



"""simple docstring"""

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        _UpperCAmelCase       (			lowerCAmelCase__):

 @slow
 @require_torch
 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Optional[int]				=	EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''  ,     '''prajjwal1/bert-tiny'''				)
     snake_case_ :     int				=	BertTokenizer.from_pretrained('''bert-base-uncased'''				)

     snake_case_ :     Optional[int]				=	bertabert.config.encoder.vocab_size
     snake_case_ :     Any				=	tokenizer.sep_token_id
     snake_case_ :     Optional[int]				=	tokenizer.cls_token_id
     snake_case_ :     List[Any]				=	128

     snake_case_ :     Dict				=	datasets.load_dataset('''cnn_dailymail'''  ,     '''3.0.0'''  ,     split='''train[:1%]'''				)
     snake_case_ :     Tuple				=	datasets.load_dataset('''cnn_dailymail'''  ,     '''3.0.0'''  ,     split='''validation[:1%]'''				)

     snake_case_ :     Optional[int]				=	train_dataset.select(range(32				)				)
     snake_case_ :     Any				=	val_dataset.select(range(16				)				)

     snake_case_ :     List[Any]				=	4

     def _map_to_encoder_decoder_inputs(lowercase_		:       Optional[Any]				):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         snake_case_ :     Union[str, Any]				=	tokenizer(batch['''article''']  ,     padding='''max_length'''  ,     truncation=lowercase_  ,     max_length=512				)
         snake_case_ :     List[str]				=	tokenizer(batch['''highlights''']  ,     padding='''max_length'''  ,     truncation=lowercase_  ,     max_length=128				)
         snake_case_ :     Optional[int]				=	inputs.input_ids
         snake_case_ :     int				=	inputs.attention_mask

         snake_case_ :     List[str]				=	outputs.input_ids
         snake_case_ :     Optional[int]				=	outputs.input_ids.copy()
         snake_case_ :     Tuple				=	[
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
         ]
         snake_case_ :     Dict				=	outputs.attention_mask

         assert all(len(lowercase_				) == 512 for x in inputs.input_ids				)
         assert all(len(lowercase_				) == 128 for x in outputs.input_ids				)

         return batch

     def _compute_metrics(lowercase_		:       str				):
         snake_case_ :     Union[str, Any]				=	pred.label_ids
         snake_case_ :     Optional[Any]				=	pred.predictions

         # all unnecessary tokens are removed
         snake_case_ :     Optional[Any]				=	tokenizer.batch_decode(lowercase_  ,     skip_special_tokens=lowercase_				)
         snake_case_ :     List[Any]				=	tokenizer.batch_decode(lowercase_  ,     skip_special_tokens=lowercase_				)

         snake_case_ :     Dict				=	sum([int(pred_str[i] == label_str[i]				) for i in range(len(lowercase_				)				)]				) / len(lowercase_				)

         return {"accuracy": accuracy}

     # map train dataset
     snake_case_ :     List[str]				=	train_dataset.map(
         _map_to_encoder_decoder_inputs  ,     batched=lowercase_  ,     batch_size=lowercase_  ,     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
     snake_case_ :     Any				=	val_dataset.map(
         _map_to_encoder_decoder_inputs  ,     batched=lowercase_  ,     batch_size=lowercase_  ,     remove_columns=['''article''', '''highlights''']  ,     )
     val_dataset.set_format(
         type='''torch'''  ,     columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']  ,     )

     snake_case_ :     Tuple				=	self.get_auto_remove_tmp_dir()

     snake_case_ :     Union[str, Any]				=	SeqaSeqTrainingArguments(
         output_dir=lowercase_  ,     per_device_train_batch_size=lowercase_  ,     per_device_eval_batch_size=lowercase_  ,     predict_with_generate=lowercase_  ,     evaluation_strategy='''steps'''  ,     do_train=lowercase_  ,     do_eval=lowercase_  ,     warmup_steps=0  ,     eval_steps=2  ,     logging_steps=2  ,     )

     # instantiate trainer
     snake_case_ :     Optional[int]				=	SeqaSeqTrainer(
         model=lowercase_  ,     args=lowercase_  ,     compute_metrics=_compute_metrics  ,     train_dataset=lowercase_  ,     eval_dataset=lowercase_  ,     tokenizer=lowercase_  ,     )

     # start training
     trainer.train()










"""simple docstring"""

import copy
import os

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




class        _UpperCAmelCase       :

 def __init__(     self		:       List[Any]				):
     snake_case_ :     List[str]				=	''''''
     snake_case_ :     Tuple				=	''''''
     snake_case_ :     int				=	[]
     snake_case_ :     Optional[int]				=	0
     snake_case_ :     Optional[Any]				=	256
     snake_case_ :     Tuple				=	0
     snake_case_ :     Tuple				=	0
     snake_case_ :     Optional[Any]				=	0
     snake_case_ :     Any				=	0

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       List[Any]				):
     snake_case_ :     List[Any]				=	cva.imread(lowercase_  ,     0				)
     snake_case_ :     Tuple				=	copy.deepcopy(self.img				)
     snake_case_,	snake_case_,	snake_case_ :     List[Any]				=	plt.hist(self.img.ravel()  ,     256  ,     [0, 256]  ,     label='''x'''				)
     snake_case_ :     str				=	np.sum(lowercase_				)
     for i in range(len(lowercase_				)				):
         snake_case_ :     Optional[Any]				=	x[i] / self.k
         self.sk += prk
         snake_case_ :     Any				=	(self.L - 1) * self.sk
         if self.rem != 0:
             snake_case_ :     Dict				=	int(last % last				)
         snake_case_ :     Union[str, Any]				=	int(last + 1 if self.rem >= 0.5 else last				)
         self.last_list.append(lowercase_				)
         snake_case_ :     int				=	int(np.ma.count(self.img				) / self.img[1].size				)
         snake_case_ :     Tuple				=	self.img[1].size
     for i in range(self.number_of_cols				):
         for j in range(self.number_of_rows				):
             snake_case_ :     Union[str, Any]				=	self.img[j][i]
             if num != self.last_list[num]:
                 snake_case_ :     List[str]				=	self.last_list[num]
     cva.imwrite('''output_data/output.jpg'''  ,     self.img				)

 def 						_snake_case					(     self		:       Tuple				):
     plt.hist(self.img.ravel()  ,     256  ,     [0, 256]				)






 def 						_snake_case					(     self		:       int				):
     cva.imshow('''Output-Image'''  ,     self.img				)
     cva.imshow('''Input-Image'''  ,     self.original_image				)
     cva.waitKey(5000				)
     cva.destroyAllWindows()


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





