




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    (		UpperCamelCase_						):






			'''simple docstring'''





			@slow
			@require_torch
			def 							UpperCAmelCase__       ( self			:   Any  )						->   Union[str, Any]:
								__magic_name__							:	List[str]    							=							EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''       ,						'''prajjwal1/bert-tiny'''  )
								__magic_name__							:	Optional[Any]    							=							BertTokenizer.from_pretrained('''bert-base-uncased'''  )

								__magic_name__							:	Any    							=							bertabert.config.encoder.vocab_size
								__magic_name__							:	List[str]    							=							tokenizer.sep_token_id
								__magic_name__							:	Any    							=							tokenizer.cls_token_id
								__magic_name__							:	Tuple    							=							128

								__magic_name__							:	str    							=							datasets.load_dataset('''cnn_dailymail'''       ,						'''3.0.0'''       ,						split='''train[:1%]'''  )
								__magic_name__							:	List[str]    							=							datasets.load_dataset('''cnn_dailymail'''       ,						'''3.0.0'''       ,						split='''validation[:1%]'''  )

								__magic_name__							:	Optional[int]    							=							train_dataset.select(range(32  )  )
								__magic_name__							:	Tuple    							=							val_dataset.select(range(16  )  )

								__magic_name__							:	List[str]    							=							4

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

													__magic_name__							:	Tuple    							=							outputs.input_ids
													__magic_name__							:	Dict    							=							outputs.input_ids.copy()
													__magic_name__							:	Optional[Any]    							=							[
													    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
													]
													__magic_name__							:	List[Any]    							=							outputs.attention_mask

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

													return batch

								def _compute_metrics(lowerCamelCase_			:   Union[str, Any]  ):
													__magic_name__							:	Dict    							=							pred.label_ids
													__magic_name__							:	Any    							=							pred.predictions

													# all unnecessary tokens are removed
													__magic_name__							:	str    							=							tokenizer.batch_decode(__A       ,						skip_special_tokens=__A  )
													__magic_name__							:	Any    							=							tokenizer.batch_decode(__A       ,						skip_special_tokens=__A  )

													__magic_name__							:	str    							=							sum([int(pred_str[i] == label_str[i]  ) for i in range(len(__A  )  )]  ) / len(__A  )

													return {"accuracy": accuracy}

								# map train dataset
								__magic_name__							:	Tuple    							=							train_dataset.map(
								    _map_to_encoder_decoder_inputs       ,						batched=__A       ,						batch_size=__A       ,						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
								__magic_name__							:	int    							=							val_dataset.map(
								    _map_to_encoder_decoder_inputs       ,						batched=__A       ,						batch_size=__A       ,						remove_columns=['''article''', '''highlights''']       ,						)
								val_dataset.set_format(
								    type='''torch'''       ,						columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']       ,						)

								__magic_name__							:	Dict    							=							self.get_auto_remove_tmp_dir()

								__magic_name__							:	List[str]    							=							SeqaSeqTrainingArguments(
								    output_dir=__A       ,						per_device_train_batch_size=__A       ,						per_device_eval_batch_size=__A       ,						predict_with_generate=__A       ,						evaluation_strategy='''steps'''       ,						do_train=__A       ,						do_eval=__A       ,						warmup_steps=0       ,						eval_steps=2       ,						logging_steps=2       ,						)

								# instantiate trainer
								__magic_name__							:	Dict    							=							SeqaSeqTrainer(
								    model=__A       ,						args=__A       ,						compute_metrics=_compute_metrics       ,						train_dataset=__A       ,						eval_dataset=__A       ,						tokenizer=__A       ,						)

								# start training
								trainer.train()









import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
	from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
	from tensorflow.keras.optimizers import Adam


class    lowerCamelCase    (		tf.keras.optimizers.schedules.LearningRateSchedule						):






