


'''simple docstring'''
import copy
import re
class        lowercase     :

		"""simple docstring"""

		_a   			=    'hp'
		_a   			=    {}
		_a   			=    None




		@classmethod
		def    lowerCAmelCase__    (		cls   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			prefix
							UpperCamelCase__       :Dict							 =			defaults
							cls.build_naming_info()




		@staticmethod
		def    lowerCAmelCase__    (		UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							if len(UpperCamelCase_							) == 0:
												return ""
							UpperCamelCase__       :Any							 =			None
							if any(char.isdigit() for char in word							):
												raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number'''							)
							if word in info["short_word"]:
												return info["short_word"][word]
							for prefix_len in range(1   ,       len(UpperCamelCase_							) + 1							):
												UpperCamelCase__       :str							 =			word[:prefix_len]
												if prefix in info["reverse_short_word"]:
																	continue
												else:
																	UpperCamelCase__       :Optional[Any]							 =			prefix
																	break

							if short_word is None:
												# Paranoid fallback
												def int_to_alphabetic(UpperCamelCase_							):
																	UpperCamelCase__       :str							 =			''''''
																	while integer != 0:
																						UpperCamelCase__       :List[str]							 =			chr(ord('''A'''							) + integer % 10							) + s
																						integer //= 10
																	return s

												UpperCamelCase__       :Dict							 =			0
												while True:
																	UpperCamelCase__       :Optional[int]							 =			word + '''#''' + int_to_alphabetic(UpperCamelCase_							)
																	if sword in info["reverse_short_word"]:
																						continue
																	else:
																						UpperCamelCase__       :List[str]							 =			sword
																						break

							UpperCamelCase__       :Optional[int]							 =			short_word
							UpperCamelCase__       :Optional[Any]							 =			word
							return short_word




		@staticmethod
		def    lowerCAmelCase__    (		UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			param_name.split('''_'''							)

							UpperCamelCase__       :str							 =			[TrialShortNamer.shortname_for_word(UpperCamelCase_   ,       UpperCamelCase_							) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							UpperCamelCase__       :List[str]							 =			['''''', '''_''']

							for separator in separators:
												UpperCamelCase__       :Any							 =			separator.join(UpperCamelCase_							)
												if shortname not in info["reverse_short_param"]:
																	UpperCamelCase__       :Tuple							 =			shortname
																	UpperCamelCase__       :int							 =			param_name
																	return shortname

							return param_name




		@staticmethod
		def    lowerCAmelCase__    (		UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			TrialShortNamer.shortname_for_key(UpperCamelCase_   ,       UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			short_name
							UpperCamelCase__       :str							 =			param_name




		@classmethod
		def    lowerCAmelCase__    (		cls							):

							'''simple docstring'''


							if cls.NAMING_INFO is not None:
												return

							UpperCamelCase__       :str							 =			{
							    '''short_word''': {},
							    '''reverse_short_word''': {},
							    '''short_param''': {},
							    '''reverse_short_param''': {},
							}

							UpperCamelCase__       :List[Any]							 =			list(cls.DEFAULTS.keys()							)

							for k in field_keys:
												cls.add_new_param_name(UpperCamelCase_   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			info




		@classmethod
		def    lowerCAmelCase__    (		cls   ,       UpperCamelCase_							):

							'''simple docstring'''


							cls.build_naming_info()
							assert cls.PREFIX is not None
							UpperCamelCase__       :Any							 =			[copy.copy(cls.PREFIX							)]

							for k, v in params.items():
												if k not in cls.DEFAULTS:
																	raise Exception(F'''You should provide a default value for the param name {k} with value {v}'''							)
												if v == cls.DEFAULTS[k]:
																	# The default value is not added to the name
																	continue

												UpperCamelCase__       :str							 =			cls.NAMING_INFO['''short_param'''][k]

												if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
																	UpperCamelCase__       :Dict							 =			1 if v else 0

												UpperCamelCase__       :str							 =			'''''' if isinstance(UpperCamelCase_   ,       (int, float)							) else '''-'''
												UpperCamelCase__       :List[str]							 =			F'''{key}{sep}{v}'''
												name.append(UpperCamelCase_							)

							return "_".join(UpperCamelCase_							)




		@classmethod
		def    lowerCAmelCase__    (		cls   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			repr[len(cls.PREFIX							) + 1 :]
							if repr == "":
												UpperCamelCase__       :Dict							 =			[]
							else:
												UpperCamelCase__       :Any							 =			repr.split('''_'''							)

