



class 		lowercase						:






	"""simple docstring"""


	def __init__(	self	:						Optional[Any]						,  __UpperCAmelCase	:						Optional[int]     )			-> str:
							UpperCAmelCase_=   val
							# Number of nodes in left subtree
							UpperCAmelCase_=   0
							UpperCAmelCase_=   None
							UpperCAmelCase_=   None
							UpperCAmelCase_=   None




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]						,  __UpperCAmelCase	:						Union[str, Any]     )			-> Optional[int]:
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
													UpperCAmelCase_=   self.right
													UpperCAmelCase_=   None
													if self.right:
																			UpperCAmelCase_=   other
													UpperCAmelCase_=   other
													UpperCAmelCase_=   self.left_tree_size * 2 + 1
													return self
							else:
													UpperCAmelCase_=   other.right
													UpperCAmelCase_=   None
													if other.right:
																			UpperCAmelCase_=   self
													UpperCAmelCase_=   self
													UpperCAmelCase_=   other.left_tree_size * 2 + 1
													return other






class 		lowercase						:






	"""simple docstring"""


	def __init__(	self	:						Tuple						,  __UpperCAmelCase	:						int=None						,  __UpperCAmelCase	:						Optional[int]=None						,  __UpperCAmelCase	:						Tuple=0     )			-> int:
							UpperCAmelCase_=   heap_size
							UpperCAmelCase_=   bottom_root
							UpperCAmelCase_=   min_node




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						Optional[int]     )			-> Optional[int]:

							# Empty heaps corner cases
							if other.size == 0:
													return None
							if self.size == 0:
													UpperCAmelCase_=   other.size
													UpperCAmelCase_=   other.bottom_root
													UpperCAmelCase_=   other.min_node
													return None
							# Update size
							UpperCAmelCase_=   self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
													UpperCAmelCase_=   other.min_node
							# Merge

							# Order roots by left_subtree_size
							UpperCAmelCase_=   []
							UpperCAmelCase_,   UpperCAmelCase_=   self.bottom_root, other.bottom_root
							while i or j:
													if i and ((not j) or i.left_tree_size < j.left_tree_size):
																			combined_roots_list.append((i, True)     )
																			UpperCAmelCase_=   i.parent
													else:
																			combined_roots_list.append((j, False)     )
																			UpperCAmelCase_=   j.parent
        # Insert links between them
							for i in range(len(__UpperCAmelCase     ) - 1     ):
													if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																			UpperCAmelCase_=   combined_roots_list[i + 1][0]
																			UpperCAmelCase_=   combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							UpperCAmelCase_=   combined_roots_list[0][0]
							while i.parent:
													if (
													    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
													) or (
													    i.left_tree_size == i.parent.left_tree_size
													    and i.left_tree_size != i.parent.parent.left_tree_size
													):
																			# Neighbouring Nodes
																			UpperCAmelCase_=   i.left
																			UpperCAmelCase_=   i.parent.parent

																			# Merging trees
																			UpperCAmelCase_=   i.merge_trees(i.parent     )

																			# Updating links
																			UpperCAmelCase_=   previous_node
																			UpperCAmelCase_=   next_node
																			if previous_node:
																									UpperCAmelCase_=   i
																			if next_node:
																									UpperCAmelCase_=   i
													else:
																			UpperCAmelCase_=   i.parent
        # Updating self.bottom_root
							while i.left:
													UpperCAmelCase_=   i.left
							UpperCAmelCase_=   i

							# Update other
							UpperCAmelCase_=   self.size
							UpperCAmelCase_=   self.bottom_root
							UpperCAmelCase_=   self.min_node

							# Return the merged heap
							return self




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						int     )			-> Optional[Any]:
							if self.size == 0:
													UpperCAmelCase_=   Node(__UpperCAmelCase     )
													UpperCAmelCase_=   1
													UpperCAmelCase_=   self.bottom_root
							else:
													# Create new node
													UpperCAmelCase_=   Node(__UpperCAmelCase     )

													# Update size
													self.size += 1

													# update min_node
													if val < self.min_node.val:
																			UpperCAmelCase_=   new_node
													# Put new_node as a bottom_root in heap
													UpperCAmelCase_=   new_node
													UpperCAmelCase_=   self.bottom_root
													UpperCAmelCase_=   new_node

													# Consecutively merge roots with same left_tree_size
													while (
													    self.bottom_root.parent
													    and self.bottom_root.left_tree_size
													    == self.bottom_root.parent.left_tree_size
													):
																			# Next node
																			UpperCAmelCase_=   self.bottom_root.parent.parent

																			# Merge
																			UpperCAmelCase_=   self.bottom_root.merge_trees(self.bottom_root.parent     )

																			# Update Links
																			UpperCAmelCase_=   next_node
																			UpperCAmelCase_=   None
																			if next_node:
																									UpperCAmelCase_=   self.bottom_root




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Optional[Any]:
							return self.min_node.val




