



"""simple docstring"""







# Imports
import numpy as np






class      UpperCAmelCase__		:



		def __init__( self  :    List[str]      ,							snake_case  :    str=None      ,							snake_case  :    int=None      ,							snake_case  :    int=None      ,							snake_case  :    str=None      ,							snake_case  :    Dict=None				)			->						Tuple:






									'''simple docstring'''

									self.set_matricies(red=snake_case      ,							green=snake_case      ,							blue=snake_case      ,							red_edge=snake_case      ,							nir=snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    List[str]=None      ,							snake_case  :    int=None      ,							snake_case  :    int=None      ,							snake_case  :    Union[str, Any]=None      ,							snake_case  :    Any=None				)			->						List[str]:






									'''simple docstring'''

									if red is not None:
																A         =					red
									if green is not None:
																A         =					green
									if blue is not None:
																A         =					blue
									if red_edge is not None:
																A         =					red_edge
									if nir is not None:
																A         =					nir
									return True



		def 					A_		( self  :    List[Any]      ,							snake_case  :    int=""      ,							snake_case  :    List[str]=None      ,							snake_case  :    List[str]=None      ,							snake_case  :    int=None      ,							snake_case  :    Optional[int]=None      ,							snake_case  :    Union[str, Any]=None				)			->						List[str]:






									'''simple docstring'''

									self.set_matricies(red=snake_case      ,							green=snake_case      ,							blue=snake_case      ,							red_edge=snake_case      ,							nir=snake_case				)
									A         =					{
									    'ARVI2': self.arvaa,
									    'CCCI': self.ccci,
									    'CVI': self.cvi,
									    'GLI': self.gli,
									    'NDVI': self.ndvi,
									    'BNDVI': self.bndvi,
									    'redEdgeNDVI': self.red_edge_ndvi,
									    'GNDVI': self.gndvi,
									    'GBNDVI': self.gbndvi,
									    'GRNDVI': self.grndvi,
									    'RBNDVI': self.rbndvi,
									    'PNDVI': self.pndvi,
									    'ATSAVI': self.atsavi,
									    'BWDRVI': self.bwdrvi,
									    'CIgreen': self.ci_green,
									    'CIrededge': self.ci_rededge,
									    'CI': self.ci,
									    'CTVI': self.ctvi,
									    'GDVI': self.gdvi,
									    'EVI': self.evi,
									    'GEMI': self.gemi,
									    'GOSAVI': self.gosavi,
									    'GSAVI': self.gsavi,
									    'Hue': self.hue,
									    'IVI': self.ivi,
									    'IPVI': self.ipvi,
									    'I': self.i,
									    'RVI': self.rvi,
									    'MRVI': self.mrvi,
									    'MSAVI': self.m_savi,
									    'NormG': self.norm_g,
									    'NormNIR': self.norm_nir,
									    'NormR': self.norm_r,
									    'NGRDI': self.ngrdi,
									    'RI': self.ri,
									    'S': self.s,
									    'IF': self._if,
									    'DVI': self.dvi,
									    'TVI': self.tvi,
									    'NDRE': self.ndre,
									}

									try:
																return funcs[index]()
									except KeyError:
																print('Index not in the list!'				)
																return False



		def 					A_		( self  :    List[Any]				)			->						str:






									'''simple docstring'''

									return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))



		def 					A_		( self  :    Tuple				)			->						Dict:






									'''simple docstring'''

									return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
									    (self.nir - self.red) / (self.nir + self.red)
									)



		def 					A_		( self  :    Optional[Any]				)			->						List[Any]:






									'''simple docstring'''

									return self.nir * (self.red / (self.green**2))



		def 					A_		( self  :    str				)			->						Optional[Any]:






									'''simple docstring'''

									return (2 * self.green - self.red - self.blue) / (
									    2 * self.green + self.red + self.blue
									)



		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									return (self.nir - self.red) / (self.nir + self.red)



		def 					A_		( self  :    List[str]				)			->						Dict:






									'''simple docstring'''

									return (self.nir - self.blue) / (self.nir + self.blue)



		def 					A_		( self  :    str				)			->						Dict:






									'''simple docstring'''

									return (self.redEdge - self.red) / (self.redEdge + self.red)



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									return (self.nir - self.green) / (self.nir + self.green)



		def 					A_		( self  :    Optional[Any]				)			->						List[Any]:






									'''simple docstring'''

									return (self.nir - (self.green + self.blue)) / (
									    self.nir + (self.green + self.blue)
									)



		def 					A_		( self  :    List[Any]				)			->						Any:






									'''simple docstring'''

									return (self.nir - (self.green + self.red)) / (
									    self.nir + (self.green + self.red)
									)



		def 					A_		( self  :    str				)			->						Union[str, Any]:






									'''simple docstring'''

									return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))



		def 					A_		( self  :    List[Any]				)			->						List[Any]:






									'''simple docstring'''

									return (self.nir - (self.green + self.red + self.blue)) / (
									    self.nir + (self.green + self.red + self.blue)
									)



		def 					A_		( self  :    Any      ,							snake_case  :    Tuple=0.08      ,							snake_case  :    Optional[Any]=1.22      ,							snake_case  :    str=0.03				)			->						Tuple:






