


"""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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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_       							=    16
a_       							=    32






def      a__ (				__lowercase     ,			__lowercase = 16   )	->	Dict:
				_A					  =			AutoTokenizer.from_pretrained("bert-base-cased"   )
				_A					  =			load_dataset("glue"     ,			"mrpc"   )

				def tokenize_function(__lowercase   ):
								# max_length=None => use the model max length (it's actually the default)
								_A					  =			tokenizer(examples["sentence1"]     ,			examples["sentence2"]     ,			truncation=__lowercase     ,			max_length=__lowercase   )
								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():
								_A					  =			datasets.map(
								    __lowercase     ,			batched=__lowercase     ,			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
				_A					  =			tokenized_datasets.rename_column("label"     ,			"labels"   )

				def collate_fn(__lowercase   ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_A					  =			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":
												_A					  =			16
								elif accelerator.mixed_precision != "no":
												_A					  =			8
								else:
												_A					  =			None

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

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

				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      a__ (				__lowercase     ,			__lowercase   )	->	Optional[int]:
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,			__lowercase   ) == "1":
								_A					  =			2
				# New Code #
				_A					  =			int(args.gradient_accumulation_steps   )
				_A					  =			int(args.local_sgd_steps   )
				# Initialize accelerator
				_A					  =			Accelerator(
				    cpu=args.cpu     ,			mixed_precision=args.mixed_precision     ,			gradient_accumulation_steps=__lowercase   )
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"   )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_A					  =			config["lr"]
				_A					  =			int(config["num_epochs"]   )
				_A					  =			int(config["seed"]   )
				_A					  =			int(config["batch_size"]   )

				_A					  =			evaluate.load("glue"     ,			"mrpc"   )

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

				# 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).
				_A					  =			model.to(accelerator.device   )

				# Instantiate optimizer
				_A					  =			AdamW(params=model.parameters()     ,			lr=__lowercase   )

				# Instantiate scheduler
				_A					  =			get_linear_schedule_with_warmup(
				    optimizer=__lowercase     ,			num_warmup_steps=100     ,			num_training_steps=(len(__lowercase   ) * num_epochs)     ,			)

				# 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.
				_A  ,		_A  ,		_A  ,		_A  ,		_A					  =			accelerator.prepare(
				    __lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )

				# Now we train the model
				for epoch in range(__lowercase   ):
								model.train()
								with LocalSGD(
								    accelerator=__lowercase     ,			model=__lowercase     ,			local_sgd_steps=__lowercase     ,			enabled=local_sgd_steps is not None   ) as local_sgd:
												for step, batch in enumerate(__lowercase   ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device   )
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(__lowercase   ):
																				_A					  =			model(**__lowercase   )
																				_A					  =			output.loss
																				accelerator.backward(__lowercase   )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

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

								_A					  =			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:"""     ,			__lowercase   )






def      a__ (				)	->	Dict:
				_A					  =			argparse.ArgumentParser(description="Simple example of training script."   )
				parser.add_argument(
				    "--mixed_precision"     ,			type=__lowercase     ,			default=__lowercase     ,			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."     ,			)
				# New Code #
				parser.add_argument(
				    "--gradient_accumulation_steps"     ,			type=__lowercase     ,			default=1     ,			help="The number of minibatches to be ran before gradients are accumulated."     ,			)
				parser.add_argument(
				    "--local_sgd_steps"     ,			type=__lowercase     ,			default=8     ,			help="Number of local SGD steps or None to disable local SGD"   )
				parser.add_argument("--cpu"     ,			action="store_true"     ,			help="If passed, will train on the CPU."   )
				_A					  =			parser.parse_args()
				_A					  =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(__lowercase     ,			__lowercase   )


if __name__ == "__main__":
		main()



"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
		from ...tokenization_utils_base import TextInput
from ...utils import logging


a_       							=    logging.get_logger(__name__)

a_       							=    {"vocab_file": "spiece.model"}

a_       							=    {
    "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",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a_       							=    {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

a_       							=    "▁"
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			VOCAB_FILES_NAMES
		__UpperCamelCase           =			PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase           =			['input_ids', 'attention_mask']







		def __init__(					self      :       List[str]    ,   a__      :       Optional[int]    ,   a__      :       Union[str, Any]="</s>"    ,   a__      :       Union[str, Any]="<unk>"    ,   a__      :       str="<pad>"    ,   a__      :       Optional[int]=1_00    ,   a__      :       List[Any]=None    ,   a__      :       Optional[Dict[str, Any]] = None    ,   a__      :       Any=True    ,   **a__      :       Optional[int]    ,   )       ->						None:





						'''simple docstring'''







						if extra_ids > 0 and additional_special_tokens is None:
										_A					  =			[F"""<extra_id_{i}>""" for i in range(a__		)]
						elif extra_ids > 0 and additional_special_tokens is not None:
										# Check that we have the right number of extra_id special tokens
										_A					  =			len(set(filter(lambda a__		: bool("extra_id" in str(a__		)		)    ,   a__		)		)		)
										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"		)
						if legacy:
										logger.warning_once(
										    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
										    " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"		)

