




'''simple docstring'''







import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


lowercase      :   Dict      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					"""summarization"""
     lowerCAmelCase_  :     Optional[int]          =					["""loss"""]
     lowerCAmelCase_  :     Union[str, Any]          =					ROUGE_KEYS
     lowerCAmelCase_  :     Optional[int]          =					"""rouge2"""







     def __init__(				self							,__a							,**__a							)      ->				Any:
          if hparams.sortish_sampler and hparams.gpus > 1:
               snake_case			:      List[str]        =			False
          elif hparams.max_tokens_per_batch is not None:
               if hparams.gpus > 1:
                    raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training"""							)
               if hparams.sortish_sampler:
                    raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"""							)

          super().__init__(__a							,num_labels=__a							,mode=self.mode							,**__a							)
          use_task_specific_params(self.model							,"""summarization"""							)
          save_git_info(self.hparams.output_dir							)
          snake_case			:      int        =			Path(self.output_dir							) / """metrics.json"""
          snake_case			:      str        =			Path(self.output_dir							) / """hparams.pkl"""
          pickle_save(self.hparams							,self.hparams_save_path							)
          snake_case			:      Union[str, Any]        =			0
          snake_case			:      Union[str, Any]        =			defaultdict(__a							)
          snake_case			:      str        =			self.config.model_type
          snake_case			:      Optional[Any]        =			self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

          snake_case			:      dict        =			{
              "data_dir": self.hparams.data_dir,
              "max_source_length": self.hparams.max_source_length,
              "prefix": self.model.config.prefix or "",
          }
          snake_case			:      Optional[int]        =			{
              """train""": self.hparams.n_train,
              """val""": self.hparams.n_val,
              """test""": self.hparams.n_test,
          }
          snake_case			:      str        =			{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

          snake_case			:      str        =			{
              """train""": self.hparams.max_target_length,
              """val""": self.hparams.val_max_target_length,
              """test""": self.hparams.test_max_target_length,
          }
          assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
          assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
          if self.hparams.freeze_embeds:
               freeze_embeds(self.model							)
          if self.hparams.freeze_encoder:
               freeze_params(self.model.get_encoder()							)
               assert_all_frozen(self.model.get_encoder()							)

          snake_case			:      Union[str, Any]        =			get_git_info()["""repo_sha"""]
          snake_case			:      List[str]        =			hparams.num_workers
          snake_case			:      List[str]        =			None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,__a							):
               snake_case			:      Dict        =			self.tokenizer.lang_code_to_id[hparams.tgt_lang]
               snake_case			:      Dict        =			self.decoder_start_token_id
          snake_case			:      Optional[int]        =			(
              SeqaSeqDataset if hasattr(self.tokenizer							,"""prepare_seq2seq_batch"""							) else LegacySeqaSeqDataset
          )
          snake_case			:      Tuple        =			False
          snake_case			:      Union[str, Any]        =			self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
               snake_case			:      List[str]        =			self.hparams.eval_max_gen_length
          else:
               snake_case			:      Tuple        =			self.model.config.max_length
          snake_case			:      List[Any]        =			self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







     def 			snake_case_      (				self							,__a							)      ->				Dict[str, List[str]]:
          snake_case			:      Any        =			{
              k: self.tokenizer.batch_decode(v.tolist()							) if """mask""" not in k else v.shape for k, v in batch.items()
          }
          save_json(__a							,Path(self.output_dir							) / """text_batch.json"""							)
          save_json({k: v.tolist() for k, v in batch.items()}							,Path(self.output_dir							) / """tok_batch.json"""							)

          snake_case			:      Dict        =			True
          return readable_batch







     def 			snake_case_      (				self							,__a							,**__a							)      ->				Tuple:
          return self.model(__a							,**__a							)







     def 			snake_case_      (				self							,__a							)      ->				List[Any]:
          snake_case			:      Union[str, Any]        =			self.tokenizer.batch_decode(
              __a							,skip_special_tokens=__a							,clean_up_tokenization_spaces=__a							)
          return lmap(str.strip							,__a							)







     def 			snake_case_      (				self							,__a							)      ->				Tuple:
          snake_case			:      Optional[int]        =			self.tokenizer.pad_token_id
          snake_case ,		snake_case			:      Optional[Any]        =			batch["""input_ids"""], batch["""attention_mask"""]
          snake_case			:      Tuple        =			batch["""labels"""]
          if isinstance(self.model							,__a							):
               snake_case			:      Dict        =			self.model._shift_right(__a							)
          else:
               snake_case			:      Tuple        =			shift_tokens_right(__a							,__a							)
          if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
               snake_case			:      Union[str, Any]        =			decoder_input_ids
               self.save_readable_batch(__a							)

