



# Imports
import numpy as np



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None       )     ->		Dict:
          self.set_matricies(red=SCREAMING_SNAKE_CASE_,			green=SCREAMING_SNAKE_CASE_,			blue=SCREAMING_SNAKE_CASE_,			red_edge=SCREAMING_SNAKE_CASE_,			nir=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None       )     ->		Optional[int]:
          if red is not None:
             UpperCamelCase    :  Dict							=	red
          if green is not None:
             UpperCamelCase    :  Optional[Any]							=	green
          if blue is not None:
             UpperCamelCase    :  Optional[int]							=	blue
          if red_edge is not None:
             UpperCamelCase    :  List[str]							=	red_edge
          if nir is not None:
             UpperCamelCase    :  List[Any]							=	nir
          return True
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_="",			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None       )     ->		Union[str, Any]:
          self.set_matricies(red=SCREAMING_SNAKE_CASE_,			green=SCREAMING_SNAKE_CASE_,			blue=SCREAMING_SNAKE_CASE_,			red_edge=SCREAMING_SNAKE_CASE_,			nir=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  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 					snake_case_       (			self       )     ->		Any:
          return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
       def 					snake_case_       (			self       )     ->		List[Any]:
          return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
              (self.nir - self.red) / (self.nir + self.red)
          )
       def 					snake_case_       (			self       )     ->		str:
          return self.nir * (self.red / (self.green**2))
       def 					snake_case_       (			self       )     ->		str:
          return (2 * self.green - self.red - self.blue) / (
              2 * self.green + self.red + self.blue
          )
       def 					snake_case_       (			self       )     ->		Dict:
          return (self.nir - self.red) / (self.nir + self.red)
       def 					snake_case_       (			self       )     ->		Tuple:
          return (self.nir - self.blue) / (self.nir + self.blue)
       def 					snake_case_       (			self       )     ->		List[str]:
          return (self.redEdge - self.red) / (self.redEdge + self.red)
       def 					snake_case_       (			self       )     ->		str:
          return (self.nir - self.green) / (self.nir + self.green)
       def 					snake_case_       (			self       )     ->		Any:
          return (self.nir - (self.green + self.blue)) / (
              self.nir + (self.green + self.blue)
          )
       def 					snake_case_       (			self       )     ->		List[str]:
          return (self.nir - (self.green + self.red)) / (
              self.nir + (self.green + self.red)
          )
       def 					snake_case_       (			self       )     ->		List[Any]:
          return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          return (self.nir - (self.green + self.red + self.blue)) / (
              self.nir + (self.green + self.red + self.blue)
          )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=0.08,			SCREAMING_SNAKE_CASE_=1.22,			SCREAMING_SNAKE_CASE_=0.03       )     ->		Tuple:
          return a * (
              (self.nir - a * self.red - b)
              / (a * self.nir + self.red - a * b + x * (1 + a**2))
          )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
       def 					snake_case_       (			self       )     ->		List[str]:
          return (self.nir / self.green) - 1
       def 					snake_case_       (			self       )     ->		str:
          return (self.nir / self.redEdge) - 1
       def 					snake_case_       (			self       )     ->		Any:
          return (self.red - self.blue) / self.red
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[Any]							=	self.ndvi()
          return ((ndvi + 0.5) / (abs(ndvi + 0.5       ))) * (abs(ndvi + 0.5       ) ** (1 / 2))
       def 					snake_case_       (			self       )     ->		Tuple:
          return self.nir - self.green
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return 2.5 * (
              (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
          )
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	(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 - 0.1_25) / (1 - self.red)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=0.16       )     ->		Union[str, Any]:
          return (self.nir - self.green) / (self.nir + self.green + y)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=0.5       )     ->		Union[str, Any]:
          return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
       def 					snake_case_       (			self       )     ->		str:
          return np.arctan(
              ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None       )     ->		int:
          return (self.nir - b) / (a * self.red)
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
       def 					snake_case_       (			self       )     ->		Any:
          return (self.red + self.green + self.blue) / 30.5
       def 					snake_case_       (			self       )     ->		List[str]:
          return self.nir / self.red
       def 					snake_case_       (			self       )     ->		Any:
          return (self.rvi() - 1) / (self.rvi() + 1)
       def 					snake_case_       (			self       )     ->		List[str]:
          return (
              (2 * self.nir + 1)
              - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
          ) / 2
       def 					snake_case_       (			self       )     ->		str:
          return self.green / (self.nir + self.red + self.green)
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.nir / (self.nir + self.red + self.green)
       def 					snake_case_       (			self       )     ->		Any:
          return self.red / (self.nir + self.red + self.green)
       def 					snake_case_       (			self       )     ->		List[str]:
          return (self.green - self.red) / (self.green + self.red)
       def 					snake_case_       (			self       )     ->		int:
          return (self.red - self.green) / (self.red + self.green)
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Union[str, Any]							=	np.max([np.max(self.red       ), np.max(self.green       ), np.max(self.blue       )]       )
          UpperCamelCase    :  Optional[Any]							=	np.min([np.min(self.red       ), np.min(self.green       ), np.min(self.blue       )]       )
          return (max_value - min_value) / max_value
       def 					snake_case_       (			self       )     ->		List[str]:
          return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
       def 					snake_case_       (			self       )     ->		List[str]:
          return self.nir / self.red
       def 					snake_case_       (			self       )     ->		List[Any]:
          return (self.ndvi() + 0.5) ** (1 / 2)






