

"""simple docstring"""




from __future__ import annotations

from collections.abc import MutableSequence







class   __lowercase      :

  def __init__(	self		:      Dict       ,A		:      int       ,A		:      MutableSequence[float]     ):






   '''simple docstring'''




   if len(_SCREAMING_SNAKE_CASE     ) != degree + 1:
    raise ValueError(
        """The number of coefficients should be equal to the degree + 1."""     )

   UpperCAmelCase__       :	Tuple        =       list(_SCREAMING_SNAKE_CASE     )
   UpperCAmelCase__       :	int        =       degree

  def __add__(	self		:      Any       ,A		:      Polynomial     ):






   '''simple docstring'''




   if self.degree > polynomial_a.degree:
    UpperCAmelCase__       :	Dict        =       self.coefficients[:]
    for i in range(polynomial_a.degree + 1     ):
     coefficients[i] += polynomial_a.coefficients[i]
    return Polynomial(self.degree       ,_SCREAMING_SNAKE_CASE     )
   else:
    UpperCAmelCase__       :	Any        =       polynomial_a.coefficients[:]
    for i in range(self.degree + 1     ):
     coefficients[i] += self.coefficients[i]
    return Polynomial(polynomial_a.degree       ,_SCREAMING_SNAKE_CASE     )

  def __sub__(	self		:      Optional[int]       ,A		:      Polynomial     ):






   '''simple docstring'''
   return self + polynomial_a * Polynomial(0       ,[-1]     )

  def __neg__(	self		:      Any     ):






   '''simple docstring'''
   return Polynomial(self.degree       ,[-c for c in self.coefficients]     )

  def __mul__(	self		:      Union[str, Any]       ,A		:      Polynomial     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       [0] * (self.degree + polynomial_a.degree + 1)
   for i in range(self.degree + 1     ):
    for j in range(polynomial_a.degree + 1     ):
     coefficients[i + j] += (
         self.coefficients[i] * polynomial_a.coefficients[j]
     )

   return Polynomial(self.degree + polynomial_a.degree       ,_SCREAMING_SNAKE_CASE     )

  def  __lowercase							(	self		:      Optional[int]       ,A		:      int | float     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       0
   for i in range(self.degree + 1     ):
    result += self.coefficients[i] * (substitution**i)
   return result

  def __str__(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       """"""
   for i in range(self.degree       ,-1       ,-1     ):
    if self.coefficients[i] == 0:
     continue
    elif self.coefficients[i] > 0:
     if polynomial:
      polynomial += " + "
    else:
     polynomial += " - "

    if i == 0:
     polynomial += str(abs(self.coefficients[i]     )     )
    elif i == 1:
     polynomial += str(abs(self.coefficients[i]     )     ) + "x"
    else:
     polynomial += str(abs(self.coefficients[i]     )     ) + "x^" + str(_SCREAMING_SNAKE_CASE     )

   return polynomial

  def __repr__(	self		:      str     ):






   '''simple docstring'''
   return self.__str__()

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       [0] * self.degree
   for i in range(self.degree     ):
    UpperCAmelCase__       :	Any        =       self.coefficients[i + 1] * (i + 1)
   return Polynomial(self.degree - 1       ,_SCREAMING_SNAKE_CASE     )

  def  __lowercase							(	self		:      Optional[int]       ,A		:      int | float = 0     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       [0] * (self.degree + 2)
   UpperCAmelCase__       :	List[Any]        =       constant
   for i in range(self.degree + 1     ):
    UpperCAmelCase__       :	List[Any]        =       self.coefficients[i] / (i + 1)
   return Polynomial(self.degree + 1       ,_SCREAMING_SNAKE_CASE     )

  def __eq__(	self		:      Any       ,A		:      object     ):






   '''simple docstring'''




   if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE     ):
    return False

   if self.degree != polynomial_a.degree:
    return False

   for i in range(self.degree + 1     ):
    if self.coefficients[i] != polynomial_a.coefficients[i]:
     return False

   return True




  def __ne__(	self		:      str       ,A		:      object     ):






   '''simple docstring'''
   return not self.__eq__(_SCREAMING_SNAKE_CASE     )













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 					A__     (     __snake_case ):

     '''simple docstring'''






     @slow
     @require_torch
     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           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					       =  128

           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(32 ) )
           UpperCamelCase					       =  val_dataset.select(range(16 ) )

           UpperCamelCase					       =  4

           def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE      :    Tuple ):
                 # Tokenizer will automatically set [BOS] <text> [EOS]
                 UpperCamelCase					       =  tokenizer(batch['article']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=512 )
                 UpperCamelCase					       =  tokenizer(batch['highlights']   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						max_length=128 )
                 UpperCamelCase					       =  inputs.input_ids
                 UpperCamelCase					       =  inputs.attention_mask

                 UpperCamelCase					       =  outputs.input_ids
                 UpperCamelCase					       =  outputs.input_ids.copy()
                 UpperCamelCase					       =  [
                     [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                 ]
                 UpperCamelCase					       =  outputs.attention_mask

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

                 return batch

           def _compute_metrics(_SCREAMING_SNAKE_CASE      :    str ):
                 UpperCamelCase					       =  pred.label_ids
                 UpperCamelCase					       =  pred.predictions

                 # all unnecessary tokens are removed
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE   ,						skip_special_tokens=_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  sum([int(pred_str[i] == label_str[i] ) for i in range(len(_SCREAMING_SNAKE_CASE ) )] ) / len(_SCREAMING_SNAKE_CASE )

                 return {"accuracy": accuracy}

           # map train dataset
           UpperCamelCase					       =  train_dataset.map(
               _map_to_encoder_decoder_inputs   ,						batched=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						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=_SCREAMING_SNAKE_CASE   ,						batch_size=_SCREAMING_SNAKE_CASE   ,						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=_SCREAMING_SNAKE_CASE   ,						per_device_train_batch_size=_SCREAMING_SNAKE_CASE   ,						per_device_eval_batch_size=_SCREAMING_SNAKE_CASE   ,						predict_with_generate=_SCREAMING_SNAKE_CASE   ,						evaluation_strategy='steps'   ,						do_train=_SCREAMING_SNAKE_CASE   ,						do_eval=_SCREAMING_SNAKE_CASE   ,						warmup_steps=0   ,						eval_steps=2   ,						logging_steps=2   ,						)

           # instantiate trainer
           UpperCamelCase					       =  SeqaSeqTrainer(
               model=_SCREAMING_SNAKE_CASE   ,						args=_SCREAMING_SNAKE_CASE   ,						compute_metrics=_compute_metrics   ,						train_dataset=_SCREAMING_SNAKE_CASE   ,						eval_dataset=_SCREAMING_SNAKE_CASE   ,						tokenizer=_SCREAMING_SNAKE_CASE   ,						)

           # start training
           trainer.train()