          snake_case			:      List[Any]        =			self(__a							,attention_mask=__a							,decoder_input_ids=__a							,use_cache=__a							)
          snake_case			:      int        =			outputs["""logits"""]
          if self.hparams.label_smoothing == 0:
               # Same behavior as modeling_bart.py, besides ignoring pad_token_id
               snake_case			:      Any        =			nn.CrossEntropyLoss(ignore_index=__a							)

               assert lm_logits.shape[-1] == self.vocab_size
               snake_case			:      Any        =			ce_loss_fct(lm_logits.view(-1							,lm_logits.shape[-1]							)							,tgt_ids.view(-1							)							)
          else:
               snake_case			:      Dict        =			nn.functional.log_softmax(__a							,dim=-1							)
               snake_case ,		snake_case			:      Optional[int]        =			label_smoothed_nll_loss(
                   __a							,__a							,self.hparams.label_smoothing							,ignore_index=__a							)
          return (loss,)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.tokenizer.pad_token_id







     def 			snake_case_      (				self							,__a							,__a							)      ->				Dict:
          snake_case			:      Tuple        =			self._step(__a							)

          snake_case			:      Any        =			dict(zip(self.loss_names							,__a							)							)
          # tokens per batch
          snake_case			:      Optional[Any]        =			batch["""input_ids"""].ne(self.pad							).sum() + batch["""labels"""].ne(self.pad							).sum()
          snake_case			:      Dict        =			batch["""input_ids"""].shape[0]
          snake_case			:      str        =			batch["""input_ids"""].eq(self.pad							).sum()
          snake_case			:      List[Any]        =			batch["""input_ids"""].eq(self.pad							).float().mean()
          # TODO(SS): make a wandb summary metric for this
          return {"loss": loss_tensors[0], "log": logs}







     def 			snake_case_      (				self							,__a							,__a							)      ->				Dict:
          return self._generative_step(__a							)







     def 			snake_case_      (				self							,__a							,__a="val"							)      ->				Dict:
          self.step_count += 1
          snake_case			:      Tuple        =			{k: torch.stack([x[k] for x in outputs]							).mean() for k in self.loss_names}
          snake_case			:      int        =			losses["""loss"""]
          snake_case			:      int        =			{
              k: np.array([x[k] for x in outputs]							).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
          }
          snake_case			:      List[str]        =			(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          snake_case			:      torch.FloatTensor        =			torch.tensor(__a							).type_as(__a							)
          generative_metrics.update({k: v.item() for k, v in losses.items()}							)
          losses.update(__a							)
          snake_case			:      Optional[int]        =			{F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
          snake_case			:      List[str]        =			self.step_count
          self.metrics[prefix].append(__a							)  # callback writes this to self.metrics_save_path
          snake_case			:      List[Any]        =			flatten_list([x["""preds"""] for x in outputs]							)
          return {
              "log": all_metrics,
              "preds": preds,
              F'''{prefix}_loss''': loss,
              F'''{prefix}_{self.val_metric}''': metric_tensor,
          }







     def 			snake_case_      (				self							,__a							,__a							)      ->				Dict:
          return calculate_rouge(__a							,__a							)







     def 			snake_case_      (				self							,__a							)      ->				dict:
          snake_case			:      Union[str, Any]        =			time.time()

          # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
          snake_case			:      List[str]        =			self.model.generate(
              batch["""input_ids"""]							,attention_mask=batch["""attention_mask"""]							,use_cache=__a							,decoder_start_token_id=self.decoder_start_token_id							,num_beams=self.eval_beams							,max_length=self.eval_max_length							,)
          snake_case			:      Union[str, Any]        =			(time.time() - ta) / batch["""input_ids"""].shape[0]
          snake_case			:      List[str]        =			self.ids_to_clean_text(__a							)
          snake_case			:      List[str]        =			self.ids_to_clean_text(batch["""labels"""]							)
          snake_case			:      Optional[int]        =			self._step(__a							)
          snake_case			:      List[Any]        =			dict(zip(self.loss_names							,__a							)							)
          snake_case			:      Dict        =			self.calc_generative_metrics(__a							,__a							)
          snake_case			:      Dict        =			np.mean(lmap(__a							,__a							)							)
          base_metrics.update(gen_time=__a							,gen_len=__a							,preds=__a							,target=__a							,**__a							)
          return base_metrics







