

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		)   ->					Any:
         self.set_matricies(red=lowercase_    ,						green=lowercase_    ,						blue=lowercase_    ,						red_edge=lowercase_    ,						nir=lowercase_		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None		)   ->					List[Any]:
         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		)   ->					List[str]:
         self.set_matricies(red=lowercase_    ,						green=lowercase_    ,						blue=lowercase_    ,						red_edge=lowercase_    ,						nir=lowercase_		)
         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		)   ->					Optional[int]:
         return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))





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





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





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





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





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





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





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





      def 					_lowerCamelCase  (  self		)   ->					Optional[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    ,						SCREAMING_SNAKE_CASE__=0.08    ,						SCREAMING_SNAKE_CASE__=1.22    ,						SCREAMING_SNAKE_CASE__=0.03		)   ->					List[Any]:
         return a * (
             (self.nir - a * self.red - b)
             / (a * self.nir + self.red - a * b + x * (1 + a**2))
         )





      def 					_lowerCamelCase  (  self		)   ->					int:
         return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)





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





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         return (self.nir / self.redEdge) - 1





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return (self.red - self.blue) / self.red





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.ndvi()
         return ((ndvi + 0.5) / (abs(ndvi + 0.5		))) * (abs(ndvi + 0.5		) ** (1 / 2))





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         return self.nir - self.green





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





      def 					_lowerCamelCase  (  self		)   ->					str:
         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 - 0.1_25) / (1 - self.red)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=0.16		)   ->					List[Any]:
         return (self.nir - self.green) / (self.nir + self.green + y)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=0.5		)   ->					List[str]:
         return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         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		)   ->					Tuple:
         return (self.nir - b) / (a * self.red)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         return (self.red + self.green + self.blue) / 30.5





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





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





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





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





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





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





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





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





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





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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         return (self.ndvi() + 0.5) ** (1 / 2)





      def 					_lowerCamelCase  (  self		)   ->					int:
         return (self.nir - self.redEdge) / (self.nir + self.redEdge)







"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.getLogger(__name__)


@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,       )
      __a								=	field(default=__a				,       metadata={'help': 'Whether tp freeze the encoder.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Whether  to freeze the embeddings.'}					)






@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
      __a								=	field(
          default='summarization'				,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,       )
      __a								=	field(
          default=1024				,       metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=128				,       metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(default=-1				,       metadata={'help': '# training examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# validation examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# test examples. -1 means use all.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Source language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Target language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': '# num_beams to use for evaluation.'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}				,       )






def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Union[str, Any]:




   """simple docstring"""
   logger.info(f'***** {split} metrics *****'   )
   for key in sorted(metrics.keys()   ):
      logger.info(f'  {key} = {metrics[key]}'   )
   save_json(A__	,    os.path.join(A__	,    f'{split}_results.json'   )   )



def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowerCamelCase__						=			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

   if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_args_into_dataclasses()

   check_output_dir(A__   )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,    datefmt="%m/%d/%Y %H:%M:%S"	,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,    )
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"	,    training_args.local_rank	,    training_args.device	,    training_args.n_gpu	,    bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )	,    training_args.fpaa	,    )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank   ):
      transformers.utils.logging.set_verbosity_info()
   logger.info("Training/evaluation parameters %s"	,    A__   )

   # Set seed
   set_seed(training_args.seed   )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   lowerCamelCase__						=			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )

   lowerCamelCase__						=			("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(A__	,    A__	,    A__   ):
         assert hasattr(A__	,    A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
         setattr(A__	,    A__	,    getattr(A__	,    A__   )   )

   lowerCamelCase__						=			AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )
   lowerCamelCase__						=			AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path	,    from_tf=".ckpt" in model_args.model_name_or_path	,    config=A__	,    cache_dir=model_args.cache_dir	,    )

   # use task specific params
   use_task_specific_params(A__	,    data_args.task   )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowerCamelCase__						=			model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(A__	,    (MBartTokenizer, MBartTokenizerFast)   ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(A__	,    A__   ):
         lowerCamelCase__						=			tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

   if model_args.freeze_embeds:
      freeze_embeds(A__   )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()   )
      assert_all_frozen(model.get_encoder()   )

   lowerCamelCase__						=			SeqaSeqDataset

   # Get datasets
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="train"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_train	,    max_target_length=data_args.max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_train
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="val"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_val	,    max_target_length=data_args.val_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="test"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_test	,    max_target_length=data_args.test_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowerCamelCase__						=			(
       build_compute_metrics_fn(data_args.task	,    A__   ) if training_args.predict_with_generate else None
   )
   lowerCamelCase__						=			SeqaSeqTrainer(
       model=A__	,    args=A__	,    data_args=A__	,    train_dataset=A__	,    eval_dataset=A__	,    data_collator=SeqaSeqDataCollator(
           A__	,    A__	,    model.config.decoder_start_token_id	,    training_args.tpu_num_cores   )	,    compute_metrics=A__	,    tokenizer=A__	,    )

   lowerCamelCase__						=			{}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"   )

      lowerCamelCase__						=			trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
      lowerCamelCase__						=			train_result.metrics
      lowerCamelCase__						=			data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir	,    "trainer_state.json"   )   )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"   )

      lowerCamelCase__						=			trainer.evaluate(metric_key_prefix="val"   )
      lowerCamelCase__						=			data_args.n_val
      lowerCamelCase__						=			round(metrics["val_loss"]	,    4   )

      if trainer.is_world_process_zero():
         handle_metrics("val"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

   if training_args.do_predict:
      logger.info("*** Predict ***"   )

      lowerCamelCase__						=			trainer.predict(test_dataset=A__	,    metric_key_prefix="test"   )
      lowerCamelCase__						=			test_output.metrics
      lowerCamelCase__						=			data_args.n_test

      if trainer.is_world_process_zero():
         lowerCamelCase__						=			round(metrics["test_loss"]	,    4   )
         handle_metrics("test"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         if training_args.predict_with_generate:
            lowerCamelCase__						=			tokenizer.batch_decode(
                test_output.predictions	,    skip_special_tokens=A__	,    clean_up_tokenization_spaces=A__   )
            lowerCamelCase__						=			lmap(str.strip	,    A__   )
            write_txt_file(A__	,    os.path.join(training_args.output_dir	,    "test_generations.txt"   )   )

   if trainer.is_world_process_zero():
      save_json(A__	,    os.path.join(training_args.output_dir	,    "all_results.json"   )   )

   return all_metrics



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()




