

"""simple docstring"""




import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 return x + 2







class   __lowercase      (     unittest.TestCase       ):

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       """x = 3"""
   UpperCAmelCase__       :	Tuple        =       {}
   UpperCAmelCase__       :	Optional[Any]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 3
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3}     )

   UpperCAmelCase__       :	int        =       """x = y"""
   UpperCAmelCase__       :	List[str]        =       {"""y""": 5}
   UpperCAmelCase__       :	Optional[Any]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   # evaluate returns the value of the last assignment.
   assert result == 5
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 5, """y""": 5}     )

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       """y = add_two(x)"""
   UpperCAmelCase__       :	Any        =       {"""x""": 3}
   UpperCAmelCase__       :	str        =       evaluate(_SCREAMING_SNAKE_CASE       ,{"""add_two""": add_two}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 5
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """y""": 5}     )

   # Won't work without the tool
   with CaptureStdout() as out:
    UpperCAmelCase__       :	List[Any]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result is None
   assert "tried to execute add_two" in out.out

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       """x = 3"""
   UpperCAmelCase__       :	Optional[int]        =       {}
   UpperCAmelCase__       :	str        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 3
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3}     )

  def  __lowercase							(	self		:      Dict     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       """test_dict = {\'x\': x, \'y\': add_two(x)}"""
   UpperCAmelCase__       :	Dict        =       {"""x""": 3}
   UpperCAmelCase__       :	Optional[int]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{"""add_two""": add_two}       ,state=_SCREAMING_SNAKE_CASE     )
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """y""": 5}     )
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}}     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       """x = 3\ny = 5"""
   UpperCAmelCase__       :	Union[str, Any]        =       {}
   UpperCAmelCase__       :	List[Any]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   # evaluate returns the value of the last assignment.
   assert result == 5
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """y""": 5}     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       """text = f\'This is x: {x}.\'"""
   UpperCAmelCase__       :	Optional[Any]        =       {"""x""": 3}
   UpperCAmelCase__       :	int        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   # evaluate returns the value of the last assignment.
   assert result == "This is x: 3."
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """text""": """This is x: 3."""}     )

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       """if x <= 3:\n    y = 2\nelse:\n    y = 5"""
   UpperCAmelCase__       :	Optional[Any]        =       {"""x""": 3}
   UpperCAmelCase__       :	Optional[int]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   # evaluate returns the value of the last assignment.
   assert result == 2
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """y""": 2}     )

   UpperCAmelCase__       :	List[Any]        =       {"""x""": 8}
   UpperCAmelCase__       :	Dict        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   # evaluate returns the value of the last assignment.
   assert result == 5
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 8, """y""": 5}     )

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[Any]        =       """test_list = [x, add_two(x)]"""
   UpperCAmelCase__       :	Any        =       {"""x""": 3}
   UpperCAmelCase__       :	Tuple        =       evaluate(_SCREAMING_SNAKE_CASE       ,{"""add_two""": add_two}       ,state=_SCREAMING_SNAKE_CASE     )
   self.assertListEqual(_SCREAMING_SNAKE_CASE       ,[3, 5]     )
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """test_list""": [3, 5]}     )

  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       """y = x"""
   UpperCAmelCase__       :	List[Any]        =       {"""x""": 3}
   UpperCAmelCase__       :	Any        =       evaluate(_SCREAMING_SNAKE_CASE       ,{}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 3
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """y""": 3}     )

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






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       """test_list = [x, add_two(x)]\ntest_list[1]"""
   UpperCAmelCase__       :	str        =       {"""x""": 3}
   UpperCAmelCase__       :	Optional[int]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{"""add_two""": add_two}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 5
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """test_list""": [3, 5]}     )

   UpperCAmelCase__       :	Dict        =       """test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']"""
   UpperCAmelCase__       :	List[Any]        =       {"""x""": 3}
   UpperCAmelCase__       :	List[str]        =       evaluate(_SCREAMING_SNAKE_CASE       ,{"""add_two""": add_two}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 5
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}}     )




  def  __lowercase							(	self		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Any        =       """x = 0\nfor i in range(3):\n    x = i"""
   UpperCAmelCase__       :	List[Any]        =       {}
   UpperCAmelCase__       :	Tuple        =       evaluate(_SCREAMING_SNAKE_CASE       ,{"""range""": range}       ,state=_SCREAMING_SNAKE_CASE     )
   assert result == 2
   self.assertDictEqual(_SCREAMING_SNAKE_CASE       ,{"""x""": 2, """i""": 2}     )













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()



