



'''simple docstring'''



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase	 =    logging.get_logger(__name__)

lowerCamelCase	 =    {'''vocab_file''': '''spiece.model'''}

lowerCamelCase	 =    {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

lowerCamelCase	 =    {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
lowerCamelCase	 =    0
lowerCamelCase	 =    1
lowerCamelCase	 =    2
lowerCamelCase	 =    3
lowerCamelCase	 =    4





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    VOCAB_FILES_NAMES
				lowerCAmelCase__					    =    PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__					    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__					    =    """left"""

				def __init__(							self  :  Dict  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  Dict=False  ,       _lowerCAmelCase  :  int=True  ,       _lowerCAmelCase  :  List[Any]=False  ,       _lowerCAmelCase  :  List[str]="<s>"  ,       _lowerCAmelCase  :  List[Any]="</s>"  ,       _lowerCAmelCase  :  Optional[Any]="<unk>"  ,       _lowerCAmelCase  :  int="<sep>"  ,       _lowerCAmelCase  :  int="<pad>"  ,       _lowerCAmelCase  :  Optional[int]="<cls>"  ,       _lowerCAmelCase  :  int="<mask>"  ,       _lowerCAmelCase  :  Optional[int]=["<eop>", "<eod>"]  ,       _lowerCAmelCase  :  Optional[Dict[str, Any]] = None  ,       **_lowerCAmelCase  :  List[Any]  ,       ):







											'''simple docstring'''

											__lowercase       	=AddedToken(UpperCamelCase_  ,       lstrip=UpperCamelCase_  ,       rstrip=UpperCamelCase_) if isinstance(UpperCamelCase_  ,       UpperCamelCase_) else mask_token

											__lowercase       	={} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=UpperCamelCase_  ,       remove_space=UpperCamelCase_  ,       keep_accents=UpperCamelCase_  ,       bos_token=UpperCamelCase_  ,       eos_token=UpperCamelCase_  ,       unk_token=UpperCamelCase_  ,       sep_token=UpperCamelCase_  ,       pad_token=UpperCamelCase_  ,       cls_token=UpperCamelCase_  ,       mask_token=UpperCamelCase_  ,       additional_special_tokens=UpperCamelCase_  ,       sp_model_kwargs=self.sp_model_kwargs  ,       **UpperCamelCase_  ,       )

											__lowercase       	=3

											__lowercase       	=do_lower_case
											__lowercase       	=remove_space
											__lowercase       	=keep_accents
											__lowercase       	=vocab_file

											__lowercase       	=spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(UpperCamelCase_)

				@property
				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

											return len(self.sp_model)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

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

				def __getstate__(							self  :  List[str]):







											'''simple docstring'''

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

				def __setstate__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Tuple):







											'''simple docstring'''

											__lowercase       	=d

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

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

				def 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											if self.remove_space:
																		__lowercase       	=' '.join(inputs.strip().split())
											else:
																		__lowercase       	=inputs
											__lowercase       	=outputs.replace('``'  ,       '"').replace('\'\''  ,       '"')

											if not self.keep_accents:
																		__lowercase       	=unicodedata.normalize('NFKD'  ,       UpperCamelCase_)
																		__lowercase       	=''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase_)])
											if self.do_lower_case:
																		__lowercase       	=outputs.lower()

											return outputs

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  str):







											'''simple docstring'''

											__lowercase       	=self.preprocess_text(UpperCamelCase_)
											__lowercase       	=self.sp_model.encode(UpperCamelCase_  ,       out_type=UpperCamelCase_)
											__lowercase       	=[]
											for piece in pieces:
																		if len(UpperCamelCase_) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
																									__lowercase       	=self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase_  ,       ''))
																									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																if len(cur_pieces[0]) == 1:
																																							__lowercase       	=cur_pieces[1:]
																																else:
																																							__lowercase       	=cur_pieces[0][1:]
																									cur_pieces.append(piece[-1])
																									new_pieces.extend(UpperCamelCase_)
																		else:
																									new_pieces.append(UpperCamelCase_)

											return new_pieces

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.sp_model.PieceToId(UpperCamelCase_)

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.sp_model.IdToPiece(UpperCamelCase_)

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  Any):







											'''simple docstring'''

											__lowercase       	=''.join(UpperCamelCase_).replace(UpperCamelCase_  ,       ' ').strip()
											return out_string

				def 				__lowerCamelCase     (							self  :  Any  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = None  ,       _lowerCAmelCase  :  bool = True  ,       **_lowerCAmelCase  :  Union[str, Any]  ,       ):







											'''simple docstring'''

