




"""simple docstring"""







from __future__ import annotations

from random import random
from typing import Generic, TypeVar

a      =							TypeVar('KT')
a      =							TypeVar('VT')






class       SCREAMING_SNAKE_CASE__					(					Generic[KT, VT]					):


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			KT | str = "root"						,						lowerCAmelCase				:			VT | None = None					):
										lowerCAmelCase  =				key
										lowerCAmelCase  =				value
										lowerCAmelCase  =				[]


						def __repr__(							self				:			List[Any]					):
										return f'''Node({self.key}: {self.value})'''




						@property
						def   __lowercase (							self				:			Tuple					):
										return len(self.forward					)






class       SCREAMING_SNAKE_CASE__					(					Generic[KT, VT]					):


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			float = 0.5						,						lowerCAmelCase				:			int = 16					):
										lowerCAmelCase  =				Node[KT, VT]()
										lowerCAmelCase  =				0
										lowerCAmelCase  =				p
										lowerCAmelCase  =				max_level


						def __str__(							self				:			List[str]					):
										lowerCAmelCase  =				list(self					)

										if len(lowerCAmelCase					) == 0:
														return f'''SkipList(level={self.level})'''

										lowerCAmelCase  =				max((len(str(lowerCAmelCase					)					) for item in items)						,						default=4					)
										lowerCAmelCase  =				max(lowerCAmelCase						,						4					) + 4

										lowerCAmelCase  =				self.head
										lowerCAmelCase  =				[]

										lowerCAmelCase  =				node.forward.copy()
										lines.append(f'''[{node.key}]'''.ljust(lowerCAmelCase						,						"""-"""					) + """* """ * len(lowerCAmelCase					)					)
										lines.append(""" """ * label_size + """| """ * len(lowerCAmelCase					)					)

										while len(node.forward					) != 0:
														lowerCAmelCase  =				node.forward[0]

														lines.append(
														    f'''[{node.key}]'''.ljust(lowerCAmelCase						,						"""-"""					)
														    + """ """.join(str(n.key					) if n.key == node.key else """|""" for n in forwards					)					)
														lines.append(""" """ * label_size + """| """ * len(lowerCAmelCase					)					)
														lowerCAmelCase  =				node.forward

										lines.append("""None""".ljust(lowerCAmelCase					) + """* """ * len(lowerCAmelCase					)					)
										return f'''SkipList(level={self.level})\n''' + "\n".join(lowerCAmelCase					)


						def __iter__(							self				:			List[str]					):
										lowerCAmelCase  =				self.head

										while len(node.forward					) != 0:
														yield node.forward[0].key
														lowerCAmelCase  =				node.forward[0]


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				1
										while random() < self.p and level < self.max_level:
														level += 1

										return level


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Any					):
										lowerCAmelCase  =				[]

										lowerCAmelCase  =				self.head

										for i in reversed(range(self.level					)					):
														# i < node.level - When node level is lesser than `i` decrement `i`.
														# node.forward[i].key < key - Jumping to node with key value higher
														#                             or equal to searched key would result
														#                             in skipping searched key.
														while i < node.level and node.forward[i].key < key:
																		lowerCAmelCase  =				node.forward[i]
														# Each leftmost node (relative to searched node) will potentially have to
														# be updated.
														update_vector.append(lowerCAmelCase					)

										update_vector.reverse()  # Note that we were inserting values in reverse order.

										# len(node.forward) != 0 - If current node doesn't contain any further
										#                          references then searched key is not present.
										# node.forward[0].key == key - Next node key should be equal to search key
										#                              if key is present.
										if len(node.forward					) != 0 and node.forward[0].key == key:
														return node.forward[0], update_vector
										else:
														return None, update_vector


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			KT					):
										lowerCAmelCase ,						lowerCAmelCase  =				self._locate_node(lowerCAmelCase					)

										if node is not None:
														for i, update_node in enumerate(lowerCAmelCase					):
																		# Remove or replace all references to removed node.
																		if update_node.level > i and update_node.forward[i].key == key:
																						if node.level > i:
																										lowerCAmelCase  =				node.forward[i]
																						else:
																										lowerCAmelCase  =				update_node.forward[:i]


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			KT						,						lowerCAmelCase				:			VT					):
										lowerCAmelCase ,						lowerCAmelCase  =				self._locate_node(lowerCAmelCase					)
										if node is not None:
														lowerCAmelCase  =				value
										else:
														lowerCAmelCase  =				self.random_level()

														if level > self.level:
																		# After level increase we have to add additional nodes to head.
																		for _ in range(self.level - 1						,						lowerCAmelCase					):
																						update_vector.append(self.head					)
																		lowerCAmelCase  =				level

														lowerCAmelCase  =				Node(lowerCAmelCase						,						lowerCAmelCase					)

														for i, update_node in enumerate(update_vector[:level]					):
																		# Change references to pass through new node.
																		if update_node.level > i:
																						new_node.forward.append(update_node.forward[i]					)

																		if update_node.level < i + 1:
																						update_node.forward.append(lowerCAmelCase					)
																		else:
																						lowerCAmelCase  =				new_node




