






import argparse

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
#
# 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_		:    Optional[int]   							=     16
a_		:    Any   							=     32






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase = 16):
			SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained('bert-base-cased')
			SCREAMING_SNAKE_CASE							=			load_dataset('glue'				,							'mrpc')

			def tokenize_function(_UpperCAmelCase):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE							=			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
			# starting with the main process first:
			with accelerator.main_process_first():
						SCREAMING_SNAKE_CASE							=			datasets.map(
						    _UpperCAmelCase				,							batched=_UpperCAmelCase				,							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
			SCREAMING_SNAKE_CASE							=			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.
						SCREAMING_SNAKE_CASE							=			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":
									SCREAMING_SNAKE_CASE							=			16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE							=			8
						else:
									SCREAMING_SNAKE_CASE							=			None

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

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE							=			DataLoader(
			    tokenized_datasets['train']				,							shuffle=_UpperCAmelCase				,							collate_fn=_UpperCAmelCase				,							batch_size=_UpperCAmelCase				,							drop_last=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			DataLoader(
			    tokenized_datasets['validation']				,							shuffle=_UpperCAmelCase				,							collate_fn=_UpperCAmelCase				,							batch_size=_UpperCAmelCase				,							drop_last=(accelerator.mixed_precision == 'fp8')				,							)

			return train_dataloader, eval_dataloader






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			# Initialize accelerator
			SCREAMING_SNAKE_CASE							=			Accelerator(cpu=args.cpu				,							mixed_precision=args.mixed_precision)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE							=			config['lr']
			SCREAMING_SNAKE_CASE							=			int(config['num_epochs'])
			SCREAMING_SNAKE_CASE							=			int(config['seed'])
			SCREAMING_SNAKE_CASE							=			int(config['batch_size'])

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

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

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

			# 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).
			SCREAMING_SNAKE_CASE							=			model.to(accelerator.device)
			# Instantiate optimizer
			SCREAMING_SNAKE_CASE							=			AdamW(params=model.parameters()				,							lr=_UpperCAmelCase)

			# Instantiate scheduler
			SCREAMING_SNAKE_CASE							=			get_linear_schedule_with_warmup(
			    optimizer=_UpperCAmelCase				,							num_warmup_steps=100				,							num_training_steps=(len(_UpperCAmelCase) * 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.

			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			accelerator.prepare(
			    _UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)

			# Now we train the model
			for epoch in range(_UpperCAmelCase):
						model.train()
						for step, batch in enumerate(_UpperCAmelCase):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									SCREAMING_SNAKE_CASE							=			model(**_UpperCAmelCase)
									SCREAMING_SNAKE_CASE							=			outputs.loss
									SCREAMING_SNAKE_CASE							=			loss / gradient_accumulation_steps
									accelerator.backward(_UpperCAmelCase)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						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():
												SCREAMING_SNAKE_CASE							=			model(**_UpperCAmelCase)
									SCREAMING_SNAKE_CASE							=			outputs.logits.argmax(dim=-1)
									SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			accelerator.gather_for_metrics((predictions, batch['labels']))
									metric.add_batch(
									    predictions=_UpperCAmelCase				,							references=_UpperCAmelCase				,							)

						SCREAMING_SNAKE_CASE							=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''				,							_UpperCAmelCase)






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			argparse.ArgumentParser(description='Simple example of training script.')
			parser.add_argument(
			    '--mixed_precision'				,							type=_UpperCAmelCase				,							default=_UpperCAmelCase				,							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.')
			SCREAMING_SNAKE_CASE							=			parser.parse_args()
			SCREAMING_SNAKE_CASE							=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_UpperCAmelCase				,							_UpperCAmelCase)


if __name__ == "__main__":
				main()











