
'''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 	A							(				SCREAMING_SNAKE_CASE__ ):



			@slow
			@require_torch
			def A__       (    self				)	->						List[Any]:




									'''simple docstring'''



									lowercase__							       =				EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				,   """prajjwal1/bert-tiny"""				)
									lowercase__							       =				BertTokenizer.from_pretrained("""bert-base-uncased"""				)

									lowercase__							       =				bertabert.config.encoder.vocab_size
									lowercase__							       =				tokenizer.sep_token_id
									lowercase__							       =				tokenizer.cls_token_id
									lowercase__							       =				128

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

									lowercase__							       =				train_dataset.select(range(32				)				)
									lowercase__							       =				val_dataset.select(range(16				)				)

									lowercase__							       =				4

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

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

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

															return batch

									def _compute_metrics(lowerCamelCase__				):
															lowercase__							       =				pred.label_ids
															lowercase__							       =				pred.predictions

															# all unnecessary tokens are removed
															lowercase__							       =				tokenizer.batch_decode(__lowerCamelCase				,   skip_special_tokens=__lowerCamelCase				)
															lowercase__							       =				tokenizer.batch_decode(__lowerCamelCase				,   skip_special_tokens=__lowerCamelCase				)

															lowercase__							       =				sum([int(pred_str[i] == label_str[i]				) for i in range(len(__lowerCamelCase				)				)]				) / len(__lowerCamelCase				)

															return {"accuracy": accuracy}

									# map train dataset
									lowercase__							       =				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
									lowercase__							       =				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"""]				,   )

									lowercase__							       =				self.get_auto_remove_tmp_dir()

									lowercase__							       =				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
									lowercase__							       =				SeqaSeqTrainer(
									    model=__lowerCamelCase				,   args=__lowerCamelCase				,   compute_metrics=_compute_metrics				,   train_dataset=__lowerCamelCase				,   eval_dataset=__lowerCamelCase				,   tokenizer=__lowerCamelCase				,   )

									# start training
									trainer.train()













"""simple docstring"""



def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    if n == 0:
        return 0
    __A    :						int						 =     float('''-inf'''						)
    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max(
            snake_case_				,prices[i - 1] + naive_cut_rod_recursive(n - i				,snake_case_						)						)

    return max_revue







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->int:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    __A    :						Dict						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    return _top_down_cut_rod_recursive(snake_case_				,snake_case_				,snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        __A    :						Any						 =     float('''-inf'''						)
        for i in range(1				,n + 1						):
            __A    :						Union[str, Any]						 =     max(
                snake_case_				,prices[i - 1] + _top_down_cut_rod_recursive(n - i				,snake_case_				,snake_case_						)				,)

        __A    :						Any						 =     max_revenue

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    __A    :						Union[str, Any]						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    __A    :						List[Any]						 =     0

    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max_rev[i]
        for j in range(1				,i + 1						):
            __A    :						str						 =     max(snake_case_				,prices[j - 1] + max_rev[i - j]						)

        __A    :						List[str]						 =     max_revenue_i

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Union[str, Any]:



    '''simple docstring'''







    if n < 0:
        __A    :						Union[str, Any]						 =     F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(snake_case_						)

    if n > len(snake_case_						):
        __A    :						List[Any]						 =     (
            '''Each integral piece of rod must have a corresponding price. '''
            F"""Got n = {n} but length of prices = {len(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)







def 		__lowercase     (   )   ->str:



    '''simple docstring'''







    __A    :						Any						 =     [6, 10, 12, 15, 20, 23]
    __A    :						Union[str, Any]						 =     len(snake_case_						)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    __A    :						str						 =     36

    __A    :						Any						 =     top_down_cut_rod(snake_case_				,snake_case_						)
    __A    :						Any						 =     bottom_up_cut_rod(snake_case_				,snake_case_						)
    __A    :						Optional[Any]						 =     naive_cut_rod_recursive(snake_case_				,snake_case_						)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()




