'''simple docstring'''

import numpy as np



class      UpperCAmelCase    :





       '''simple docstring'''



       def __init__(		self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None					)    ->    Dict:
        self.set_matricies(red=__a      ,  green=__a      ,  blue=__a      ,  red_edge=__a      ,  nir=__a					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None					)    ->    Dict:
        if red is not None:
         lowercase__							:       Any										=   red
        if green is not None:
         lowercase__							:       List[Any]										=   green
        if blue is not None:
         lowercase__							:       Union[str, Any]										=   blue
        if red_edge is not None:
         lowercase__							:       Optional[int]										=   red_edge
        if nir is not None:
         lowercase__							:       List[str]										=   nir
        return True

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase=""      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None					)    ->    List[str]:
        self.set_matricies(red=__a      ,  green=__a      ,  blue=__a      ,  red_edge=__a      ,  nir=__a					)
        lowercase__							:       Union[str, Any]										=   {
            '''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					)    ->    List[Any]:
        return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))

       def _lowerCAmelCase(		self					)    ->    Any:
        return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
            (self.nir - self.red) / (self.nir + self.red)
        )

       def _lowerCAmelCase(		self					)    ->    Optional[int]:
        return self.nir * (self.red / (self.green**2))

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

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

       def _lowerCAmelCase(		self					)    ->    str:
        return (self.nir - self.blue) / (self.nir + self.blue)

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

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

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        return (self.nir - (self.green + self.blue)) / (
            self.nir + (self.green + self.blue)
        )

       def _lowerCAmelCase(		self					)    ->    Tuple:
        return (self.nir - (self.green + self.red)) / (
            self.nir + (self.green + self.red)
        )

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

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

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase=0.0_8      ,  __lowerCAmelCase=1.2_2      ,  __lowerCAmelCase=0.0_3					)    ->    Optional[Any]:
        return a * (
            (self.nir - a * self.red - b)
            / (a * self.nir + self.red - a * b + x * (1 + a**2))
        )

       def _lowerCAmelCase(		self					)    ->    Dict:
        return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)

       def _lowerCAmelCase(		self					)    ->    List[str]:
        return (self.nir / self.green) - 1

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

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

       def _lowerCAmelCase(		self					)    ->    Optional[int]:
        lowercase__							:       int										=   self.ndvi()
        return ((ndvi + 0.5) / (abs(ndvi + 0.5					))) * (abs(ndvi + 0.5					) ** (1 / 2))

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

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

       def _lowerCAmelCase(		self					)    ->    Tuple:
        lowercase__							:       Optional[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 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase=0.1_6					)    ->    Optional[Any]:
        return (self.nir - self.green) / (self.nir + self.green + y)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase=0.5					)    ->    Dict:
        return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)

       def _lowerCAmelCase(		self					)    ->    Dict:
        return np.arctan(
            ((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue)					)

       def _lowerCAmelCase(		self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None					)    ->    Any:
        return (self.nir - b) / (a * self.red)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)

       def _lowerCAmelCase(		self					)    ->    Optional[Any]:
        return (self.red + self.green + self.blue) / 3_0.5

       def _lowerCAmelCase(		self					)    ->    Any:
        return self.nir / self.red

       def _lowerCAmelCase(		self					)    ->    Tuple:
        return (self.rvi() - 1) / (self.rvi() + 1)

       def _lowerCAmelCase(		self					)    ->    List[Any]:
        return (
            (2 * self.nir + 1)
            - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
        ) / 2

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

       def _lowerCAmelCase(		self					)    ->    str:
        return self.nir / (self.nir + self.red + self.green)

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

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

       def _lowerCAmelCase(		self					)    ->    Dict:
        return (self.red - self.green) / (self.red + self.green)

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

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

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

       def _lowerCAmelCase(		self					)    ->    Any:
        return (self.ndvi() + 0.5) ** (1 / 2)


       def _lowerCAmelCase(		self					)    ->    Union[str, Any]:
        return (self.nir - self.redEdge) / (self.nir + self.redEdge)



"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				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)