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   float       ,						lowerCamelCase_			:   Callable       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   float = 1.0       ,						lowerCamelCase_			:   str = None       ,						)						->   Optional[int]:
								super().__init__()
								__magic_name__							:	Union[str, Any]    							=							initial_learning_rate
								__magic_name__							:	Optional[int]    							=							warmup_steps
								__magic_name__							:	Union[str, Any]    							=							power
								__magic_name__							:	List[Any]    							=							decay_schedule_fn
								__magic_name__							:	Dict    							=							name

			def __call__( self			:   Any       ,						lowerCamelCase_			:   Dict  )						->   Optional[Any]:
								with tf.name_scope(self.name or '''WarmUp'''  ) as name:
													# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
													# learning rate will be `global_step/num_warmup_steps * init_lr`.
													__magic_name__							:	Union[str, Any]    							=							tf.cast(lowerCamelCase_       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							tf.cast(self.warmup_steps       ,						tf.floataa  )
													__magic_name__							:	Dict    							=							global_step_float / warmup_steps_float
													__magic_name__							:	List[str]    							=							self.initial_learning_rate * tf.math.pow(lowerCamelCase_       ,						self.power  )
													return tf.cond(
													    global_step_float < warmup_steps_float       ,						lambda: warmup_learning_rate       ,						lambda: self.decay_schedule_fn(step - self.warmup_steps  )       ,						name=lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   str:
								return {
								    "initial_learning_rate": self.initial_learning_rate,
								    "decay_schedule_fn": self.decay_schedule_fn,
								    "warmup_steps": self.warmup_steps,
								    "power": self.power,
								    "name": self.name,
								}







def 		lowercase__   (		__A:	float							,__A:	int							,__A:	int							,__A:	float = 0.0							,__A:	float = 0.9							,__A:	float = 0.999							,__A:	float = 1e-8							,__A:	Optional[float] = None							,__A:	Optional[float] = None							,__A:	float = 0.0							,__A:	float = 1.0							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Any    							=							tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=__A							,decay_steps=num_train_steps - num_warmup_steps							,end_learning_rate=init_lr * min_lr_ratio							,power=__A							,)
					if num_warmup_steps:
										__magic_name__							:	Tuple    							=							WarmUp(
										    initial_learning_rate=__A							,decay_schedule_fn=__A							,warmup_steps=__A							,)
					if weight_decay_rate > 0.0:
										__magic_name__							:	Union[str, Any]    							=							AdamWeightDecay(
										    learning_rate=__A							,weight_decay_rate=__A							,beta_a=__A							,beta_a=__A							,epsilon=__A							,clipnorm=__A							,global_clipnorm=__A							,exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']							,include_in_weight_decay=__A							,)
					else:
										__magic_name__							:	str    							=							tf.keras.optimizers.Adam(
										    learning_rate=__A							,beta_a=__A							,beta_a=__A							,epsilon=__A							,clipnorm=__A							,global_clipnorm=__A							,)
					# We return the optimizer and the LR scheduler in order to better track the
					# evolution of the LR independently of the optimizer.
					return optimizer, lr_schedule


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1       ,						lowerCamelCase_			:   float = 0.9       ,						lowerCamelCase_			:   float = 0.9_9_9       ,						lowerCamelCase_			:   float = 1E-7       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   float = 0.0       ,						lowerCamelCase_			:   Optional[List[str]] = None       ,						lowerCamelCase_			:   Optional[List[str]] = None       ,						lowerCamelCase_			:   str = "AdamWeightDecay"       ,						**lowerCamelCase_			:   int       ,						)						->   List[str]:
								super().__init__(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	int    							=							weight_decay_rate
								__magic_name__							:	Tuple    							=							include_in_weight_decay
								__magic_name__							:	Union[str, Any]    							=							exclude_from_weight_decay

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   int       ,						lowerCamelCase_			:   int  )						->   Optional[Any]:
								__magic_name__							:	Tuple    							=							{'''WarmUp''': WarmUp}
								return super(lowerCamelCase_       ,						cls  ).from_config(lowerCamelCase_       ,						custom_objects=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   int:
								super(lowerCamelCase_       ,						self  )._prepare_local(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								__magic_name__							:	Any    							=							tf.constant(
								    self.weight_decay_rate       ,						name='''adam_weight_decay_rate'''  )

			def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any  )						->   Optional[Any]:
								__magic_name__							:	Optional[int]    							=							self._do_use_weight_decay(var.name  )
								if do_decay:
													return var.assign_sub(
													    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']       ,						use_locking=self._use_locking       ,						)
								return tf.no_op()

