

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
      import datasets






class 						__magic_name__    (       _snake_case    ):


 @slow
 @require_torch
 def _UpperCamelCase	(						self		: List[Any]    )    ->      List[str]:
   UpperCAmelCase					      =  EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"			,						"prajjwal1/bert-tiny"    )
   UpperCAmelCase					      =  BertTokenizer.from_pretrained("bert-base-uncased"    )

   UpperCAmelCase					      =  bertabert.config.encoder.vocab_size
   UpperCAmelCase					      =  tokenizer.sep_token_id
   UpperCAmelCase					      =  tokenizer.cls_token_id
   UpperCAmelCase					      =  1_2_8

   UpperCAmelCase					      =  datasets.load_dataset("cnn_dailymail"			,						"3.0.0"			,						split="train[:1%]"    )
   UpperCAmelCase					      =  datasets.load_dataset("cnn_dailymail"			,						"3.0.0"			,						split="validation[:1%]"    )

   UpperCAmelCase					      =  train_dataset.select(range(3_2    )    )
   UpperCAmelCase					      =  val_dataset.select(range(1_6    )    )

   UpperCAmelCase					      =  4

   def _map_to_encoder_decoder_inputs(lowerCAmelCase__		: int    ):
     # Tokenizer will automatically set [BOS] <text> [EOS]
     UpperCAmelCase					      =  tokenizer(batch["article"]			,						padding="max_length"			,						truncation=lowerCAmelCase__			,						max_length=5_1_2    )
     UpperCAmelCase					      =  tokenizer(batch["highlights"]			,						padding="max_length"			,						truncation=lowerCAmelCase__			,						max_length=1_2_8    )
     UpperCAmelCase					      =  inputs.input_ids
     UpperCAmelCase					      =  inputs.attention_mask

     UpperCAmelCase					      =  outputs.input_ids
     UpperCAmelCase					      =  outputs.input_ids.copy()
     UpperCAmelCase					      =  [
         [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
     ]
     UpperCAmelCase					      =  outputs.attention_mask

     assert all(len(lowerCAmelCase__    ) == 5_1_2 for x in inputs.input_ids    )
     assert all(len(lowerCAmelCase__    ) == 1_2_8 for x in outputs.input_ids    )

     return batch

   def _compute_metrics(lowerCAmelCase__		: Optional[Any]    ):
     UpperCAmelCase					      =  pred.label_ids
     UpperCAmelCase					      =  pred.predictions

     # all unnecessary tokens are removed
     UpperCAmelCase					      =  tokenizer.batch_decode(lowerCAmelCase__			,						skip_special_tokens=lowerCAmelCase__    )
     UpperCAmelCase					      =  tokenizer.batch_decode(lowerCAmelCase__			,						skip_special_tokens=lowerCAmelCase__    )

     UpperCAmelCase					      =  sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCAmelCase__    )    )]    ) / len(lowerCAmelCase__    )

     return {"accuracy": accuracy}

   # map train dataset
   UpperCAmelCase					      =  train_dataset.map(
       _map_to_encoder_decoder_inputs			,						batched=lowerCAmelCase__			,						batch_size=lowerCAmelCase__			,						remove_columns=["article", "highlights"]			,						)
   train_dataset.set_format(
       type="torch"			,						columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]			,						)

   # same for validation dataset
   UpperCAmelCase					      =  val_dataset.map(
       _map_to_encoder_decoder_inputs			,						batched=lowerCAmelCase__			,						batch_size=lowerCAmelCase__			,						remove_columns=["article", "highlights"]			,						)
   val_dataset.set_format(
       type="torch"			,						columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]			,						)

   UpperCAmelCase					      =  self.get_auto_remove_tmp_dir()

   UpperCAmelCase					      =  SeqaSeqTrainingArguments(
       output_dir=lowerCAmelCase__			,						per_device_train_batch_size=lowerCAmelCase__			,						per_device_eval_batch_size=lowerCAmelCase__			,						predict_with_generate=lowerCAmelCase__			,						evaluation_strategy="steps"			,						do_train=lowerCAmelCase__			,						do_eval=lowerCAmelCase__			,						warmup_steps=0			,						eval_steps=2			,						logging_steps=2			,						)

   # instantiate trainer
   UpperCAmelCase					      =  SeqaSeqTrainer(
       model=lowerCAmelCase__			,						args=lowerCAmelCase__			,						compute_metrics=_compute_metrics			,						train_dataset=lowerCAmelCase__			,						eval_dataset=lowerCAmelCase__			,						tokenizer=lowerCAmelCase__			,						)

   # start training
   trainer.train()






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt






class 						__magic_name__    :


 def __init__(						self		: Optional[int]    )    ->      Optional[Any]:
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  ""
   UpperCAmelCase					      =  []
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  2_5_6
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0
   UpperCAmelCase					      =  0


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Optional[Any]    )    ->      List[str]:
   UpperCAmelCase					      =  cva.imread(lowerCAmelCase__			,						0    )
   UpperCAmelCase					      =  copy.deepcopy(self.img    )
   UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]			,						label="x"    )
   UpperCAmelCase					      =  np.sum(lowerCAmelCase__    )
   for i in range(len(lowerCAmelCase__    )    ):
     UpperCAmelCase					      =  x[i] / self.k
     self.sk += prk
     UpperCAmelCase					      =  (self.L - 1) * self.sk
     if self.rem != 0:
       UpperCAmelCase					      =  int(last % last    )
     UpperCAmelCase					      =  int(last + 1 if self.rem >= 0.5 else last    )
     self.last_list.append(lowerCAmelCase__    )
     UpperCAmelCase					      =  int(np.ma.count(self.img    ) / self.img[1].size    )
     UpperCAmelCase					      =  self.img[1].size
   for i in range(self.number_of_cols    ):
     for j in range(self.number_of_rows    ):
       UpperCAmelCase					      =  self.img[j][i]
       if num != self.last_list[num]:
         UpperCAmelCase					      =  self.last_list[num]
   cva.imwrite("output_data/output.jpg"			,						self.img    )


 def _UpperCamelCase	(						self		: str    )    ->      int:
   plt.hist(self.img.ravel()			,						2_5_6			,						[0, 2_5_6]    )


 def _UpperCamelCase	(						self		: Dict    )    ->      Optional[Any]:
   cva.imshow("Output-Image"			,						self.img    )
   cva.imshow("Input-Image"			,						self.original_image    )
   cva.waitKey(5_0_0_0    )
   cva.destroyAllWindows()


if __name__ == "__main__":
      lowerCAmelCase__       = os.path.join(os.path.basename(__file__), "image_data/input.jpg")
      lowerCAmelCase__       = ConstantStretch()
      stretcher.stretch(file_path)
      stretcher.plot_histogram()
      stretcher.show_image()



