




'''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()
a		:	str       =      logging.get_logger(__name__)





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Tuple							       =      MobileNetVaConfig(layer_norm_eps=0.001						)

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

    _a				:							Dict							       =      re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""   ,	UpperCAmelCase_						)
    if matches:
        _a				:							Optional[int]							       =      float(matches[1]						)
        _a				:							List[str]							       =      int(matches[2]						)

    if model_name.startswith("""deeplabv3_"""						):
        _a				:							Any							       =      8
        _a				:							Tuple							       =      21
        _a				:							Tuple							       =      """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".
        _a				:							str							       =      1_001
        _a				:							List[str]							       =      """imagenet-1k-id2label.json"""

    _a				:							Union[str, Any]							       =      """huggingface/label-files"""
    _a				:							Dict							       =      json.load(open(hf_hub_download(UpperCAmelCase_   ,	UpperCAmelCase_   ,	repo_type="""dataset"""						)   ,	"""r"""						)						)

    if config.num_labels == 1_001:
        _a				:							Union[str, Any]							       =      {int(UpperCAmelCase_						) + 1: v for k, v in idalabel.items()}
        _a				:							Tuple							       =      """background"""
    else:
        _a				:							Dict							       =      {int(UpperCAmelCase_						): v for k, v in idalabel.items()}

    _a				:							Any							       =      idalabel
    _a				:							Optional[Any]							       =      {v: k for k, v in idalabel.items()}

    return config





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





@torch.no_grad()
def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_=False						):
    _a				:							Any							       =      get_mobilenet_va_config(UpperCAmelCase_						)

    # Load 🤗 model
    if model_name.startswith("""deeplabv3_"""						):
        _a				:							Dict							       =      MobileNetVaForSemanticSegmentation(UpperCAmelCase_						).eval()
    else:
        _a				:							Optional[int]							       =      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
    _a				:							Optional[int]							       =      MobileNetVaImageProcessor(
        crop_size={"""width""": config.image_size, """height""": config.image_size}   ,	size={"""shortest_edge""": config.image_size + 32}   ,	)
    _a				:							List[Any]							       =      image_processor(images=prepare_img()   ,	return_tensors="""pt"""						)
    _a				:							Optional[int]							       =      model(**UpperCAmelCase_						)
    _a				:							int							       =      outputs.logits

    if model_name.startswith("""deeplabv3_"""						):
        assert logits.shape == (1, 21, 65, 65)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            _a				:							Union[str, Any]							       =      torch.tensor(
                [
                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]						)

        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, 1_001)

        if model_name == "mobilenet_v2_1.4_224":
            _a				:							Tuple							       =      torch.tensor([0.0181, -1.0015, 0.4688]						)
        elif model_name == "mobilenet_v2_1.0_224":
            _a				:							Optional[Any]							       =      torch.tensor([0.2445, -1.1993, 0.1905]						)
        elif model_name == "mobilenet_v2_0.75_160":
            _a				:							Dict							       =      torch.tensor([0.2482, 0.4136, 0.6669]						)
        elif model_name == "mobilenet_v2_0.35_96":
            _a				:							Optional[Any]							       =      torch.tensor([0.1451, -0.4624, 0.7192]						)
        else:
            _a				:							int							       =      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..."""						)
        _a				:							List[str]							       =      """google/""" + model_name
        image_processor.push_to_hub(UpperCAmelCase_						)
        model.push_to_hub(UpperCAmelCase_						)


if __name__ == "__main__":
  a		:	Optional[int]       =      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.'''
  )

  a		:	Tuple       =      parser.parse_args()
  convert_movilevit_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )










"""simple docstring"""




from __future__ import annotations






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				tuple[float, float]:







  '''simple docstring'''







  assert (
      c % greatest_common_divisor(UpperCamelCase_  ,			UpperCamelCase_       ) == 0
  )  # greatest_common_divisor(a,b) function implemented below
  ((_a)      ,						(_a)      ,						(_a))					      =						extended_gcd(UpperCamelCase_  ,			UpperCamelCase_       )  # extended_gcd(a,b) function implemented below
  _a					      =						c / d
  return (r * x, r * y)






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				int = 2       )							->				None:







  '''simple docstring'''







  ((_a)      ,						(_a))					      =						diophantine(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )  # Initial value
  _a					      =						greatest_common_divisor(UpperCamelCase_  ,			UpperCamelCase_       )
  _a					      =						a // d
  _a					      =						b // d

  for i in range(UpperCamelCase_       ):
    _a					      =						xa + i * q
    _a					      =						ya - i * p
    print(UpperCamelCase_  ,			UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if a < b:
    _a      ,						_a					      =						b, a

  while a % b != 0:
    _a      ,						_a					      =						b, a % b

  return b






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				int       )							->				tuple[int, int, int]:







  '''simple docstring'''







  assert a >= 0 and b >= 0

  if b == 0:
    _a      ,						_a      ,						_a					      =						a, 1, 0
  else:
    ((_a)      ,						(_a)      ,						(_a))					      =						extended_gcd(UpperCamelCase_  ,			a % b       )
    _a					      =						q
    _a					      =						p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)


if __name__ == "__main__":
     from doctest import testmod

     testmod(name="""diophantine""", verbose=True)
     testmod(name="""diophantine_all_soln""", verbose=True)
     testmod(name="""extended_gcd""", verbose=True)
     testmod(name="""greatest_common_divisor""", verbose=True)


