



"""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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@slow
		@require_torch
		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""							,    """prajjwal1/bert-tiny"""   )
								snake_case_   : List[str]						=			BertTokenizer.from_pretrained("""bert-base-uncased"""   )

								snake_case_   : int						=			bertabert.config.encoder.vocab_size
								snake_case_   : Optional[Any]						=			tokenizer.sep_token_id
								snake_case_   : List[str]						=			tokenizer.cls_token_id
								snake_case_   : Union[str, Any]						=			1_2_8

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

								snake_case_   : Optional[int]						=			train_dataset.select(range(3_2   )   )
								snake_case_   : List[Any]						=			val_dataset.select(range(1_6   )   )

								snake_case_   : Any						=			4

								def _map_to_encoder_decoder_inputs(_lowercase   ):
														# Tokenizer will automatically set [BOS] <text> [EOS]
														snake_case_   : List[str]						=			tokenizer(batch["""article"""]							,    padding="""max_length"""							,    truncation=_lowercase							,    max_length=5_1_2   )
														snake_case_   : Any						=			tokenizer(batch["""highlights"""]							,    padding="""max_length"""							,    truncation=_lowercase							,    max_length=1_2_8   )
														snake_case_   : Optional[Any]						=			inputs.input_ids
														snake_case_   : Any						=			inputs.attention_mask

														snake_case_   : Dict						=			outputs.input_ids
														snake_case_   : Tuple						=			outputs.input_ids.copy()
														snake_case_   : Dict						=			[
														    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
														]
														snake_case_   : List[str]						=			outputs.attention_mask

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

														return batch

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

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

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

														return {"accuracy": accuracy}

								# map train dataset
								snake_case_   : Union[str, Any]						=			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_   : int						=			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_   : str						=			self.get_auto_remove_tmp_dir()

								snake_case_   : 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_   : Dict						=			SeqaSeqTrainer(
								    model=_lowercase							,    args=_lowercase							,    compute_metrics=_compute_metrics							,    train_dataset=_lowercase							,    eval_dataset=_lowercase							,    tokenizer=_lowercase							,    )

								# start training
								trainer.train()






"""simple docstring"""





import math

import tensorflow as tf
from packaging import version
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0    )					, x.dtype    )    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : int						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : List[Any]						=			tf.cast(math.pi					, x.dtype    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi    ) * (x + coeff * tf.pow(__UpperCamelCase					, 3    ))    ))

						return x * cdf
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			tf.convert_to_tensor(__UpperCamelCase    )

						return x * tf.tanh(tf.math.softplus(__UpperCamelCase    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						snake_case_   : str						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : int						=			tf.cast(0.044_715					, x.dtype    )
						snake_case_   : Optional[int]						=			tf.cast(0.7_978_845_608					, x.dtype    )

						return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)    ))
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : Tuple						=			tf.convert_to_tensor(__UpperCamelCase    )
						snake_case_   : str						=			tf.cast(1.702					, x.dtype    )
						return x * tf.math.sigmoid(coeff * x    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return tf.clip_by_value(_gelu(__UpperCamelCase    )					, -1_0					, 1_0    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[str]=-1    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : List[Any]						=			tf.split(__UpperCamelCase					, 2					, axis=__UpperCamelCase    )
						return a * tf.math.sigmoid(__UpperCamelCase    )


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
					def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


											'''simple docstring'''






											return tf.keras.activations.gelu(__UpperCamelCase					, approximate=__UpperCamelCase    )

					__lowerCAmelCase       :					int      					=					tf.keras.activations.gelu






					__lowerCAmelCase       :					Optional[Any]      					=					approximate_gelu_wrap
else:
					__lowerCAmelCase       :					List[Any]      					=					_gelu
					__lowerCAmelCase       :					Any      					=					_gelu_new


__lowerCAmelCase       :					Dict      					=					{
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}






def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any    ):


						'''simple docstring'''






						if activation_string in ACTaFN:
												return ACTaFN[activation_string]
						else:
												raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()    )}'    )

