



# Imports
import numpy as np
class 			_snake_case     :




				def __init__(  self   :  Optional[int],   __lowercase   :  str=None,   __lowercase   :  str=None,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  str=None,   __lowercase   :  str=None  ):
							self.set_matricies(red=__lowercase,   green=__lowercase,   blue=__lowercase,   red_edge=__lowercase,   nir=__lowercase  )




				def 							A__ (  self   :  Tuple,   __lowercase   :  Any=None,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  List[str]=None,   __lowercase   :  Any=None,   __lowercase   :  str=None  ):
							if red is not None:
										lowercase__   					=			red
							if green is not None:
										lowercase__   					=			green
							if blue is not None:
										lowercase__   					=			blue
							if red_edge is not None:
										lowercase__   					=			red_edge
							if nir is not None:
										lowercase__   					=			nir
							return True




				def 							A__ (  self   :  str,   __lowercase   :  Dict="",   __lowercase   :  Optional[int]=None,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Tuple=None,   __lowercase   :  List[Any]=None,   __lowercase   :  Tuple=None  ):
							self.set_matricies(red=__lowercase,   green=__lowercase,   blue=__lowercase,   red_edge=__lowercase,   nir=__lowercase  )
							lowercase__   					=			{
							    "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   :  Any  ):
							return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))




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




				def 							A__ (  self   :  Optional[Any]  ):
							return self.nir * (self.red / (self.green**2))




				def 							A__ (  self   :  List[Any]  ):
							return (2 * self.green - self.red - self.blue) / (
							    2 * self.green + self.red + self.blue
							)




				def 							A__ (  self   :  Union[str, Any]  ):
							return (self.nir - self.red) / (self.nir + self.red)




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




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




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




				def 							A__ (  self   :  List[Any]  ):
							return (self.nir - (self.green + self.blue)) / (
							    self.nir + (self.green + self.blue)
							)




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




				def 							A__ (  self   :  List[Any]  ):
							return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))




				def 							A__ (  self   :  List[str]  ):
							return (self.nir - (self.green + self.red + self.blue)) / (
							    self.nir + (self.green + self.red + self.blue)
							)




				def 							A__ (  self   :  Dict,   __lowercase   :  Optional[int]=0.08,   __lowercase   :  Any=1.22,   __lowercase   :  Any=0.03  ):
							return a * (
							    (self.nir - a * self.red - b)
							    / (a * self.nir + self.red - a * b + x * (1 + a**2))
							)




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




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




				def 							A__ (  self   :  Union[str, Any]  ):
							return (self.nir / self.redEdge) - 1




				def 							A__ (  self   :  Optional[int]  ):
							return (self.red - self.blue) / self.red




				def 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			self.ndvi()
							return ((ndvi + 0.5) / (abs(ndvi + 0.5  ))) * (abs(ndvi + 0.5  ) ** (1 / 2))




				def 							A__ (  self   :  int  ):
							return self.nir - self.green




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




				def 							A__ (  self   :  Optional[Any]  ):
							lowercase__   					=			(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   :  Dict,   __lowercase   :  Union[str, Any]=0.16  ):
							return (self.nir - self.green) / (self.nir + self.green + y)




				def 							A__ (  self   :  List[str],   __lowercase   :  Union[str, Any]=0.5  ):
							return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)




				def 							A__ (  self   :  Optional[Any]  ):
							return np.arctan(
							    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)  )




				def 							A__ (  self   :  Dict,   __lowercase   :  Tuple=None,   __lowercase   :  Optional[int]=None  ):
							return (self.nir - b) / (a * self.red)




				def 							A__ (  self   :  List[Any]  ):
							return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)




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




				def 							A__ (  self   :  Tuple  ):
							return self.nir / self.red




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




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




				def 							A__ (  self   :  Union[str, Any]  ):
							return self.green / (self.nir + self.red + self.green)




				def 							A__ (  self   :  Optional[Any]  ):
							return self.nir / (self.nir + self.red + self.green)




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




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




				def 							A__ (  self   :  Optional[Any]  ):
							return (self.red - self.green) / (self.red + self.green)




				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__   					=			np.max([np.max(self.red  ), np.max(self.green  ), np.max(self.blue  )]  )
							lowercase__   					=			np.min([np.min(self.red  ), np.min(self.green  ), np.min(self.blue  )]  )
							return (max_value - min_value) / max_value




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




				def 							A__ (  self   :  Tuple  ):
							return self.nir / self.red




				def 							A__ (  self   :  List[Any]  ):
							return (self.ndvi() + 0.5) ** (1 / 2)




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









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							lowercase_						=       parser.parse_args()

							if args.model_type == "roberta":
														lowercase_						=       RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase_						=       """roberta"""
							elif args.model_type == "gpt2":
														lowercase_						=       GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase_						=       """transformer"""

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase_						=       state_dict[F'{prefix}.{param_name}']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase_						=       F'{prefix}.embeddings.{w}.weight'
																					lowercase_						=       state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase_						=       F'{prefix}.embeddings.LayerNorm.{w}'
																					lowercase_						=       state_dict[param_name]

    # Transformer Blocks #
							lowercase_						=       0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       state_dict["""lm_head.weight"""]

							print(F'N layers selected for distillation: {std_idx}')
							print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

							print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
							torch.save(compressed_sd, args.dump_checkpoint)