"""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


__UpperCAmelCase						=			logging.get_logger(__name__)

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

__UpperCAmelCase						=			{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}



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



							'''simple docstring'''



							a__  :	List[str]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else mask_token

							a__  :	List[str]		 =			{} if sp_model_kwargs is None else sp_model_kwargs

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

							a__  :	Union[str, Any]		 =			3

							a__  :	Dict		 =			do_lower_case
							a__  :	Union[str, Any]		 =			remove_space
							a__  :	int		 =			keep_accents
							a__  :	str		 =			vocab_file

							a__  :	str		 =			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(a_    )

							try:
										import jieba
							except ModuleNotFoundError as error:
										raise error.__class__(
										    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
										    "See https://pypi.org/project/jieba/ for installation."    )
							a__  :	Optional[int]		 =			jieba
							a__  :	Optional[int]		 =			str.maketrans(" \n"	, "\u2582\u2583"    )
				@property
				# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							return len(self.sp_model    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Optional[int]		 =			{self.convert_ids_to_tokens(a_    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab
				def __getstate__(		self    :			Tuple    )	->	List[str]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.__dict__.copy()
							a__  :	Union[str, Any]		 =			None
							return state
				def __setstate__(		self    :			Tuple	, a_    :			int    )	->	List[str]:



							'''simple docstring'''



							a__  :	Any		 =			d

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

							a__  :	List[str]		 =			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							if self.remove_space:
										a__  :	Union[str, Any]		 =			" ".join(inputs.strip().split()    )
							else:
										a__  :	Optional[Any]		 =			inputs
							a__  :	List[str]		 =			outputs.replace("``"	, "\""    ).replace("''"	, "\""    )

							if not self.keep_accents:
										a__  :	Union[str, Any]		 =			unicodedata.normalize("NFKD"	, a_    )
										a__  :	Union[str, Any]		 =			"".join([c for c in outputs if not unicodedata.combining(a_    )]    )
							if self.do_lower_case:
										a__  :	List[Any]		 =			outputs.lower()

							return outputs
				def       UpperCAmelCase      (		self    :			Any	, a_    :			str    )	->	List[str]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.preprocess_text(a_    )
							a__  :	Dict		 =			self.sp_model.encode(a_	, out_type=a_    )
							a__  :	Optional[Any]		 =			[]
							for piece in pieces:
										if len(a_    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
													a__  :	Optional[Any]		 =			self.sp_model.EncodeAsPieces(piece[:-1].replace(a_	, ""    )    )
													if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																if len(cur_pieces[0]    ) == 1:
																			a__  :	List[str]		 =			cur_pieces[1:]
																else:
																			a__  :	List[str]		 =			cur_pieces[0][1:]
													cur_pieces.append(piece[-1]    )
													new_pieces.extend(a_    )
										else:
													new_pieces.append(a_    )

							return new_pieces
				def       UpperCAmelCase      (		self    :			int	, a_    :			Dict    )	->	int:



							'''simple docstring'''



							return self.sp_model.PieceToId(a_    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Tuple    )	->	List[Any]:



							'''simple docstring'''



							return self.sp_model.IdToPiece(a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			List[Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"".join(a_    ).replace(a_	, " "    ).strip()
							return out_string
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	List[int]:



							'''simple docstring'''



							a__  :	List[Any]		 =			[self.sep_token_id]
							a__  :	int		 =			[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       UpperCAmelCase      (		self    :			int	, 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_    )

							if token_ids_a is not None:
										return ([0] * len(a_    )) + [1] + ([0] * len(a_    )) + [1, 1]
							return ([0] * len(a_    )) + [1, 1]
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	List[int]:



							'''simple docstring'''



							a__  :	List[str]		 =			[self.sep_token_id]
							a__  :	Tuple		 =			[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       UpperCAmelCase      (		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__  :	Optional[int]		 =			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__  :	int		 =			self.sp_model.serialized_model_proto()
													fi.write(a_    )

							return (out_vocab_file,)






				def       UpperCAmelCase      (		self    :			str	, *a_    :			Union[str, Any]	, **a_    :			Any    )	->	int:



							'''simple docstring'''



							a__  :	Optional[int]		 =			super()._decode(*a_	, **a_    )
							a__  :	Tuple		 =			text.replace(" "	, ""    ).replace("\u2582"	, " "    ).replace("\u2583"	, "\n"    )
							return text