	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> List[str]:
							return self.size == 0




	def   _SCREAMING_SNAKE_CASE		(	self	:						int     )			-> Union[str, Any]:
							UpperCAmelCase_=   self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
													# Update size
													UpperCAmelCase_=   0

													# Update bottom root
													UpperCAmelCase_=   None

													# Update min_node
													UpperCAmelCase_=   None

													return min_value
							# No right subtree corner case
							# The structure of the tree implies that this should be the bottom root
							# and there is at least one other root
							if self.min_node.right is None:
													# Update size
													self.size -= 1

													# Update bottom root
													UpperCAmelCase_=   self.bottom_root.parent
													UpperCAmelCase_=   None

													# Update min_node
													UpperCAmelCase_=   self.bottom_root
													UpperCAmelCase_=   self.bottom_root.parent
													while i:
																			if i.val < self.min_node.val:
																									UpperCAmelCase_=   i
																			UpperCAmelCase_=   i.parent
													return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							UpperCAmelCase_=   self.min_node.right
							UpperCAmelCase_=   None
							UpperCAmelCase_=   bottom_of_new
							UpperCAmelCase_=   1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
													UpperCAmelCase_=   size_of_new * 2 + 1
													UpperCAmelCase_=   bottom_of_new.left
													if bottom_of_new.val < min_of_new.val:
																			UpperCAmelCase_=   bottom_of_new
        # Corner case of single root on top left path
							if (not self.min_node.left) and (not self.min_node.parent):
													UpperCAmelCase_=   size_of_new
													UpperCAmelCase_=   bottom_of_new
													UpperCAmelCase_=   min_of_new
													# print("Single root, multiple nodes case")
													return min_value
							# Remaining cases
							# Construct heap of right subtree
							UpperCAmelCase_=   BinomialHeap(
							    bottom_root=__UpperCAmelCase						,  min_node=__UpperCAmelCase						,  heap_size=__UpperCAmelCase     )

							# Update size
							UpperCAmelCase_=   self.size - 1 - size_of_new

							# Neighbour nodes
							UpperCAmelCase_=   self.min_node.left
							UpperCAmelCase_=   self.min_node.parent

							# Initialize new bottom_root and min_node
							UpperCAmelCase_=   previous_node or next_node
							UpperCAmelCase_=   next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
													UpperCAmelCase_=   next_node

													# Update bottom_root and search for min_node below
													UpperCAmelCase_=   previous_node
													UpperCAmelCase_=   previous_node
													while self.bottom_root.left:
																			UpperCAmelCase_=   self.bottom_root.left
																			if self.bottom_root.val < self.min_node.val:
																									UpperCAmelCase_=   self.bottom_root
							if next_node:
													UpperCAmelCase_=   previous_node

													# Search for new min_node above min_node
													UpperCAmelCase_=   next_node
													while i:
																			if i.val < self.min_node.val:
																									UpperCAmelCase_=   i
																			UpperCAmelCase_=   i.parent
        # Merge heaps
							self.merge_heaps(__UpperCAmelCase     )

							return min_value




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> int:
							UpperCAmelCase_=   self.bottom_root
							while top_root.parent:
													UpperCAmelCase_=   top_root.parent
							# preorder
							UpperCAmelCase_=   []
							self.__traversal(__UpperCAmelCase						,  __UpperCAmelCase     )
							return heap_pre_order




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]						,  __UpperCAmelCase	:						Union[str, Any]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Optional[Any]=0     )			-> Union[str, Any]:
							if curr_node:
													preorder.append((curr_node.val, level)     )
													self.__traversal(curr_node.left						,  __UpperCAmelCase						,  level + 1     )
													self.__traversal(curr_node.right						,  __UpperCAmelCase						,  level + 1     )
							else:
													preorder.append(("""#""", level)     )




	def __str__(	self	:						Any     )			-> Optional[int]:
							if self.is_empty():
													return ""
							UpperCAmelCase_=   self.pre_order()

							return "\n".join(("""-""" * level + str(__UpperCAmelCase     )) for value, level in preorder_heap     )


# Unit Tests
if __name__ == "__main__":
	import doctest

	doctest.testmod()









import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
	__A				    =						True
	__A				    =						'''p3dn.24xlarge'''
	import smdistributed.dataparallel.tensorflow as sdp
else:
	__A				    =						False



def        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					int=None				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=   tqdm(lowerCAmelCase_				)
						for i, batch in enumerate(lowerCAmelCase_				):
												with tf.GradientTape() as tape:
																		UpperCAmelCase_,   UpperCAmelCase_=   batch
																		UpperCAmelCase_=   model(lowerCAmelCase_				)
																		UpperCAmelCase_=   loss(lowerCAmelCase_			,outputs.logits				)

												if SDP_ENABLED:
																		UpperCAmelCase_=   sdp.DistributedGradientTape(lowerCAmelCase_			,sparse_as_dense=lowerCAmelCase_				)