						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			VT					):
										lowerCAmelCase ,						lowerCAmelCase  =				self._locate_node(lowerCAmelCase					)

										if node is not None:
														return node.value

										return None





def 							lowercase					()     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()
				skip_list.insert("""Key1"""      ,       3 )
				skip_list.insert("""Key2"""      ,       12 )
				skip_list.insert("""Key3"""      ,       41 )
				skip_list.insert("""Key4"""      ,       -19 )

				lowerCAmelCase  =				skip_list.head
				lowerCAmelCase  =				{}
				while node.level != 0:
								lowerCAmelCase  =				node.forward[0]
								lowerCAmelCase  =				node.value

				assert len(snake_case__ ) == 4
				assert all_values["Key1"] == 3
				assert all_values["Key2"] == 12
				assert all_values["Key3"] == 41
				assert all_values["Key4"] == -19





def 							lowercase					()     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()
				skip_list.insert("""Key1"""      ,       10 )
				skip_list.insert("""Key1"""      ,       12 )

				skip_list.insert("""Key5"""      ,       7 )
				skip_list.insert("""Key7"""      ,       10 )
				skip_list.insert("""Key10"""      ,       5 )

				skip_list.insert("""Key7"""      ,       7 )
				skip_list.insert("""Key5"""      ,       5 )
				skip_list.insert("""Key10"""      ,       10 )

				lowerCAmelCase  =				skip_list.head
				lowerCAmelCase  =				{}
				while node.level != 0:
								lowerCAmelCase  =				node.forward[0]
								lowerCAmelCase  =				node.value

				if len(snake_case__ ) != 4:
								print()
				assert len(snake_case__ ) == 4
				assert all_values["Key1"] == 12
				assert all_values["Key7"] == 7
				assert all_values["Key5"] == 5
				assert all_values["Key10"] == 10





def 							lowercase					()     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()
				assert skip_list.find("""Some key""" ) is None





def 							lowercase					()     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()

				skip_list.insert("""Key2"""      ,       20 )
				assert skip_list.find("""Key2""" ) == 20

				skip_list.insert("""Some Key"""      ,       10 )
				skip_list.insert("""Key2"""      ,       8 )
				skip_list.insert("""V"""      ,       13 )

				assert skip_list.find("""Y""" ) is None
				assert skip_list.find("""Key2""" ) == 8
				assert skip_list.find("""Some Key""" ) == 10
				assert skip_list.find("""V""" ) == 13





def 							lowercase					()     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()
				skip_list.delete("""Some key""" )

				assert len(skip_list.head.forward ) == 0





def 							lowercase					()     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()

				skip_list.insert("""Key1"""      ,       12 )
				skip_list.insert("""V"""      ,       13 )
				skip_list.insert("""X"""      ,       14 )
				skip_list.insert("""Key2"""      ,       15 )

				skip_list.delete("""V""" )
				skip_list.delete("""Key2""" )

				assert skip_list.find("""V""" ) is None
				assert skip_list.find("""Key2""" ) is None





def 							lowercase					()     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()

				skip_list.insert("""Key1"""      ,       12 )
				skip_list.insert("""V"""      ,       13 )
				skip_list.insert("""X"""      ,       14 )
				skip_list.insert("""Key2"""      ,       15 )

				skip_list.delete("""V""" )
				assert skip_list.find("""V""" ) is None
				assert skip_list.find("""X""" ) == 14
				assert skip_list.find("""Key1""" ) == 12
				assert skip_list.find("""Key2""" ) == 15

				skip_list.delete("""X""" )
				assert skip_list.find("""V""" ) is None
				assert skip_list.find("""X""" ) is None
				assert skip_list.find("""Key1""" ) == 12
				assert skip_list.find("""Key2""" ) == 15

				skip_list.delete("""Key1""" )
				assert skip_list.find("""V""" ) is None
				assert skip_list.find("""X""" ) is None
				assert skip_list.find("""Key1""" ) is None
				assert skip_list.find("""Key2""" ) == 15

				skip_list.delete("""Key2""" )
				assert skip_list.find("""V""" ) is None
				assert skip_list.find("""X""" ) is None
				assert skip_list.find("""Key1""" ) is None
				assert skip_list.find("""Key2""" ) is None





def 							lowercase					()     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()

				skip_list.insert("""Key1"""      ,       12 )
				skip_list.insert("""V"""      ,       13 )
				skip_list.insert("""X"""      ,       142 )
				skip_list.insert("""Key2"""      ,       15 )

				skip_list.delete("""X""" )

				def traverse_keys(snake_case__   : Tuple ):
								yield node.key
								for forward_node in node.forward:
												yield from traverse_keys(snake_case__ )

				assert len(set(traverse_keys(skip_list.head ) ) ) == 4





def 							lowercase					()     ->  str:


				'''simple docstring'''






				def is_sorted(snake_case__   : Union[str, Any] ):
								return all(next_item >= item for item, next_item in zip(snake_case__      ,       lst[1:] ) )

				lowerCAmelCase  =				SkipList()
				for i in range(10 ):
								skip_list.insert(snake_case__      ,       snake_case__ )
				assert is_sorted(list(snake_case__ ) )
				skip_list.delete(5 )
				skip_list.delete(8 )
				skip_list.delete(2 )
				assert is_sorted(list(snake_case__ ) )
				skip_list.insert(-12      ,       -12 )
				skip_list.insert(77      ,       77 )
				assert is_sorted(list(snake_case__ ) )





def 							lowercase					()     ->  Union[str, Any]:


