


'''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
       )



'''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."