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








import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__UpperCAmelCase          =       ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
__UpperCAmelCase          =       {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
    raise Exception('''requires fairseq >= 0.9.0''')


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

__UpperCAmelCase          =       ''' Hello world! cécé herlolip'''

__UpperCAmelCase          =       [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def 		UpperCamelCase	(   snake_case__       :    Union[str, Any]	)	->     List[str]:
   UpperCamelCase    :  int							=	[
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Any:
   UpperCamelCase    :  Dict							=	dct.pop(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	val
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   UpperCamelCase    :  int							=	torch.load(snake_case__       ,     map_location='cpu'	)
   UpperCamelCase    :  Dict							=	torch.hub.load('pytorch/fairseq'       ,     'bart.large.cnn'	).eval()
   hub_interface.model.load_state_dict(sd['model']	)
   return hub_interface
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Dict:
   UpperCamelCase	,     UpperCamelCase    :  str							=	emb.weight.shape
   UpperCamelCase    :  Optional[int]							=	nn.Linear(snake_case__       ,     snake_case__       ,     bias=snake_case__	)
   UpperCamelCase    :  List[str]							=	emb.weight.data
   return lin_layer
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=None	)	->     Optional[Any]:
   if not os.path.exists(snake_case__	):
      UpperCamelCase    :  List[str]							=	torch.hub.load('pytorch/fairseq'       ,     snake_case__	).eval()
   else:
      UpperCamelCase    :  int							=	load_xsum_checkpoint(snake_case__	)

   bart.model.upgrade_state_dict(bart.model.state_dict()	)
   if hf_checkpoint_name is None:
      UpperCamelCase    :  Tuple							=	checkpoint_path.replace('.'       ,     '-'	)
   UpperCamelCase    :  Optional[int]							=	BartConfig.from_pretrained(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	bart.encode(snake_case__	).unsqueeze(0	)
   UpperCamelCase    :  Any							=	BartTokenizer.from_pretrained(snake_case__	).encode(snake_case__       ,     return_tensors='pt'	).unsqueeze(0	)
   if not torch.eq(snake_case__       ,     snake_case__	).all():
      raise ValueError(
          F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""	)

   if checkpoint_path == "bart.large.mnli":
      UpperCamelCase    :  Union[str, Any]							=	bart.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  int							=	state_dict['model.decoder.embed_tokens.weight']
      for src, dest in mnli_rename_keys:
         rename_key(snake_case__       ,     snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Any							=	BartForSequenceClassification(snake_case__	).eval()
      model.load_state_dict(snake_case__	)
      UpperCamelCase    :  Any							=	bart.predict('mnli'       ,     snake_case__       ,     return_logits=snake_case__	)
      UpperCamelCase    :  Tuple							=	model(snake_case__	)[0]  # logits
   else:  # no classification heads to worry about
      UpperCamelCase    :  List[str]							=	bart.model.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  List[str]							=	state_dict['decoder.embed_tokens.weight']
      UpperCamelCase    :  Union[str, Any]							=	bart.extract_features(snake_case__	)
      if hf_checkpoint_name == "facebook/bart-large":
         UpperCamelCase    :  List[str]							=	BartModel(snake_case__	).eval()
         model.load_state_dict(snake_case__	)
         UpperCamelCase    :  Optional[int]							=	model(snake_case__	).model[0]
      else:
         UpperCamelCase    :  Union[str, Any]							=	BartForConditionalGeneration(snake_case__	).eval()  # an existing summarization ckpt
         model.model.load_state_dict(snake_case__	)
         if hasattr(snake_case__       ,     'lm_head'	):
            UpperCamelCase    :  Optional[int]							=	make_linear_from_emb(model.model.shared	)
         UpperCamelCase    :  Dict							=	model.model(snake_case__	)[0]

    # Check results
   if fairseq_output.shape != new_model_outputs.shape:
      raise ValueError(
          F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""	)
   if (fairseq_output != new_model_outputs).any().item():
      raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`'	)
   Path(snake_case__	).mkdir(exist_ok=snake_case__	)
   model.save_pretrained(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