							UpperCamelCase__       :Optional[Any]							 =			{}

							for value in values:
												if "-" in value:
																	UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			value.split('''-'''							)
												else:
																	UpperCamelCase__       :List[str]							 =			re.sub('''[0-9.]'''   ,       ''''''   ,       UpperCamelCase_							)
																	UpperCamelCase__       :Optional[int]							 =			float(re.sub('''[^0-9.]'''   ,       ''''''   ,       UpperCamelCase_							)							)

												UpperCamelCase__       :Union[str, Any]							 =			cls.NAMING_INFO['''reverse_short_param'''][p_k]

												UpperCamelCase__       :Optional[Any]							 =			p_v

							for k in cls.DEFAULTS:
												if k not in parameters:
																	UpperCamelCase__       :Tuple							 =			cls.DEFAULTS[k]

							return parameters



'''simple docstring'''
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case											=					16
__snake_case											=					32


def       a				(      __a			, __a = 16			, __a = "bert-base-cased"       )  ->     Any:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			AutoTokenizer.from_pretrained(__a       )
					UpperCamelCase__       :List[Any]							 =			load_dataset('''glue'''			, '''mrpc'''       )

					def tokenize_function(__a       ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__       :Tuple							 =			tokenizer(examples['''sentence1''']			, examples['''sentence2''']			, truncation=__a			, max_length=__a       )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					UpperCamelCase__       :Optional[int]							 =			datasets.map(
					    __a			, batched=__a			, remove_columns=['''idx''', '''sentence1''', '''sentence2''']			, load_from_cache_file=__a       )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					UpperCamelCase__       :Optional[int]							 =			tokenized_datasets.rename_column('''label'''			, '''labels'''       )

					def collate_fn(__a       ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__a			, padding='''max_length'''			, max_length=128			, return_tensors='''pt'''       )
										return tokenizer.pad(__a			, padding='''longest'''			, return_tensors='''pt'''       )

					# Instantiate dataloaders.
					UpperCamelCase__       :Dict							 =			DataLoader(
					    tokenized_datasets['''train''']			, shuffle=__a			, collate_fn=__a			, batch_size=__a       )
					UpperCamelCase__       :str							 =			DataLoader(
					    tokenized_datasets['''validation''']			, shuffle=__a			, collate_fn=__a			, batch_size=__a       )

					return train_dataloader, eval_dataloader


def       a				(      __a			, __a			, __a			, __a       )  ->     str:



					'''simple docstring'''






					model.eval()
					UpperCamelCase__       :List[str]							 =			0
					for step, batch in enumerate(__a       ):
										# We could avoid this line since we set the accelerator with `device_placement=True`.
										batch.to(accelerator.device       )
										with torch.no_grad():
															UpperCamelCase__       :int							 =			model(**__a       )
										UpperCamelCase__       :Tuple							 =			outputs.logits.argmax(dim=-1       )
										# It is slightly faster to call this once, than multiple times
										UpperCamelCase__ ,    UpperCamelCase__       :int							 =			accelerator.gather(
										    (predictions, batch['''labels'''])       )  # If we are in a multiprocess environment, the last batch has duplicates
										if accelerator.use_distributed:
															if step == len(__a       ) - 1:
																				UpperCamelCase__       :Union[str, Any]							 =			predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																				UpperCamelCase__       :List[str]							 =			references[: len(eval_dataloader.dataset       ) - samples_seen]
															else:
																				samples_seen += references.shape[0]
										metric.add_batch(
										    predictions=__a			, references=__a			, )

					UpperCamelCase__       :Union[str, Any]							 =			metric.compute()
					return eval_metric["accuracy"]


def       a				(      __a			, __a       )  ->     List[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Any							 =			Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase__       :Any							 =			config['''lr''']
					UpperCamelCase__       :Optional[int]							 =			int(config['''num_epochs''']       )
					UpperCamelCase__       :List[Any]							 =			int(config['''seed''']       )
					UpperCamelCase__       :List[Any]							 =			int(config['''batch_size''']       )
					UpperCamelCase__       :List[Any]							 =			args.model_name_or_path

					set_seed(__a       )
					UpperCamelCase__ ,    UpperCamelCase__       :Any							 =			get_dataloaders(__a			, __a			, __a       )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__       :Union[str, Any]							 =			AutoModelForSequenceClassification.from_pretrained(__a			, return_dict=__a       )

