
"""simple docstring"""







# Imports
import numpy as np




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			self.set_matricies(red=__lowerCamelCase ,	green=__lowerCamelCase ,	blue=__lowerCamelCase ,	red_edge=__lowerCamelCase ,	nir=__lowerCamelCase				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):






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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE="" ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			self.set_matricies(red=__lowerCamelCase ,	green=__lowerCamelCase ,	blue=__lowerCamelCase ,	red_edge=__lowerCamelCase ,	nir=__lowerCamelCase				)
			__lowerCAmelCase		    =   {
			    '''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 							__lowerCamelCase							(   self				):
			return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))

		def 							__lowerCamelCase							(   self				):
			return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
			    (self.nir - self.red) / (self.nir + self.red)
			)

		def 							__lowerCamelCase							(   self				):
			return self.nir * (self.red / (self.green**2))

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

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.red) / (self.nir + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.blue) / (self.nir + self.blue)

		def 							__lowerCamelCase							(   self				):
			return (self.redEdge - self.red) / (self.redEdge + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.green) / (self.nir + self.green)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.blue)) / (
			    self.nir + (self.green + self.blue)
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.green + self.red)) / (
			    self.nir + (self.green + self.red)
			)

		def 							__lowerCamelCase							(   self				):
			return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.08 ,	_SCREAMING_SNAKE_CASE=1.22 ,	_SCREAMING_SNAKE_CASE=0.03				):
			return a * (
			    (self.nir - a * self.red - b)
			    / (a * self.nir + self.red - a * b + x * (1 + a**2))
			)

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

		def 							__lowerCamelCase							(   self				):
			return (self.nir / self.green) - 1

		def 							__lowerCamelCase							(   self				):
			return (self.nir / self.redEdge) - 1

		def 							__lowerCamelCase							(   self				):
			return (self.red - self.blue) / self.red

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase		    =   self.ndvi()
			return ((ndvi + 0.5) / (abs(ndvi + 0.5				))) * (abs(ndvi + 0.5				) ** (1 / 2))

		def 							__lowerCamelCase							(   self				):
			return self.nir - self.green

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase		    =   (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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.16				):
			return (self.nir - self.green) / (self.nir + self.green + y)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0.5				):
			return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			return (self.nir - b) / (a * self.red)

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

		def 							__lowerCamelCase							(   self				):
			return (self.red + self.green + self.blue) / 30.5

		def 							__lowerCamelCase							(   self				):
			return self.nir / self.red

		def 							__lowerCamelCase							(   self				):
			return (self.rvi() - 1) / (self.rvi() + 1)

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

		def 							__lowerCamelCase							(   self				):
			return self.green / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return self.nir / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return self.red / (self.nir + self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			return (self.green - self.red) / (self.green + self.red)

		def 							__lowerCamelCase							(   self				):
			return (self.red - self.green) / (self.red + self.green)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase		    =   np.max([np.max(self.red				), np.max(self.green				), np.max(self.blue				)]				)
			__lowerCAmelCase		    =   np.min([np.min(self.red				), np.min(self.green				), np.min(self.blue				)]				)
			return (max_value - min_value) / max_value

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

		def 							__lowerCamelCase							(   self				):
			return self.nir / self.red

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







		def 							__lowerCamelCase							(   self				):
			return (self.nir - self.redEdge) / (self.nir + self.redEdge)

"""simple docstring"""







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase__           =	data_utils.TransfoXLTokenizer
lowerCamelCase__           =	data_utils.TransfoXLCorpus
lowerCamelCase__           =	data_utils
lowerCamelCase__           =	data_utils
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(_UpperCamelCase ,						'rb'	) as fp:
			__lowerCAmelCase    :		int		    =   pickle.load(_UpperCamelCase ,						encoding='latin1'	)
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		__lowerCAmelCase    :		Any		    =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
		print(F"Save vocabulary to {pytorch_vocab_dump_path}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   corpus.vocab.__dict__
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab' ,						_UpperCamelCase	)
		__lowerCAmelCase    :		List[str]		    =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F"Save dataset to {pytorch_dataset_dump_path}"	)
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__lowerCAmelCase    :		Tuple		    =   os.path.abspath(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   os.path.abspath(_UpperCamelCase	)

		print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."	)
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__lowerCAmelCase    :		Any		    =   TransfoXLConfig()
		else:
			__lowerCAmelCase    :		str		    =   TransfoXLConfig.from_json_file(_UpperCamelCase	)
		print(F"Building PyTorch model from configuration: {config}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   TransfoXLLMHeadModel(_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   load_tf_weights_in_transfo_xl(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
		# Save pytorch-model
		__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		print(F"Save PyTorch model to {os.path.abspath(_UpperCamelCase	)}"	)
		torch.save(model.state_dict() ,						_UpperCamelCase	)
		print(F"Save configuration file to {os.path.abspath(_UpperCamelCase	)}"	)
		with open(_UpperCamelCase ,						'w' ,						encoding='utf-8'	) as f:
			f.write(config.to_json_string()	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
							)
							parser.add_argument(
							    """--tf_checkpoint_path""",
							    default="""""",
							    type=str,
							    help="""An optional path to a TensorFlow checkpoint path to be converted.""",
							)
							parser.add_argument(
							    """--transfo_xl_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained BERT model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--transfo_xl_dataset_file""",
							    default="""""",
							    type=str,
							    help="""An optional dataset file to be converted in a vocabulary.""",
							)
							lowerCamelCase__           =	parser.parse_args()
							convert_transfo_xl_checkpoint_to_pytorch(
							    args.tf_checkpoint_path,
							    args.transfo_xl_config_file,
							    args.pytorch_dump_folder_path,
							    args.transfo_xl_dataset_file,
							)