





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 SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@slow
						@require_torch
						def UpperCAmelCase_				(      self      :      str	)							->    str:







							"""simple docstring"""


							snake_case_		:			str											=	EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'		,   'prajjwal1/bert-tiny'	)
							snake_case_		:			Any											=	BertTokenizer.from_pretrained('bert-base-uncased'	)

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

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

							snake_case_		:			Optional[int]											=	train_dataset.select(range(32	)	)
							snake_case_		:			Union[str, Any]											=	val_dataset.select(range(16	)	)

							snake_case_		:			List[Any]											=	4

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

								snake_case_		:			Tuple											=	outputs.input_ids
								snake_case_		:			Optional[int]											=	outputs.input_ids.copy()
								snake_case_		:			Optional[Any]											=	[
								    [-100 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(_A	) == 512 for x in inputs.input_ids	)
								assert all(len(_A	) == 128 for x in outputs.input_ids	)

								return batch

							def _compute_metrics(_A      :      Optional[Any]	):
								snake_case_		:			Any											=	pred.label_ids
								snake_case_		:			List[Any]											=	pred.predictions

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

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

								return {"accuracy": accuracy}

							# map train dataset
							snake_case_		:			str											=	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
							snake_case_		:			Any											=	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']		,   )

							snake_case_		:			str											=	self.get_auto_remove_tmp_dir()

							snake_case_		:			Optional[Any]											=	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
							snake_case_		:			Tuple											=	SeqaSeqTrainer(
							    model=_A		,   args=_A		,   compute_metrics=_compute_metrics		,   train_dataset=_A		,   eval_dataset=_A		,   tokenizer=_A		,   )

							# start training
							trainer.train()








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