				'''simple docstring'''






				for _ in range(100 ):
								# Repeat test 100 times due to the probabilistic nature of skip list
								# random values == random bugs
								test_insert()
								test_insert_overrides_existing_value()

								test_searching_empty_list_returns_none()
								test_search()

								test_deleting_item_from_empty_list_do_nothing()
								test_deleted_items_are_not_founded_by_find_method()
								test_delete_removes_only_given_key()
								test_delete_doesnt_leave_dead_nodes()

								test_iter_always_yields_sorted_values()





def 							lowercase					()     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				SkipList()
				skip_list.insert(2      ,       """2""" )
				skip_list.insert(4      ,       """4""" )
				skip_list.insert(6      ,       """4""" )
				skip_list.insert(4      ,       """5""" )
				skip_list.insert(8      ,       """4""" )
				skip_list.insert(9      ,       """4""" )

				skip_list.delete(4 )

				print(snake_case__ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()








"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a      =							1_6
a      =							3_2





def 							lowercase					(snake_case__   : Accelerator      ,       snake_case__   : int = 16 )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCAmelCase  =				load_dataset("""glue"""      ,       """mrpc""" )

				def tokenize_function(snake_case__   : List[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase  =				tokenizer(examples["""sentence1"""]      ,       examples["""sentence2"""]      ,       truncation=snake_case__      ,       max_length=snake_case__ )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCAmelCase  =				datasets.map(
								    snake_case__      ,       batched=snake_case__      ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase  =				tokenized_datasets.rename_column("""label"""      ,       """labels""" )

				def collate_fn(snake_case__   : Union[str, Any] ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase  =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase  =				16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase  =				8
								else:
												lowerCAmelCase  =				None

								return tokenizer.pad(
								    snake_case__      ,       padding="""longest"""      ,       max_length=snake_case__      ,       pad_to_multiple_of=snake_case__      ,       return_tensors="""pt"""      ,       )

				# Instantiate dataloaders.
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""train"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""validation"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		a      =							mocked_dataloaders  # noqa: F811





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       snake_case__ ) == "1":
								lowerCAmelCase  =				2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCAmelCase  =				Accelerator(
								    cpu=args.cpu      ,       mixed_precision=args.mixed_precision      ,       log_with="""all"""      ,       project_dir=args.project_dir )
				else:
								lowerCAmelCase  =				Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase  =				config["""lr"""]
				lowerCAmelCase  =				int(config["""num_epochs"""] )
				lowerCAmelCase  =				int(config["""seed"""] )
				lowerCAmelCase  =				int(config["""batch_size"""] )
				set_seed(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				get_dataloaders(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				evaluate.load("""glue"""      ,       """mrpc""" )

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase  =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase  =				batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase  =				MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase  =				AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,       return_dict=snake_case__ )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowerCAmelCase  =				model.to(accelerator.device )

				# Instantiate optimizer
				lowerCAmelCase  =				AdamW(params=model.parameters()      ,       lr=snake_case__ )

				# Instantiate scheduler
				lowerCAmelCase  =				get_linear_schedule_with_warmup(
				    optimizer=snake_case__      ,       num_warmup_steps=100      ,       num_training_steps=(len(snake_case__ ) * num_epochs) // gradient_accumulation_steps      ,       )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				accelerator.prepare(
				    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCAmelCase  =				os.path.split(snake_case__ )[-1].split(""".""" )[0]
								accelerator.init_trackers(snake_case__      ,       snake_case__ )

				# Now we train the model
				for epoch in range(snake_case__ ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCAmelCase  =				0
								for step, batch in enumerate(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase  =				loss / gradient_accumulation_steps
												accelerator.backward(snake_case__ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(snake_case__ ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device )
												with torch.no_grad():
																lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.logits.argmax(dim=-1 )
												lowerCAmelCase ,						lowerCAmelCase  =				accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=snake_case__      ,       references=snake_case__      ,       )

								lowerCAmelCase  =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''      ,       snake_case__ )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(snake_case__ ),
												        """epoch""": epoch,
												    }      ,       step=snake_case__      ,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""      ,       type=snake_case__      ,       default=snake_case__      ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,       help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""      ,       )
				parser.add_argument("""--cpu"""      ,       action="""store_true"""      ,       help="""If passed, will train on the CPU.""" )
				parser.add_argument(
				    """--with_tracking"""      ,       action="""store_true"""      ,       help="""Whether to load in all available experiment trackers from the environment and use them for logging."""      ,       )
				parser.add_argument(
				    """--project_dir"""      ,       type=snake_case__      ,       default="""logs"""      ,       help="""Location on where to store experiment tracking logs` and relevent project information"""      ,       )
				lowerCAmelCase  =				parser.parse_args()
				lowerCAmelCase  =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		main()