											__lowercase       	=kwargs.pop('use_source_tokenizer'  ,       UpperCamelCase_)

											__lowercase       	=self.convert_ids_to_tokens(UpperCamelCase_  ,       skip_special_tokens=UpperCamelCase_)

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											__lowercase       	=[]
											__lowercase       	=[]
											for token in filtered_tokens:
																		if skip_special_tokens and token in self.all_special_ids:
																									continue
																		if token in self.added_tokens_encoder:
																									if current_sub_text:
																																sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_))
																																__lowercase       	=[]
																									sub_texts.append(UpperCamelCase_)
																		else:
																									current_sub_text.append(UpperCamelCase_)
											if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_))

											# Mimic the behavior of the Rust tokenizer:
											# By default, there are no spaces between special tokens
											__lowercase       	=''.join(UpperCamelCase_)

											__lowercase       	=(
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
																		__lowercase       	=self.clean_up_tokenization(UpperCamelCase_)
																		return clean_text
											else:
																		return text

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  Optional[List[int]] = None):







											'''simple docstring'''

											__lowercase       	=[self.sep_token_id]
											__lowercase       	=[self.cls_token_id]
											if token_ids_a is None:
																		return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  Optional[List[int]] = None  ,       _lowerCAmelCase  :  bool = False):







											'''simple docstring'''

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

											if token_ids_a is not None:
																		return ([0] * len(UpperCamelCase_)) + [1] + ([0] * len(UpperCamelCase_)) + [1, 1]
											return ([0] * len(UpperCamelCase_)) + [1, 1]

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[int]  ,       _lowerCAmelCase  :  Optional[List[int]] = None):







											'''simple docstring'''

											__lowercase       	=[self.sep_token_id]
											__lowercase       	=[2]

											if token_ids_a is None:
																		return len(token_ids_a + sep) * [0] + cls_segment_id
											return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  str  ,       _lowerCAmelCase  :  Optional[str] = None):







											'''simple docstring'''

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

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

											return (out_vocab_file,)








'''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
#
########################################################################


lowerCamelCase	 =       16
lowerCamelCase	 =       32



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = 16					):



							"""simple docstring"""



							__lowercase       	=AutoTokenizer.from_pretrained('bert-base-cased'					)
							__lowercase       	=load_dataset('glue'  ,   'mrpc'					)

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

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

														return tokenizer.pad(
														    _lowerCAmelCase  ,   padding='longest'  ,   max_length=_lowerCAmelCase  ,   pad_to_multiple_of=_lowerCAmelCase  ,   return_tensors='pt'  ,   )

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

							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

		lowerCamelCase	 =       mocked_dataloaders  # noqa: F811



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,   _lowerCAmelCase					) == "1":
														__lowercase       	=2
							# New Code #
							__lowercase       	=int(args.gradient_accumulation_steps					)
							__lowercase       	=int(args.local_sgd_steps					)
							# Initialize accelerator
							__lowercase       	=Accelerator(
							    cpu=args.cpu  ,   mixed_precision=args.mixed_precision  ,   gradient_accumulation_steps=_lowerCAmelCase					)
							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
							__lowercase       	=config['lr']
							__lowercase       	=int(config['num_epochs']					)
							__lowercase       	=int(config['seed']					)
							__lowercase       	=int(config['batch_size']					)

							__lowercase       	=evaluate.load('glue'  ,   'mrpc'					)

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

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

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

							# Instantiate scheduler
							__lowercase       	=get_linear_schedule_with_warmup(
							    optimizer=_lowerCAmelCase  ,   num_warmup_steps=100  ,   num_training_steps=(len(_lowerCAmelCase					) * 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.
							__lowercase	,				__lowercase	,				__lowercase	,				__lowercase	,				__lowercase       	=accelerator.prepare(
							    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							# Now we train the model
							for epoch in range(_lowerCAmelCase					):
														model.train()
														with LocalSGD(
														    accelerator=_lowerCAmelCase  ,   model=_lowerCAmelCase  ,   local_sgd_steps=_lowerCAmelCase  ,   enabled=local_sgd_steps is not None					) as local_sgd:
																					for step, batch in enumerate(_lowerCAmelCase					):
																												# 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(_lowerCAmelCase					):
																																			__lowercase       	=model(**_lowerCAmelCase					)
																																			__lowercase       	=output.loss
																																			accelerator.backward(_lowerCAmelCase					)
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

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

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



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=argparse.ArgumentParser(description='Simple example of training script.'					)
							parser.add_argument(
							    '--mixed_precision'  ,   type=_lowerCAmelCase  ,   default=_lowerCAmelCase  ,   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=_lowerCAmelCase  ,   default=1  ,   help='The number of minibatches to be ran before gradients are accumulated.'  ,   )
							parser.add_argument(
							    '--local_sgd_steps'  ,   type=_lowerCAmelCase  ,   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.'					)
							__lowercase       	=parser.parse_args()
							__lowercase       	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_lowerCAmelCase  ,   _lowerCAmelCase					)


if __name__ == "__main__":
		main()