						_A					  =			legacy
						_A					  =			{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=a__    ,   unk_token=a__    ,   pad_token=a__    ,   extra_ids=a__    ,   additional_special_tokens=a__    ,   sp_model_kwargs=self.sp_model_kwargs    ,   legacy=a__    ,   **a__    ,   )

						_A					  =			vocab_file
						_A					  =			extra_ids

						_A					  =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(a__		)







		@staticmethod
		def  a_ (					a__      :       List[str]    ,   a__      :       Optional[int]    ,   a__      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
										_A					  =			TaTokenizer.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."    ,   a__    ,   )

						return max_model_length







		@property
		def  a_ (					self      :       List[Any]		)       ->						Dict:





						'''simple docstring'''







						return self.sp_model.get_piece_size() + self._extra_ids







		def  a_ (					self      :       Dict		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			{self.convert_ids_to_tokens(a__		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab







		def  a_ (					self      :       Optional[Any]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None    ,   a__      :       bool = False		)       ->						List[int]:





						'''simple docstring'''







						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=a__    ,   token_ids_a=a__    ,   already_has_special_tokens=a__		)

						# normal case: some special tokens
						if token_ids_a is None:
										return ([0] * len(a__		)) + [1]
						return ([0] * len(a__		)) + [1] + ([0] * len(a__		)) + [1]







		def  a_ (					self      :       List[str]		)       ->						List[str]:





						'''simple docstring'''







						return list(
						    set(filter(lambda a__		: bool(re.search(r"<extra_id_\d+>"    ,   a__		)		) is not None    ,   self.additional_special_tokens		)		)		)







		def  a_ (					self      :       str		)       ->						List[Any]:





						'''simple docstring'''







						return [self._convert_token_to_id(a__		) for token in self.get_sentinel_tokens()]







		def  a_ (					self      :       List[Any]    ,   a__      :       List[int]		)       ->						List[int]:





						'''simple docstring'''







						if len(a__		) > 0 and token_ids[-1] == self.eos_token_id:
										warnings.warn(
										    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
										    " eos tokens being added."		)
										return token_ids
						else:
										return token_ids + [self.eos_token_id]







		def  a_ (					self      :       int    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			[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      :       Union[str, Any]    ,   a__      :       List[int]    ,   a__      :       Optional[List[int]] = None		)       ->						List[int]:





						'''simple docstring'''







						_A					  =			self._add_eos_if_not_present(a__		)
						if token_ids_a is None:
										return token_ids_a
						else:
										_A					  =			self._add_eos_if_not_present(a__		)
										return token_ids_a + token_ids_a







		def __getstate__(					self      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.__dict__.copy()
						_A					  =			None
						return state







		def __setstate__(					self      :       int    ,   a__      :       Optional[int]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			d

						# for backward compatibility
						if not hasattr(self    ,   "sp_model_kwargs"		):
										_A					  =			{}

						_A					  =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(self.vocab_file		)







		def  a_ (					self      :       int    ,   a__      :       "TextInput"    ,   **a__      :       List[str]		)       ->						List[str]:





						'''simple docstring'''







						if not self.legacy:
										_A					  =			SPIECE_UNDERLINE + text.replace(a__    ,   " "		)
						return super().tokenize(a__    ,   **a__		)







		def  a_ (					self      :       str    ,   a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						Any:





						'''simple docstring'''







						if not self.legacy:
										_A					  =			text.startswith(a__		)
										if is_first:
														_A					  =			text[1:]

						_A					  =			self.sp_model.encode(a__    ,   out_type=a__		)

						if not self.legacy and not is_first and not text.startswith(" "		) and tokens[0].startswith(a__		):
										_A					  =			([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
						return tokens







		def  a_ (					self      :       int    ,   a__      :       List[Any]		)       ->						List[str]:





						'''simple docstring'''







						if token.startswith("<extra_id_"		):
										_A					  =			re.match(r"<extra_id_(\d+)>"    ,   a__		)
										_A					  =			int(match.group(1		)		)
										return self.vocab_size - num - 1
						return self.sp_model.piece_to_id(a__		)







		def  a_ (					self      :       Dict    ,   a__      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







						if index < self.sp_model.get_piece_size():
										_A					  =			self.sp_model.IdToPiece(a__		)
						else:
										_A					  =			F"""<extra_id_{self.vocab_size - 1 - index}>"""
						return token







		def  a_ (					self      :       Optional[int]    ,   a__      :       Tuple		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			[]
						_A					  =			""
						_A					  =			False
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														if not prev_is_special:
																		out_string += " "
														out_string += self.sp_model.decode(a__		) + token
														_A					  =			True
														_A					  =			[]
										else:
														current_sub_tokens.append(a__		)
														_A					  =			False
						out_string += self.sp_model.decode(a__		)
						return out_string.strip()







		def  a_ (					self      :       Dict    ,   a__      :       str    ,   a__      :       Optional[str] = None		)       ->						Tuple[str]:





						'''simple docstring'''







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

						if os.path.abspath(self.vocab_file		) != os.path.abspath(a__		) and os.path.isfile(self.vocab_file		):
										copyfile(self.vocab_file    ,   a__		)
						elif not os.path.isfile(self.vocab_file		):
										with open(a__    ,   "wb"		) as fi:
														_A					  =			self.sp_model.serialized_model_proto()
														fi.write(a__		)

						return (out_vocab_file,)