



'''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_ :	int         =  16
snake_case_ :	int         =  32






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_ = 1_6  ,  UpperCAmelCase_ = "bert-base-cased"							):
						_UpperCamelCase					:		List[Any] 				=   AutoTokenizer.from_pretrained(UpperCAmelCase_							)
						_UpperCamelCase					:		List[Any] 				=   load_dataset('glue'  ,  'mrpc'							)

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

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

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

						def collate_fn(UpperCAmelCase_							):
												# 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(UpperCAmelCase_  ,  padding='max_length'  ,  max_length=1_2_8  ,  return_tensors='pt'							)
												return tokenizer.pad(UpperCAmelCase_  ,  padding='longest'  ,  return_tensors='pt'							)

						# Instantiate dataloaders.
						_UpperCamelCase					:		Any 				=   DataLoader(
						    tokenized_datasets['train']  ,  shuffle=UpperCAmelCase_  ,  collate_fn=UpperCAmelCase_  ,  batch_size=UpperCAmelCase_							)
						_UpperCamelCase					:		int 				=   DataLoader(
						    tokenized_datasets['validation']  ,  shuffle=UpperCAmelCase_  ,  collate_fn=UpperCAmelCase_  ,  batch_size=UpperCAmelCase_							)

						return train_dataloader, eval_dataloader






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						# Initialize accelerator
						_UpperCamelCase					:		List[Any] 				=   Accelerator()

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

						set_seed(UpperCAmelCase_							)
						_UpperCamelCase				,			_UpperCamelCase					:		Dict 				=   get_dataloaders(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCamelCase					:		str 				=   AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_  ,  return_dict=UpperCAmelCase_							)

						# Instantiate optimizer
						_UpperCamelCase					:		Tuple 				=   (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						_UpperCamelCase					:		Union[str, Any] 				=   optimizer_cls(params=model.parameters()  ,  lr=UpperCAmelCase_							)

						if accelerator.state.deepspeed_plugin is not None:
												_UpperCamelCase					:		int 				=   accelerator.state.deepspeed_plugin.deepspeed_config[
												    'gradient_accumulation_steps'
												]
						else:
												_UpperCamelCase					:		List[Any] 				=   1
						_UpperCamelCase					:		str 				=   (len(UpperCAmelCase_							) * 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					:		Union[str, Any] 				=   get_linear_schedule_with_warmup(
												    optimizer=UpperCAmelCase_  ,  num_warmup_steps=0  ,  num_training_steps=UpperCAmelCase_  ,  )
						else:
												_UpperCamelCase					:		str 				=   DummyScheduler(UpperCAmelCase_  ,  total_num_steps=UpperCAmelCase_  ,  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(
						    UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)

						# We need to keep track of how many total steps we have iterated over
						_UpperCamelCase					:		str 				=   0
						# We also need to keep track of the stating epoch so files are named properly
						_UpperCamelCase					:		int 				=   0

						# Now we train the model
						_UpperCamelCase					:		Any 				=   evaluate.load('glue'  ,  'mrpc'							)
						_UpperCamelCase					:		Union[str, Any] 				=   0
						_UpperCamelCase					:		str 				=   {}
						for epoch in range(UpperCAmelCase_  ,  UpperCAmelCase_							):
												model.train()
												for step, batch in enumerate(UpperCAmelCase_							):
																		_UpperCamelCase					:		Dict 				=   model(**UpperCAmelCase_							)
																		_UpperCamelCase					:		Dict 				=   outputs.loss
																		_UpperCamelCase					:		List[str] 				=   loss / gradient_accumulation_steps
																		accelerator.backward(UpperCAmelCase_							)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

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

												_UpperCamelCase					:		Any 				=   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'  ,  UpperCAmelCase_							)
												_UpperCamelCase					:		Optional[Any] 				=   eval_metric['accuracy']

												if best_performance < eval_metric["accuracy"]:
																		_UpperCamelCase					:		Dict 				=   eval_metric['accuracy']

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir  ,  'all_results.json'							)  ,  'w'							) as f:
																		json.dump(UpperCAmelCase_  ,  UpperCAmelCase_							)






def A__    (     ):
						_UpperCamelCase					:		Union[str, Any] 				=   argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
						parser.add_argument(
						    '--model_name_or_path'  ,  type=UpperCAmelCase_  ,  default='bert-base-cased'  ,  help='Path to pretrained model or model identifier from huggingface.co/models.'  ,  required=UpperCAmelCase_  ,  )
						parser.add_argument(
						    '--output_dir'  ,  type=UpperCAmelCase_  ,  default='.'  ,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'  ,  )
						parser.add_argument(
						    '--performance_lower_bound'  ,  type=UpperCAmelCase_  ,  default=UpperCAmelCase_  ,  help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'  ,  )
						parser.add_argument(
						    '--num_epochs'  ,  type=UpperCAmelCase_  ,  default=3  ,  help='Number of train epochs.'  ,  )
						_UpperCamelCase					:		List[Any] 				=   parser.parse_args()
						_UpperCamelCase					:		Optional[int] 				=   {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
						training_function(UpperCAmelCase_  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					main()








'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							a  :				Dict									=  None


a  :				List[Any]									=  logging.get_logger(__name__)

a  :				List[Any]									=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a  :				str									=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a  :				List[Any]									=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]
				snake_case_      =		TaTokenizer

				snake_case_      =		[]


				def __init__(		self    :					List[Any]       ,				lowercase_    :					int=None       ,				lowercase_    :					Dict=None       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					List[Any]="<unk>"       ,				lowercase_    :					int="<pad>"       ,				lowercase_    :					int=100       ,				lowercase_    :					List[Any]=None       ,				**lowercase_    :					List[str]       ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
														snake_case_									=				[F"<extra_id_{i}>" for i in range(lowercase_					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														snake_case_									=				len(set(filter(lambda lowercase_					: bool('''extra_id_''' in str(lowercase_					)					)       ,				lowercase_					)					)					)
														if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																			    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																			    ''' tokens'''					)

									super().__init__(
									    lowercase_       ,				tokenizer_file=lowercase_       ,				eos_token=lowercase_       ,				unk_token=lowercase_       ,				pad_token=lowercase_       ,				extra_ids=lowercase_       ,				additional_special_tokens=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_file
									snake_case_									=				False if not self.vocab_file else True
									snake_case_									=				extra_ids


				@staticmethod
				def   A_ (		lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														snake_case_									=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
														elif init_max_model_length is None:
																			warnings.warn(
																			    '''This tokenizer was incorrectly instantiated with a model max length of'''
																			    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																			    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																			    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																			    F" {pretrained_model_name_or_path} automatically truncating your input to"
																			    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																			    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																			    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																			    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,				lowercase_       ,				)

									return max_model_length


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''					)

									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					):
														copyfile(self.vocab_file       ,				lowercase_					)
														logger.info(F"Copy vocab file to {out_vocab_file}"					)

									return (out_vocab_file,)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
									else:
														snake_case_									=				token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a


				def   A_ (		self    :					int       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[self.eos_token_id]

									if token_ids_a is None:
														return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


				def   A_ (		self    :					Dict					):
									return list(
									    set(filter(lambda lowercase_					: bool(re.search(R'''<extra_id_\d+>'''       ,				lowercase_					)					) is not None       ,				self.additional_special_tokens					)					)					)


				def   A_ (		self    :					Any					):
									return [self.convert_tokens_to_ids(lowercase_					) for token in self.get_sentinel_tokens()]