			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							,    __magic_name__							:	Dict    							=							list(zip(*lowerCamelCase_  )  )
								return super(lowerCamelCase_       ,						self  ).apply_gradients(zip(lowerCamelCase_       ,						lowerCamelCase_  )       ,						name=lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								if apply_state is None:
													return self._decayed_lr_t[var_dtype], {}

								__magic_name__							:	str    							=							apply_state or {}
								__magic_name__							:	Optional[Any]    							=							apply_state.get((var_device, var_dtype)  )
								if coefficients is None:
													__magic_name__							:	List[str]    							=							self._fallback_apply_state(lowerCamelCase_       ,						lowerCamelCase_  )
													__magic_name__							:	List[Any]    							=							coefficients

								return coefficients["lr_t"], {"apply_state": apply_state}

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Dict=None  )						->   Optional[Any]:
								__magic_name__							,    __magic_name__							:	Union[str, Any]    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								with tf.control_dependencies([decay]  ):
													return super(lowerCamelCase_       ,						self  )._resource_apply_dense(lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Union[str, Any]=None  )						->   Any:
								__magic_name__							,    __magic_name__							:	Tuple    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
								__magic_name__							:	Optional[int]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
								with tf.control_dependencies([decay]  ):
													return super(lowerCamelCase_       ,						self  )._resource_apply_sparse(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
								__magic_name__							:	List[Any]    							=							super().get_config()
								config.update({'''weight_decay_rate''': self.weight_decay_rate}  )
								return config


			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
								if self.weight_decay_rate == 0:
													return False

								if self._include_in_weight_decay:
													for r in self._include_in_weight_decay:
																		if re.search(lowerCamelCase_       ,						lowerCamelCase_  ) is not None:
																							return True

								if self._exclude_from_weight_decay:
													for r in self._exclude_from_weight_decay:
																		if re.search(lowerCamelCase_       ,						lowerCamelCase_  ) is not None:
																							return False
								return True






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   Optional[Any]  )						->   Any:
								__magic_name__							:	Optional[int]    							=							[]
								__magic_name__							:	Dict    							=							None

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								if self._accum_steps is None:
													__magic_name__							:	Optional[Any]    							=							tf.Variable(
													    tf.constant(0       ,						dtype=tf.intaa  )       ,						trainable=lowerCamelCase_       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)

								return self._accum_steps.value()

			@property
			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
								if not self._gradients:
													raise ValueError('''The accumulator should be called first to initialize the gradients'''  )
								return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

			def __call__( self			:   Optional[int]       ,						lowerCamelCase_			:   Any  )						->   List[str]:
								if not self._gradients:
													__magic_name__							:	int    							=							self.step  # Create the step variable.
													self._gradients.extend(
													    [
													        tf.Variable(
													            tf.zeros_like(lowerCamelCase_  )       ,						trainable=lowerCamelCase_       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)
													        if gradient is not None
													        else gradient
													        for gradient in gradients
													    ]  )
								if len(lowerCamelCase_  ) != len(self._gradients  ):
													raise ValueError(F'''Expected {len(self._gradients  )} gradients, but got {len(lowerCamelCase_  )}'''  )

								for accum_gradient, gradient in zip(self._gradients       ,						lowerCamelCase_  ):
													if accum_gradient is not None and gradient is not None:
																		accum_gradient.assign_add(lowerCamelCase_  )

								self._accum_steps.assign_add(1  )


			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[int]:
								if not self._gradients:
													return
								self._accum_steps.assign(0  )
								for gradient in self._gradients:
													if gradient is not None:
																		gradient.assign(tf.zeros_like(lowerCamelCase_  )  )



