


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()
A      :    Any		=					logging.get_logger(__name__)

def       lowercase_		(		_A			:    List[str]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  MobileNetVaConfig(layer_norm_eps=0.001						)

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

      lowerCamelCase__		:				List[str]    =  re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"    ,					_A						)
      if matches:
            lowerCamelCase__		:				Union[str, Any]    =  float(matches[1]						)
            lowerCamelCase__		:				Dict    =  int(matches[2]						)

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

      return config

def       lowercase_		(		):

      """simple docstring"""




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

@torch.no_grad()
def       lowercase_		(		_A			:    str    ,					_A			:    Optional[int]    ,					_A			:    List[str]    ,					_A			:    Any=False						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  get_mobilenet_va_config(_A						)

      # Load 🤗 model
      lowerCamelCase__		:				Dict    =  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
      lowerCamelCase__		:				List[str]    =  MobileNetVaImageProcessor(
          crop_size={"width": config.image_size, "height": config.image_size}    ,					size={"shortest_edge": config.image_size + 32}    ,					)
      lowerCamelCase__		:				int    =  image_processor(images=prepare_img()    ,					return_tensors="pt"						)
      lowerCamelCase__		:				int    =  model(**_A						)
      lowerCamelCase__		:				Any    =  outputs.logits

      assert logits.shape == (1, 1001)

      if model_name == "mobilenet_v1_1.0_224":
            lowerCamelCase__		:				str    =  torch.tensor([-4.1_739, -1.1_233, 3.1_205]						)
      elif model_name == "mobilenet_v1_0.75_192":
            lowerCamelCase__		:				List[Any]    =  torch.tensor([-3.9_440, -2.3_141, -0.3_333]						)
      else:
            lowerCamelCase__		:				Optional[Any]    =  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..."						)
            lowerCamelCase__		:				Union[str, Any]    =  "google/" + model_name
            image_processor.push_to_hub(_A						)
            model.push_to_hub(_A						)


if __name__ == "__main__":
    A      :    Tuple		=					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."
    )

    A      :    Optional[Any]		=					parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )






import math
import os
import sys

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  ""
      try:
            with open(_A    ,					"rb"						) as binary_file:
                  lowerCamelCase__		:				Union[str, Any]    =  binary_file.read()
            for dat in data:
                  lowerCamelCase__		:				Optional[Any]    =  F"{dat:08b}"
                  result += curr_byte
            return result
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    dict[str, str]    ,					_A			:    str    ,					_A			:    int    ,					_A			:    str						):

      """simple docstring"""




      lexicon.pop(_A						)
      lowerCamelCase__		:				List[Any]    =  last_match_id

      if math.loga(_A						).is_integer():
            for curr_key in lexicon:
                  lowerCamelCase__		:				Any    =  "0" + lexicon[curr_key]

      lowerCamelCase__		:				Tuple    =  bin(_A						)[2:]

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  {"0": "0", "1": "1"}
      lowerCamelCase__    ,   lowerCamelCase__		:				int    =  "", ""
      lowerCamelCase__		:				int    =  len(_A						)

      for i in range(len(_A						)						):
            curr_string += data_bits[i]
            if curr_string not in lexicon:
                  continue

            lowerCamelCase__		:				Optional[Any]    =  lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(_A    ,					_A    ,					_A    ,					_A						)
            index += 1
            lowerCamelCase__		:				Dict    =  ""

      while curr_string != "" and curr_string not in lexicon:
            curr_string += "0"

      if curr_string != "":
            lowerCamelCase__		:				int    =  lexicon[curr_string]
            result += last_match_id

      return result

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  os.path.getsize(_A						)
      lowerCamelCase__		:				Dict    =  bin(_A						)[2:]
      lowerCamelCase__		:				List[Any]    =  len(_A						)

      return "0" * (length_length - 1) + file_length_binary + compressed

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  8
      try:
            with open(_A    ,					"wb"						) as opened_file:
                  lowerCamelCase__		:				Any    =  [
                      to_write[i : i + byte_length]
                      for i in range(0    ,					len(_A						)    ,					_A						)
                  ]

                  if len(result_byte_array[-1]						) % byte_length == 0:
                        result_byte_array.append("10000000"						)
                  else:
                        result_byte_array[-1] += "1" + "0" * (
                            byte_length - len(result_byte_array[-1]						) - 1
                        )

                  for elem in result_byte_array:
                        opened_file.write(int(_A    ,					2						).to_bytes(1    ,					byteorder="big"						)						)
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  read_file_binary(_A						)
      lowerCamelCase__		:				List[Any]    =  compress_data(_A						)
      lowerCamelCase__		:				str    =  add_file_length(_A    ,					_A						)
      write_file_binary(_A    ,					_A						)


if __name__ == "__main__":
    compress(sys.argv[1], sys.argv[2])