												UpperCAmelCase_=   tape.gradient(lowerCAmelCase_			,model.trainable_variables				)
												opt.apply_gradients(zip(lowerCAmelCase_			,model.trainable_variables				)				)

												pbar.set_description(F"""Loss: {loss_value:.4f}"""				)

												if SDP_ENABLED and i == 0:
																		sdp.broadcast_variables(model.variables			,root_rank=0				)
																		sdp.broadcast_variables(opt.variables()			,root_rank=0				)

												if max_steps and i >= max_steps:
																		break

						UpperCAmelCase_=   {"""loss""": loss_value.numpy()}
						return train_results



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					str				)      -> str:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   load_dataset("""imdb"""			,split=["""train""", """test"""]				)

						# Preprocess train dataset
						UpperCAmelCase_=   train_dataset.map(
						    lambda lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						train_dataset.set_format(type="""tensorflow"""			,columns=["""input_ids""", """attention_mask""", """label"""]				)

						UpperCAmelCase_=   {
						    x: train_dataset[x].to_tensor(default_value=0			,shape=[None, tokenizer.model_max_length]				)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						UpperCAmelCase_=   tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])				)

						# Preprocess test dataset
						UpperCAmelCase_=   test_dataset.map(
						    lambda lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						test_dataset.set_format(type="""tensorflow"""			,columns=["""input_ids""", """attention_mask""", """label"""]				)

						UpperCAmelCase_=   {
						    x: test_dataset[x].to_tensor(default_value=0			,shape=[None, tokenizer.model_max_length]				)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						UpperCAmelCase_=   tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])				)

						if SDP_ENABLED:
												UpperCAmelCase_=   tf_train_dataset.shard(sdp.size()			,sdp.rank()				)
												UpperCAmelCase_=   tf_test_dataset.shard(sdp.size()			,sdp.rank()				)
						UpperCAmelCase_=   tf_train_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)
						UpperCAmelCase_=   tf_test_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	__A				    =						argparse.ArgumentParser()

	# Hyperparameters sent by the client are passed as command-line arguments to the script.
	parser.add_argument('''--epochs''', type=int, default=3)
	parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
	parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
	parser.add_argument('''--model_name_or_path''', type=str)
	parser.add_argument('''--learning_rate''', type=str, default=5E-5)
	parser.add_argument('''--do_train''', type=bool, default=True)
	parser.add_argument('''--do_eval''', type=bool, default=True)
	parser.add_argument('''--output_dir''', type=str)
	parser.add_argument('''--max_steps''', type=int, default=None)

	# Data, model, and output directories
	parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
	parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
	parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

	__A    ,			__A				    =						parser.parse_known_args()

	# Set up logging
	__A				    =						logging.getLogger(__name__)

	logging.basicConfig(
	    level=logging.getLevelName('''INFO'''),
	    handlers=[logging.StreamHandler(sys.stdout)],
	    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
	)

	if SDP_ENABLED:
		sdp.init()

		__A				    =						tf.config.experimental.list_physical_devices('''GPU''')
		for gpu in gpus:
			tf.config.experimental.set_memory_growth(gpu, True)
		if gpus:
			tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
	__A				    =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	__A				    =						AutoTokenizer.from_pretrained(args.model_name_or_path)

	# get datasets
	__A    ,			__A				    =						get_datasets(
	    tokenizer=tokenizer,
	    train_batch_size=args.per_device_train_batch_size,
	    eval_batch_size=args.per_device_eval_batch_size,
	)

	# fine optimizer and loss
	__A				    =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
	__A				    =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
	__A				    =						[tf.keras.metrics.SparseCategoricalAccuracy()]
	model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

	# Training
	if args.do_train:
		# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
		__A				    =						time.time()
		__A				    =						fit(
		    model,
		    loss,
		    optimizer,
		    tf_train_dataset,
		    args.epochs,
		    args.per_device_train_batch_size,
		    max_steps=args.max_steps,
		)
		__A				    =						time.time() - start_train_time
		logger.info('''*** Train ***''')
		logger.info(f'train_runtime = {end_train_time}')

		__A				    =						os.path.join(args.output_dir, '''train_results.txt''')

		if not SDP_ENABLED or sdp.rank() == 0:
			with open(output_eval_file, '''w''') as writer:
				logger.info('''***** Train results *****''')
				logger.info(train_results)
				for key, value in train_results.items():
					logger.info(f'  {key} = {value}')
					writer.write(f'{key} = {value}\n')

    # Evaluation
	if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
		__A				    =						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
		logger.info('''*** Evaluate ***''')

		__A				    =						os.path.join(args.output_dir, '''eval_results.txt''')

		with open(output_eval_file, '''w''') as writer:
			logger.info('''***** Eval results *****''')
			logger.info(result)
			for key, value in result.items():
				logger.info(f'  {key} = {value}')
				writer.write(f'{key} = {value}\n')

    # Save result
	if SDP_ENABLED:
		if sdp.rank() == 0:
			model.save_pretrained(args.output_dir)
			tokenizer.save_pretrained(args.output_dir)
	else:
		model.save_pretrained(args.output_dir)
		tokenizer.save_pretrained(args.output_dir)




