





"""simple docstring"""






# Imports
import numpy as np



class 					__UpperCAmelCase							:
				def __init__(		self    :			Any	, a_    :			List[Any]=None	, a_    :			Optional[int]=None	, a_    :			Tuple=None	, a_    :			List[str]=None	, a_    :			int=None    )	->	List[str]:



							'''simple docstring'''



							self.set_matricies(red=a_	, green=a_	, blue=a_	, red_edge=a_	, nir=a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			Tuple=None	, a_    :			Optional[int]=None	, a_    :			Any=None	, a_    :			Dict=None	, a_    :			Any=None    )	->	str:



							'''simple docstring'''



							if red is not None:
										a__  :	Dict		 =			red
							if green is not None:
										a__  :	Union[str, Any]		 =			green
							if blue is not None:
										a__  :	Union[str, Any]		 =			blue
							if red_edge is not None:
										a__  :	Dict		 =			red_edge
							if nir is not None:
										a__  :	Optional[Any]		 =			nir
							return True
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Tuple=""	, a_    :			Dict=None	, a_    :			Optional[Any]=None	, a_    :			Optional[int]=None	, a_    :			Tuple=None	, a_    :			str=None    )	->	Union[str, Any]:



							'''simple docstring'''



							self.set_matricies(red=a_	, green=a_	, blue=a_	, red_edge=a_	, nir=a_    )
							a__  :	Tuple		 =			{
							    "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       UpperCAmelCase      (		self    :			Any    )	->	Dict:



							'''simple docstring'''



							return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[Any]:



							'''simple docstring'''



							return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
							    (self.nir - self.red) / (self.nir + self.red)
							)
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							return self.nir * (self.red / (self.green**2))
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[str]:



							'''simple docstring'''



							return (2 * self.green - self.red - self.blue) / (
							    2 * self.green + self.red + self.blue
							)
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[int]:



							'''simple docstring'''



							return (self.nir - self.red) / (self.nir + self.red)
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							return (self.nir - self.blue) / (self.nir + self.blue)
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[int]:



							'''simple docstring'''



							return (self.redEdge - self.red) / (self.redEdge + self.red)
				def       UpperCAmelCase      (		self    :			Any    )	->	List[str]:



							'''simple docstring'''



							return (self.nir - self.green) / (self.nir + self.green)
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							return (self.nir - (self.green + self.blue)) / (
							    self.nir + (self.green + self.blue)
							)
				def       UpperCAmelCase      (		self    :			int    )	->	int:



							'''simple docstring'''



							return (self.nir - (self.green + self.red)) / (
							    self.nir + (self.green + self.red)
							)
				def       UpperCAmelCase      (		self    :			int    )	->	Union[str, Any]:



							'''simple docstring'''



							return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	int:



							'''simple docstring'''



							return (self.nir - (self.green + self.red + self.blue)) / (
							    self.nir + (self.green + self.red + self.blue)
							)
				def       UpperCAmelCase      (		self    :			Any	, a_    :			Union[str, Any]=0.08	, a_    :			int=1.22	, a_    :			Tuple=0.03    )	->	int:



							'''simple docstring'''



							return a * (
							    (self.nir - a * self.red - b)
							    / (a * self.nir + self.red - a * b + x * (1 + a**2))
							)
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[str]:



							'''simple docstring'''



							return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Any:



							'''simple docstring'''



							return (self.nir / self.green) - 1
				def       UpperCAmelCase      (		self    :			Dict    )	->	Tuple:



							'''simple docstring'''



							return (self.nir / self.redEdge) - 1
				def       UpperCAmelCase      (		self    :			Any    )	->	List[Any]:



							'''simple docstring'''



							return (self.red - self.blue) / self.red
				def       UpperCAmelCase      (		self    :			str    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.ndvi()
							return ((ndvi + 0.5) / (abs(ndvi + 0.5    ))) * (abs(ndvi + 0.5    ) ** (1 / 2))
				def       UpperCAmelCase      (		self    :			int    )	->	int:



							'''simple docstring'''



							return self.nir - self.green
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Any:



							'''simple docstring'''



							return 2.5 * (
							    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
							)
				def       UpperCAmelCase      (		self    :			Tuple    )	->	List[Any]:



							'''simple docstring'''



							a__  :	int		 =			(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       UpperCAmelCase      (		self    :			Dict	, a_    :			Any=0.16    )	->	Dict:



							'''simple docstring'''



							return (self.nir - self.green) / (self.nir + self.green + y)
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			Dict=0.5    )	->	Optional[int]:



							'''simple docstring'''



							return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Any:



							'''simple docstring'''



							return np.arctan(
							    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)    )
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			Dict=None	, a_    :			int=None    )	->	Dict:



							'''simple docstring'''



							return (self.nir - b) / (a * self.red)
				def       UpperCAmelCase      (		self    :			int    )	->	Dict:



							'''simple docstring'''



							return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return (self.red + self.green + self.blue) / 30.5
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[Any]:



							'''simple docstring'''



							return self.nir / self.red
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Any:



							'''simple docstring'''



							return (self.rvi() - 1) / (self.rvi() + 1)
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Any:



							'''simple docstring'''



							return (
							    (2 * self.nir + 1)
							    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
							) / 2
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Optional[int]:



							'''simple docstring'''



							return self.green / (self.nir + self.red + self.green)
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Optional[int]:



							'''simple docstring'''



							return self.nir / (self.nir + self.red + self.green)
				def       UpperCAmelCase      (		self    :			Any    )	->	int:



							'''simple docstring'''



							return self.red / (self.nir + self.red + self.green)
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Optional[Any]:



							'''simple docstring'''



							return (self.green - self.red) / (self.green + self.red)
				def       UpperCAmelCase      (		self    :			List[str]    )	->	str:



							'''simple docstring'''



							return (self.red - self.green) / (self.red + self.green)
				def       UpperCAmelCase      (		self    :			int    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			np.max([np.max(self.red    ), np.max(self.green    ), np.max(self.blue    )]    )
							a__  :	int		 =			np.min([np.min(self.red    ), np.min(self.green    ), np.min(self.blue    )]    )
							return (max_value - min_value) / max_value
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
				def       UpperCAmelCase      (		self    :			str    )	->	Any:



							'''simple docstring'''



							return self.nir / self.red
				def       UpperCAmelCase      (		self    :			int    )	->	Tuple:



							'''simple docstring'''



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






				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Optional[Any]:



							'''simple docstring'''



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






"""simple docstring"""






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[Any]=None			)    ->			str:



			'''simple docstring'''
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
			a__  :	Optional[Any]		 =			nn.Parameter(lowerCAmelCase__			)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
						a__  :	str		 =			nn.Parameter(lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	Tuple		 =			np.asarray(weights[0]			)
			a__  :	Any		 =			np.asarray(weights[1]			)
			a__  :	Optional[Any]		 =			np.asarray(weights[2]			)

			set_param(
			    torch_layer.self_attention.query_key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			str:



			'''simple docstring'''
			# set torch weights for 1-to-1 comparison
			a__  :	int		 =			np.asarray(weights[0]			)
			a__  :	List[str]		 =			np.asarray(weights[1]			)
			a__  :	Optional[int]		 =			np.asarray(weights[2]			)
			a__  :	int		 =			np.asarray(weights[3]			)

			set_param(
			    torch_layer.self_attention.query							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.key							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.self_attention.value							,			torch.tensor(lowerCAmelCase__			).transpose(1							,			2			).contiguous().view(-1							,			lowerCAmelCase__			)							,			)
			set_param(
			    torch_layer.output.dense							,			torch.tensor(lowerCAmelCase__			).view(-1							,			lowerCAmelCase__			).contiguous().transpose(0							,			1			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# layernorm 1
			a__  :	int		 =			weights[0][0][0]
			a__  :	Optional[Any]		 =			np.asarray(layer_norm_a[0]			)
			a__  :	Optional[int]		 =			np.asarray(layer_norm_a[1]			)
			set_param(
			    torch_block.attention.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# lsh weights + output
			a__  :	Dict		 =			weights[0][1]
			if len(lowerCAmelCase__			) < 4:
						set_layer_weights_in_torch_lsh(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)
			else:
						set_layer_weights_in_torch_local(lowerCAmelCase__							,			torch_block.attention							,			lowerCAmelCase__			)