     def 			snake_case_      (				self							,__a							,__a							)      ->				List[Any]:
          return self._generative_step(__a							)







     def 			snake_case_      (				self							,__a							)      ->				Union[str, Any]:
          return self.validation_epoch_end(__a							,prefix="""test"""							)







     def 			snake_case_      (				self							,__a							)      ->				SeqaSeqDataset:
          snake_case			:      List[Any]        =			self.n_obs[type_path]
          snake_case			:      Optional[Any]        =			self.target_lens[type_path]
          snake_case			:      str        =			self.dataset_class(
              self.tokenizer							,type_path=__a							,n_obs=__a							,max_target_length=__a							,**self.dataset_kwargs							,)
          return dataset







     def 			snake_case_      (				self							,__a							,__a							,__a = False							)      ->				DataLoader:
          snake_case			:      Any        =			self.get_dataset(__a							)

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
               snake_case			:      str        =			dataset.make_sortish_sampler(__a							,distributed=self.hparams.gpus > 1							)
               return DataLoader(
                   __a							,batch_size=__a							,collate_fn=dataset.collate_fn							,shuffle=__a							,num_workers=self.num_workers							,sampler=__a							,)

          elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
               snake_case			:      Union[str, Any]        =			dataset.make_dynamic_sampler(
                   self.hparams.max_tokens_per_batch							,distributed=self.hparams.gpus > 1							)
               return DataLoader(
                   __a							,batch_sampler=__a							,collate_fn=dataset.collate_fn							,num_workers=self.num_workers							,)
          else:
               return DataLoader(
                   __a							,batch_size=__a							,collate_fn=dataset.collate_fn							,shuffle=__a							,num_workers=self.num_workers							,sampler=__a							,)







     def 			snake_case_      (				self							)      ->				DataLoader:
          snake_case			:      str        =			self.get_dataloader("""train"""							,batch_size=self.hparams.train_batch_size							,shuffle=__a							)
          return dataloader







     def 			snake_case_      (				self							)      ->				DataLoader:
          return self.get_dataloader("""val"""							,batch_size=self.hparams.eval_batch_size							)







     def 			snake_case_      (				self							)      ->				DataLoader:
          return self.get_dataloader("""test"""							,batch_size=self.hparams.eval_batch_size							)







     @staticmethod
     def 			snake_case_      (				__a							,__a							)      ->				Union[str, Any]:
          BaseTransformer.add_model_specific_args(__a							,__a							)
          add_generic_args(__a							,__a							)
          parser.add_argument(
              """--max_source_length"""							,default=1_024							,type=__a							,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )							,)
          parser.add_argument(
              """--max_target_length"""							,default=56							,type=__a							,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )							,)
          parser.add_argument(
              """--val_max_target_length"""							,default=142							,type=__a							,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )							,)
          parser.add_argument(
              """--test_max_target_length"""							,default=142							,type=__a							,help=(
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )							,)
          parser.add_argument("""--freeze_encoder"""							,action="""store_true"""							)
          parser.add_argument("""--freeze_embeds"""							,action="""store_true"""							)
          parser.add_argument("""--sortish_sampler"""							,action="""store_true"""							,default=__a							)
          parser.add_argument("""--overwrite_output_dir"""							,action="""store_true"""							,default=__a							)
          parser.add_argument("""--max_tokens_per_batch"""							,type=__a							,default=__a							)
          parser.add_argument("""--logger_name"""							,type=__a							,choices=["""default""", """wandb""", """wandb_shared"""]							,default="""default"""							)
          parser.add_argument("""--n_train"""							,type=__a							,default=-1							,required=__a							,help="""# examples. -1 means use all."""							)
          parser.add_argument("""--n_val"""							,type=__a							,default=500							,required=__a							,help="""# examples. -1 means use all."""							)
          parser.add_argument("""--n_test"""							,type=__a							,default=-1							,required=__a							,help="""# examples. -1 means use all."""							)
          parser.add_argument(
              """--task"""							,type=__a							,default="""summarization"""							,required=__a							,help="""# examples. -1 means use all."""							)
          parser.add_argument("""--label_smoothing"""							,type=__a							,default=0.0							,required=__a							)
          parser.add_argument("""--src_lang"""							,type=__a							,default=""""""							,required=__a							)
          parser.add_argument("""--tgt_lang"""							,type=__a							,default=""""""							,required=__a							)
          parser.add_argument("""--eval_beams"""							,type=__a							,default=__a							,required=__a							)
          parser.add_argument(
              """--val_metric"""							,type=__a							,default=__a							,required=__a							,choices=["""bleu""", """rouge2""", """loss""", None]							)
          parser.add_argument("""--eval_max_gen_length"""							,type=__a							,default=__a							,help="""never generate more than n tokens"""							)
          parser.add_argument("""--save_top_k"""							,type=__a							,default=1							,required=__a							,help="""How many checkpoints to save"""							)
          parser.add_argument(
              """--early_stopping_patience"""							,type=__a							,default=-1							,required=__a							,help=(
                  """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                  """ val_check_interval will effect it."""
              )							,)
          return parser







