           UpperCamelCase__ 						=						torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]   )
        else:
           UpperCamelCase__ 						=						None
  
        if expected_logits is not None:
           assert torch.allclose(logits[0, :3]    ,		_UpperCamelCase    ,		atol=1e-4   )
  
     Path(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
     print(F'Saving model {model_name} to {pytorch_dump_folder_path}'   )
     model.save_pretrained(_UpperCamelCase   )
     print(F'Saving image processor to {pytorch_dump_folder_path}'   )
     image_processor.save_pretrained(_UpperCamelCase   )
  
     if push_to_hub:
        print("Pushing to the hub..."   )
        UpperCamelCase__ 						=						"google/" + model_name
        image_processor.push_to_hub(_UpperCamelCase   )
        model.push_to_hub(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
         __lowercase:		Dict							=     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."
         )
  
         __lowercase:		Union[str, Any]							=     parser.parse_args()
         convert_movilevit_checkpoint(
             args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
         )
style_context: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."