


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()
UpperCAmelCase__        =				logging.get_logger(__name__)
def 							UpperCAmelCase_   (				__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =MobileNetVaConfig(layer_norm_eps=0.0_01		)

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

 _lowercase				 =re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''	,		_lowerCamelCase		)
 if matches:
  _lowercase				 =float(matches[1]		)
  _lowercase				 =int(matches[2]		)

 if model_name.startswith('''deeplabv3_'''		):
  _lowercase				 =8
  _lowercase				 =21
  _lowercase				 ="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".
  _lowercase				 =1001
  _lowercase				 ="imagenet-1k-id2label.json"

 _lowercase				 ="huggingface/label-files"
 _lowercase				 =json.load(open(hf_hub_download(_lowerCamelCase	,		_lowerCamelCase	,		repo_type='''dataset'''		)	,		'''r'''		)		)

 if config.num_labels == 1001:
  _lowercase				 ={int(_lowerCamelCase		) + 1: v for k, v in idalabel.items()}
  _lowercase				 ="background"
 else:
  _lowercase				 ={int(_lowerCamelCase		): v for k, v in idalabel.items()}

 _lowercase				 =idalabel
 _lowercase				 ={v: k for k, v in idalabel.items()}

 return config
def 							UpperCAmelCase_   (				)	->     Optional[int]:







 """simple docstring"""


 _lowercase				 ="http://images.cocodataset.org/val2017/000000039769.jpg"
 _lowercase				 =Image.open(requests.get(_lowerCamelCase	,		stream=_lowerCamelCase		).raw		)
 return im
@torch.no_grad()
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case=False		)	->     int:







 """simple docstring"""


 _lowercase				 =get_mobilenet_va_config(_lowerCamelCase		)

 # Load 🤗 model
 if model_name.startswith('''deeplabv3_'''		):
  _lowercase				 =MobileNetVaForSemanticSegmentation(_lowerCamelCase		).eval()
 else:
  _lowercase				 =MobileNetVaForImageClassification(_lowerCamelCase		).eval()

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

 # Check outputs on an image, prepared by MobileNetV2ImageProcessor
 _lowercase				 =MobileNetVaImageProcessor(
     crop_size={'''width''': config.image_size, '''height''': config.image_size}	,		size={'''shortest_edge''': config.image_size + 32}	,		)
 _lowercase				 =image_processor(images=prepare_img()	,		return_tensors='''pt'''		)
 _lowercase				 =model(**_lowerCamelCase		)
 _lowercase				 =outputs.logits

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

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   _lowercase				 =torch.tensor(
       [
           [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
           [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
           [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
       ]		)

  else:
   raise ValueError(F"Unknown model name: {model_name}"		)

  assert torch.allclose(logits[0, :3, :3, :3]	,		_lowerCamelCase	,		atol=1e-4		)
 else:
  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v2_1.4_224":
   _lowercase				 =torch.tensor([0.01_81, -1.00_15, 0.46_88]		)
  elif model_name == "mobilenet_v2_1.0_224":
   _lowercase				 =torch.tensor([0.24_45, -1.19_93, 0.19_05]		)
  elif model_name == "mobilenet_v2_0.75_160":
   _lowercase				 =torch.tensor([0.24_82, 0.41_36, 0.66_69]		)
  elif model_name == "mobilenet_v2_0.35_96":
   _lowercase				 =torch.tensor([0.14_51, -0.46_24, 0.71_92]		)
  else:
   _lowercase				 =None

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

 Path(_lowerCamelCase		).mkdir(exist_ok=_lowerCamelCase		)
 print(F"Saving model {model_name} to {pytorch_dump_folder_path}"		)
 model.save_pretrained(_lowerCamelCase		)
 print(F"Saving image processor to {pytorch_dump_folder_path}"		)
 image_processor.save_pretrained(_lowerCamelCase		)

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


if __name__ == "__main__":
  UpperCAmelCase__        =				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.'''
  )

  UpperCAmelCase__        =				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 Callable

__SCREAMING_SNAKE_CASE     =	Callable[[float], float]  # type alias for a real -> real function




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     ):
			A				:       int														=    float(_lowerCamelCase     )
			while True:
						try:
									A				:       Optional[int]														=    prev_guess - function(_lowerCamelCase     ) / derivative(_lowerCamelCase     )
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"     ) from None
						if abs(prev_guess - next_guess     ) < 10**-5:
									return next_guess
						A				:       Any														=    next_guess




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return (x**3) - (2 * x) - 5




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return 3 * (x**2) - 2


if __name__ == "__main__":
							print(newton(f, fa, 3))