			# intermediate weighs
			a__  :	Optional[int]		 =			weights[2][0][1][2]

			# Chunked Feed Forward
			if len(lowerCAmelCase__			) == 4:
						a__  :	List[str]		 =			intermediate_weights[2]

			# layernorm 2
			a__  :	List[Any]		 =			np.asarray(intermediate_weights[0][0]			)
			a__  :	Optional[int]		 =			np.asarray(intermediate_weights[0][1]			)
			set_param(
			    torch_block.feed_forward.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate dense
			a__  :	Tuple		 =			np.asarray(intermediate_weights[1][0]			)
			a__  :	Union[str, Any]		 =			np.asarray(intermediate_weights[1][1]			)
			set_param(
			    torch_block.feed_forward.dense.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)

			# intermediate out
			a__  :	Optional[Any]		 =			np.asarray(intermediate_weights[4][0]			)
			a__  :	Dict		 =			np.asarray(intermediate_weights[4][1]			)
			set_param(
			    torch_block.feed_forward.output.dense							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			Any:



			'''simple docstring'''
			# reformer model
			a__  :	int		 =			torch_model.reformer

			# word embeds
			a__  :	List[Any]		 =			np.asarray(weights[1]			)
			set_param(
			    torch_model_reformer.embeddings.word_embeddings							,			torch.tensor(lowerCAmelCase__			)							,			)

			if isinstance(weights[3]							,			lowerCAmelCase__			):
						a__  :	Dict		 =			torch_model_reformer.embeddings.position_embeddings
						for emb_idx in range(len(position_embeddings.weights			)			):
									a__  :	str		 =			np.asarray(weights[3][emb_idx][0]			)
									assert (
									    position_embeddings.weights[emb_idx].shape == emb_weights.shape
									), F"{position_embeddings[emb_idx]} emb does not match"
									a__  :	List[str]		 =			nn.Parameter(torch.tensor(lowerCAmelCase__			)			)

			a__  :	Optional[Any]		 =			weights[5]
			assert len(torch_model_reformer.encoder.layers			) * 4 == len(
			    lowerCAmelCase__			), "HF and trax model do not have the same number of layers"
			for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
						a__  :	Union[str, Any]		 =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# output layer norm
			a__  :	str		 =			np.asarray(weights[7][0]			)
			a__  :	List[Any]		 =			np.asarray(weights[7][1]			)
			set_param(
			    torch_model_reformer.encoder.layer_norm							,			torch.tensor(lowerCAmelCase__			)							,			torch.tensor(lowerCAmelCase__			)							,			)

			# output embeddings
			a__  :	str		 =			np.asarray(weights[9][0]			)
			a__  :	Optional[int]		 =			np.asarray(weights[9][1]			)
			set_param(
			    torch_model.lm_head.decoder							,			torch.tensor(lowerCAmelCase__			).transpose(0							,			1			).contiguous()							,			torch.tensor(lowerCAmelCase__			)							,			)






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			# Initialise PyTorch model
			a__  :	List[Any]		 =			ReformerConfig.from_json_file(lowerCAmelCase__			)
			print(F"Building PyTorch model from configuration: {config}"			)
			a__  :	Optional[Any]		 =			ReformerModelWithLMHead(lowerCAmelCase__			)

			with open(lowerCAmelCase__							,			"rb"			) as f:
						a__  :	List[str]		 =			pickle.load(lowerCAmelCase__			)["weights"]

			set_model_weights_in_torch(lowerCAmelCase__							,			lowerCAmelCase__							,			config.hidden_size			)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"			)
			torch.save(model.state_dict()							,			lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained Reformer model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)