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					"""translation"""
     lowerCAmelCase_  :     int          =					["""loss"""]
     lowerCAmelCase_  :     Optional[Any]          =					["""bleu"""]
     lowerCAmelCase_  :     List[str]          =					"""bleu"""







     def __init__(				self							,__a							,**__a							)      ->				str:
          super().__init__(__a							,**__a							)
          snake_case			:      Tuple        =			hparams.src_lang
          snake_case			:      Dict        =			hparams.tgt_lang







     def 			snake_case_      (				self							,__a							,__a							)      ->				dict:
          return calculate_bleu(__a							,__a							)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase=None				):
     Path(args.output_dir				).mkdir(exist_ok=__lowercase				)
     check_output_dir(__lowercase     ,   expected_items=3				)

     if model is None:
          if "summarization" in args.task:
               snake_case			:      SummarizationModule        =			SummarizationModule(__lowercase				)
          else:
               snake_case			:      SummarizationModule        =			TranslationModule(__lowercase				)
     snake_case			:      List[Any]        =			Path(args.data_dir				).name
     if (
         args.logger_name == "default"
         or args.fast_dev_run
         or str(args.output_dir				).startswith("""/tmp"""				)
         or str(args.output_dir				).startswith("""/var"""				)
     ):
          snake_case			:      List[str]        =			True  # don't pollute wandb logs unnecessarily
     elif args.logger_name == "wandb":
          from pytorch_lightning.loggers import WandbLogger

          snake_case			:      Optional[Any]        =			os.environ.get("""WANDB_PROJECT"""     ,   __lowercase				)
          snake_case			:      str        =			WandbLogger(name=model.output_dir.name     ,   project=__lowercase				)

     elif args.logger_name == "wandb_shared":
          from pytorch_lightning.loggers import WandbLogger

          snake_case			:      int        =			WandbLogger(name=model.output_dir.name     ,   project=F'''hf_{dataset}'''				)

     if args.early_stopping_patience >= 0:
          snake_case			:      Optional[int]        =			get_early_stopping_callback(model.val_metric     ,   args.early_stopping_patience				)
     else:
          snake_case			:      str        =			False

     snake_case			:      Optional[int]        =			args.val_metric == """loss"""
     snake_case			:      pl.Trainer        =			generic_train(
         __lowercase     ,   __lowercase     ,   logging_callback=SeqaSeqLoggingCallback()     ,   checkpoint_callback=get_checkpoint_callback(
             args.output_dir     ,   model.val_metric     ,   args.save_top_k     ,   __lowercase				)     ,   early_stopping_callback=__lowercase     ,   logger=__lowercase     ,   )
     pickle_save(model.hparams     ,   model.output_dir / """hparams.pkl"""				)
     if not args.do_predict:
          return model

     snake_case			:      str        =			""""""
     snake_case			:      Tuple        =			sorted(glob.glob(os.path.join(args.output_dir     ,   """*.ckpt"""				)     ,   recursive=__lowercase				)				)
     if checkpoints:
          snake_case			:      int        =			checkpoints[-1]
          snake_case			:      List[str]        =			checkpoints[-1]
     trainer.logger.log_hyperparams(model.hparams				)

     # test() without a model tests using the best checkpoint automatically
     trainer.test()
     return model


if __name__ == "__main__":
      lowercase      :   Any      =       argparse.ArgumentParser()
      lowercase      :   Optional[int]      =       pl.Trainer.add_argparse_args(parser)
      lowercase      :   List[str]      =       SummarizationModule.add_model_specific_args(parser, os.getcwd())

      lowercase      :   str      =       parser.parse_args()

      main(args)









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			np.zeros((image.shape[0], image.shape[1])				)

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0     ,   image.shape[0]				):
          for j in range(0     ,   image.shape[1]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