									'''simple docstring'''

									return a * (
									    (self.nir - a * self.red - b)
									    / (a * self.nir + self.red - a * b + x * (1 + a**2))
									)



		def 					A_		( self  :    List[str]				)			->						List[Any]:






									'''simple docstring'''

									return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)



		def 					A_		( self  :    List[str]				)			->						Dict:






									'''simple docstring'''

									return (self.nir / self.green) - 1



		def 					A_		( self  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									return (self.nir / self.redEdge) - 1



		def 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									return (self.red - self.blue) / self.red



		def 					A_		( self  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									A         =					self.ndvi()
									return ((ndvi + 0.5) / (abs(ndvi + 0.5				))) * (abs(ndvi + 0.5				) ** (1 / 2))



		def 					A_		( self  :    str				)			->						Dict:






									'''simple docstring'''

									return self.nir - self.green



		def 					A_		( self  :    Dict				)			->						Optional[Any]:






									'''simple docstring'''

									return 2.5 * (
									    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
									)



		def 					A_		( self  :    Any				)			->						str:






									'''simple docstring'''

									A         =					(2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
									    self.nir + self.red + 0.5
									)
									return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)



		def 					A_		( self  :    str      ,							snake_case  :    str=0.16				)			->						List[str]:






									'''simple docstring'''

									return (self.nir - self.green) / (self.nir + self.green + y)



		def 					A_		( self  :    Dict      ,							snake_case  :    Optional[int]=0.5				)			->						str:






									'''simple docstring'''

									return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)



		def 					A_		( self  :    Union[str, Any]				)			->						List[Any]:






									'''simple docstring'''

									return np.arctan(
									    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)				)



		def 					A_		( self  :    str      ,							snake_case  :    List[Any]=None      ,							snake_case  :    Tuple=None				)			->						Dict:






									'''simple docstring'''

									return (self.nir - b) / (a * self.red)



		def 					A_		( self  :    List[str]				)			->						List[Any]:






									'''simple docstring'''

									return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)



		def 					A_		( self  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									return (self.red + self.green + self.blue) / 30.5



		def 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									return self.nir / self.red



		def 					A_		( self  :    str				)			->						str:






									'''simple docstring'''

									return (self.rvi() - 1) / (self.rvi() + 1)



		def 					A_		( self  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									return (
									    (2 * self.nir + 1)
									    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
									) / 2



		def 					A_		( self  :    Union[str, Any]				)			->						str:






									'''simple docstring'''

									return self.green / (self.nir + self.red + self.green)



		def 					A_		( self  :    Dict				)			->						Optional[Any]:






									'''simple docstring'''

									return self.nir / (self.nir + self.red + self.green)



		def 					A_		( self  :    Optional[int]				)			->						int:






									'''simple docstring'''

									return self.red / (self.nir + self.red + self.green)



		def 					A_		( self  :    List[Any]				)			->						Any:






									'''simple docstring'''

									return (self.green - self.red) / (self.green + self.red)



		def 					A_		( self  :    Tuple				)			->						str:






									'''simple docstring'''

									return (self.red - self.green) / (self.red + self.green)



		def 					A_		( self  :    str				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					np.max([np.max(self.red				), np.max(self.green				), np.max(self.blue				)]				)
									A         =					np.min([np.min(self.red				), np.min(self.green				), np.min(self.blue				)]				)
									return (max_value - min_value) / max_value



		def 					A_		( self  :    str				)			->						Union[str, Any]:






									'''simple docstring'''

									return (2 * self.red - self.green - self.blue) / (self.green - self.blue)



		def 					A_		( self  :    Tuple				)			->						Optional[Any]:






									'''simple docstring'''

									return self.nir / self.red



		def 					A_		( self  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									return (self.ndvi() + 0.5) ** (1 / 2)



		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									return (self.nir - self.redEdge) / (self.nir + self.redEdge)








"""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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							A         =					MobileNetVaConfig(layer_norm_eps=0.0_01			)

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

							A         =					re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'				,      lowerCamelCase__			)
							if matches:
														A         =					float(matches[1]			)
														A         =					int(matches[2]			)

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

							return config

def   lowerCAmelCase__			(     ) ->							List[Any]:
							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							A         =					Image.open(requests.get(lowerCamelCase__				,      stream=lowerCamelCase__			).raw			)
							return im

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False			) ->							Dict:
							A         =					get_mobilenet_va_config(lowerCamelCase__			)

							# Load 🤗 model
							A         =					MobileNetVaForImageClassification(lowerCamelCase__			).eval()

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

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							A         =					MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}				,      size={'shortest_edge': config.image_size + 32}				,      )
							A         =					image_processor(images=prepare_img()				,      return_tensors='pt'			)
							A         =					model(**lowerCamelCase__			)
							A         =					outputs.logits

							assert logits.shape == (1, 1001)

							if model_name == "mobilenet_v1_1.0_224":
														A         =					torch.tensor([-4.17_39, -1.12_33, 3.12_05]			)
							elif model_name == "mobilenet_v1_0.75_192":
														A         =					torch.tensor([-3.94_40, -2.31_41, -0.33_33]			)
							else:
														A         =					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...'			)
														A         =					'google/' + model_name
														image_processor.push_to_hub(lowerCamelCase__			)
														model.push_to_hub(lowerCamelCase__			)


if __name__ == "__main__":
							A    =			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    =			parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)