					# Instantiate optimizer
					UpperCamelCase__       :Any							 =			(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					UpperCamelCase__       :Optional[Any]							 =			optimizer_cls(params=model.parameters()			, lr=__a       )

					if accelerator.state.deepspeed_plugin is not None:
										UpperCamelCase__       :Tuple							 =			accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										UpperCamelCase__       :Dict							 =			1
					UpperCamelCase__       :Tuple							 =			(len(__a       ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										UpperCamelCase__       :Any							 =			get_linear_schedule_with_warmup(
										    optimizer=__a			, num_warmup_steps=0			, num_training_steps=__a			, )
					else:
										UpperCamelCase__       :Any							 =			DummyScheduler(__a			, total_num_steps=__a			, warmup_num_steps=0       )

					# 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.
					UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       :Dict							 =			accelerator.prepare(
					    __a			, __a			, __a			, __a			, __a       )

					# We need to keep track of how many total steps we have iterated over
					UpperCamelCase__       :Tuple							 =			0
					# We also need to keep track of the stating epoch so files are named properly
					UpperCamelCase__       :Optional[Any]							 =			0
					UpperCamelCase__       :Optional[int]							 =			evaluate.load('''glue'''			, '''mrpc'''       )
					UpperCamelCase__       :List[Any]							 =			num_epochs

					if args.partial_train_epoch is not None:
										UpperCamelCase__       :Optional[Any]							 =			args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint       )
										UpperCamelCase__       :Dict							 =			args.resume_from_checkpoint.split('''epoch_'''       )[1]
										UpperCamelCase__       :Tuple							 =			''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										UpperCamelCase__       :Any							 =			int(__a       ) + 1
										UpperCamelCase__       :Dict							 =			evaluation_loop(__a			, __a			, __a			, __a       )
										accelerator.print('''resumed checkpoint performance:'''			, __a       )
										accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''			, lr_scheduler.get_lr()[0]       )
										accelerator.print('''resumed optimizers\'s lr:'''			, optimizer.param_groups[0]['''lr''']       )
										with open(os.path.join(args.output_dir			, f'''state_{starting_epoch-1}.json'''       )			, '''r'''       ) as f:
															UpperCamelCase__       :Optional[int]							 =			json.load(__a       )
															assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["lr"] == lr_scheduler.get_lr()[0]
															), "Scheduler learning rate mismatch, loading from checkpoint failed"
															assert (
															    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
															), "Optimizer learning rate mismatch, loading from checkpoint failed"
															assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
															return

    # Now we train the model
					UpperCamelCase__       :Optional[Any]							 =			{}
					for epoch in range(__a			, __a       ):
										model.train()
										for step, batch in enumerate(__a       ):
															UpperCamelCase__       :Optional[int]							 =			model(**__a       )
															UpperCamelCase__       :Optional[int]							 =			outputs.loss
															UpperCamelCase__       :str							 =			loss / gradient_accumulation_steps
															accelerator.backward(__a       )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1
										UpperCamelCase__       :Union[str, Any]							 =			f'''epoch_{epoch}'''
										UpperCamelCase__       :List[Any]							 =			os.path.join(args.output_dir			, __a       )
										accelerator.save_state(__a       )
										UpperCamelCase__       :List[Any]							 =			evaluation_loop(__a			, __a			, __a			, __a       )
										UpperCamelCase__       :int							 =			accuracy
										UpperCamelCase__       :List[Any]							 =			lr_scheduler.get_lr()[0]
										UpperCamelCase__       :Any							 =			optimizer.param_groups[0]['''lr''']
										UpperCamelCase__       :int							 =			epoch
										UpperCamelCase__       :Tuple							 =			overall_step
										accelerator.print(f'''epoch {epoch}:'''			, __a       )

										accelerator.wait_for_everyone()
										if accelerator.is_main_process:
															with open(os.path.join(args.output_dir			, f'''state_{epoch}.json'''       )			, '''w'''       ) as f:
																				json.dump(__a			, __a       )


def       a				(      )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :List[Any]							 =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
					parser.add_argument(
					    '''--model_name_or_path'''			, type=__a			, default='''bert-base-cased'''			, help='''Path to pretrained model or model identifier from huggingface.co/models.'''			, required=__a			, )
					parser.add_argument(
					    '''--output_dir'''			, type=__a			, default='''.'''			, help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			, )
					parser.add_argument(
					    '''--resume_from_checkpoint'''			, type=__a			, default=__a			, help='''If the training should continue from a checkpoint folder.'''			, )
					parser.add_argument(
					    '''--partial_train_epoch'''			, type=__a			, default=__a			, help='''If passed, the training will stop after this number of epochs.'''			, )
					parser.add_argument(
					    '''--num_epochs'''			, type=__a			, default=2			, help='''Number of train epochs.'''			, )
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()
					UpperCamelCase__       :List[str]							 =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(__a			, __a       )


if __name__ == "__main__":
		main()