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			:		Tuple =			logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
  					__a    : Optional[int]      					=						MobileNetVaConfig(layer_norm_eps=0.0_0_1     )
  
  					if "quant" in model_name:
  										raise ValueError('Quantized models are not supported.'     )
  
  					__a    : Any      					=						re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'		,   _SCREAMING_SNAKE_CASE     )
  					if matches:
  										__a    : Dict      					=						float(matches[1]     )
  										__a    : Union[str, Any]      					=						int(matches[2]     )
  
  					if model_name.startswith('deeplabv3_'     ):
  										__a    : int      					=						8
  										__a    : Union[str, Any]      					=						21
  										__a    : Dict      					=						'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    : Optional[int]      					=						1_001
  										__a    : Dict      					=						'imagenet-1k-id2label.json'
  
  					__a    : List[Any]      					=						'huggingface/label-files'
  					__a    : str      					=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   repo_type='dataset'     )		,   'r'     )     )
  
  					if config.num_labels == 1_001:
  										__a    : List[Any]      					=						{int(_SCREAMING_SNAKE_CASE     ) + 1: v for k, v in idalabel.items()}
  										__a    : List[Any]      					=						'background'
  					else:
  										__a    : Tuple      					=						{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
  
  					__a    : str      					=						idalabel
  					__a    : Tuple      					=						{v: k for k, v in idalabel.items()}
  
  					return config
  
  
  
  
  
  
  def 						lowerCamelCase							():
  					__a    : Optional[int]      					=						'http://images.cocodataset.org/val2017/000000039769.jpg'
  					__a    : Optional[int]      					=						Image.open(requests.get(_SCREAMING_SNAKE_CASE		,   stream=_SCREAMING_SNAKE_CASE     ).raw     )
  					return im
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     List[Any]		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     List[Any]=False     ):
  					__a    : List[Any]      					=						get_mobilenet_va_config(_SCREAMING_SNAKE_CASE     )
  
  					# Load 🤗 model
  					if model_name.startswith('deeplabv3_'     ):
  										__a    : Tuple      					=						MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE     ).eval()
  					else:
  										__a    : List[Any]      					=						MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE     ).eval()
  
  					# Load weights from TensorFlow checkpoint
  					load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  
  					# Check outputs on an image, prepared by MobileNetV2ImageProcessor
  					__a    : Optional[Any]      					=						MobileNetVaImageProcessor(
  					    crop_size={'width': config.image_size, 'height': config.image_size}		,   size={'shortest_edge': config.image_size + 32}		,   )
  					__a    : int      					=						image_processor(images=prepare_img()		,   return_tensors='pt'     )
  					__a    : Optional[int]      					=						model(**_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						outputs.logits
  
  					if model_name.startswith('deeplabv3_'     ):
  										assert logits.shape == (1, 21, 65, 65)
  
  										if model_name == "deeplabv3_mobilenet_v2_1.0_513":
  															__a    : List[Any]      					=						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]		,   _SCREAMING_SNAKE_CASE		,   atol=1e-4     )
  					else:
  										assert logits.shape == (1, 1_001)
  
  										if model_name == "mobilenet_v2_1.4_224":
  															__a    : Any      					=						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":
  															__a    : Optional[Any]      					=						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":
  															__a    : Optional[int]      					=						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":
  															__a    : Union[str, Any]      					=						torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]     )
  										else:
  															__a    : Union[str, Any]      					=						None
  
  										if expected_logits is not None:
  															assert torch.allclose(logits[0, :3]		,   _SCREAMING_SNAKE_CASE		,   atol=1e-4     )
  
  					Path(_SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )
  					print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
  					model.save_pretrained(_SCREAMING_SNAKE_CASE     )
  					print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
  					image_processor.save_pretrained(_SCREAMING_SNAKE_CASE     )
  
  					if push_to_hub:
  										print('Pushing to the hub...'     )
  										__a    : Tuple      					=						'google/' + model_name
  										image_processor.push_to_hub(_SCREAMING_SNAKE_CASE     )
  										model.push_to_hub(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	__lowercase			:		Tuple =			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			:		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'''
  
  
  
  import itertools
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 1_000_000     ):
  					__a    : str      					=						[1]
  
  					for i in itertools.count(len(_SCREAMING_SNAKE_CASE     )     ):
  										__a    : Dict      					=						0
  										for j in itertools.count(1     ):
  															__a    : int      					=						-1 if j % 2 == 0 else +1
  															__a    : Optional[int]      					=						(j * j * 3 - j) // 2
  															if index > i:
  																				break
  															item += partitions[i - index] * sign
  															item %= number
  															index += j
  															if index > i:
  																				break
  															item += partitions[i - index] * sign
  															item %= number
  
  										if item == 0:
  															return i
  										partitions.append(_SCREAMING_SNAKE_CASE     )
  
  					return 0
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  	print(f'''{solution() = }''')
  
  
  
  