
'''simple docstring'''







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

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


_lowercase				:						Union[str, Any]												= logging.get_logger(__name__)

_lowercase				:						Optional[int]												= {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase				:						int												= {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}






class 				lowerCAmelCase__     (						__lowerCamelCase  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=False       ,							__SCREAMING_SNAKE_CASE=True       ,							__SCREAMING_SNAKE_CASE=False       ,							__SCREAMING_SNAKE_CASE="<s>"       ,							__SCREAMING_SNAKE_CASE="</s>"       ,							__SCREAMING_SNAKE_CASE="<unk>"       ,							__SCREAMING_SNAKE_CASE="<sep>"       ,							__SCREAMING_SNAKE_CASE="<pad>"       ,							__SCREAMING_SNAKE_CASE="<cls>"       ,							__SCREAMING_SNAKE_CASE="<mask>"       ,							__SCREAMING_SNAKE_CASE=["<eop>", "<eod>"]       ,							**__SCREAMING_SNAKE_CASE       ,							):


										"""simple docstring"""





										lowercase_			:  str									= AddedToken(__SCREAMING_SNAKE_CASE       ,							lstrip=__SCREAMING_SNAKE_CASE       ,							rstrip=__SCREAMING_SNAKE_CASE	) if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) else mask_token

										super().__init__(
										    vocab_file=__SCREAMING_SNAKE_CASE       ,							tokenizer_file=__SCREAMING_SNAKE_CASE       ,							do_lower_case=__SCREAMING_SNAKE_CASE       ,							remove_space=__SCREAMING_SNAKE_CASE       ,							keep_accents=__SCREAMING_SNAKE_CASE       ,							bos_token=__SCREAMING_SNAKE_CASE       ,							eos_token=__SCREAMING_SNAKE_CASE       ,							unk_token=__SCREAMING_SNAKE_CASE       ,							sep_token=__SCREAMING_SNAKE_CASE       ,							pad_token=__SCREAMING_SNAKE_CASE       ,							cls_token=__SCREAMING_SNAKE_CASE       ,							mask_token=__SCREAMING_SNAKE_CASE       ,							additional_special_tokens=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE       ,							)

										lowercase_			:  Union[str, Any]									= 3
										lowercase_			:  List[str]									= do_lower_case
										lowercase_			:  Dict									= remove_space
										lowercase_			:  Any									= keep_accents
										lowercase_			:  Optional[Any]									= vocab_file
										lowercase_			:  int									= False if not self.vocab_file else True

										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.'''	)
										lowercase_			:  Dict									= jieba
										lowercase_			:  Tuple									= str.maketrans(''' \n'''       ,							'''\u2582\u2583'''	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None	):


										"""simple docstring"""





										lowercase_			:  Optional[Any]									= [self.sep_token_id]
										lowercase_			:  Dict									= [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 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None	):


										"""simple docstring"""





										lowercase_			:  Union[str, Any]									= [self.sep_token_id]
										lowercase_			:  Dict									= [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 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None	):


										"""simple docstring"""





										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(__SCREAMING_SNAKE_CASE	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
										lowercase_			:  Tuple									= os.path.join(
										    __SCREAMING_SNAKE_CASE       ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

										if os.path.abspath(self.vocab_file	) != os.path.abspath(__SCREAMING_SNAKE_CASE	):
															copyfile(self.vocab_file       ,							__SCREAMING_SNAKE_CASE	)

										return (out_vocab_file,)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										lowercase_			:  Optional[Any]									= [
										    ''' '''.join([x.translate(self.translator	) for x in self.jieba.cut(__SCREAMING_SNAKE_CASE       ,							cut_all=__SCREAMING_SNAKE_CASE	)]	)
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(__SCREAMING_SNAKE_CASE       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


										"""simple docstring"""





										lowercase_			:  Optional[int]									= super()._decode(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)
										lowercase_			:  Dict									= text.replace(''' '''       ,							''''''	).replace('''\u2582'''       ,							''' '''	).replace('''\u2583'''       ,							'''\n'''	)
										return text





'''simple docstring'''







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_lowercase				:						Tuple												= 3_2
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Accelerator   ,   __SCREAMING_SNAKE_CASE      :				int = 16							):



						"""simple docstring"""





						lowercase_			:  int									= AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						lowercase_			:  int									= load_dataset('''glue'''   ,   '''mrpc'''							)

						def tokenize_function(__SCREAMING_SNAKE_CASE      :				Optional[int]							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_			:  Union[str, Any]									= tokenizer(examples['''sentence1''']   ,   examples['''sentence2''']   ,   truncation=__SCREAMING_SNAKE_CASE   ,   max_length=__SCREAMING_SNAKE_CASE							)
												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_			:  Optional[Any]									= datasets.map(
												    __SCREAMING_SNAKE_CASE   ,   batched=__SCREAMING_SNAKE_CASE   ,   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_			:  Any									= tokenized_datasets.rename_column('''label'''   ,   '''labels'''							)

						def collate_fn(__SCREAMING_SNAKE_CASE      :				List[Any]							):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase_			:  List[str]									= 16
												elif accelerator.mixed_precision != "no":
																		lowercase_			:  List[Any]									= 8
												else:
																		lowercase_			:  List[Any]									= None

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

						# Instantiate dataloaders.
						lowercase_			:  List[str]									= DataLoader(
						    tokenized_datasets['''train''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_SNAKE_CASE							)
						lowercase_			:  int									= DataLoader(
						    tokenized_datasets['''validation''']   ,   shuffle=__SCREAMING_SNAKE_CASE   ,   collate_fn=__SCREAMING_SNAKE_CASE   ,   batch_size=__SCREAMING_SNAKE_CASE							)

						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

							_lowercase				:						Tuple												= mocked_dataloaders  # noqa: F811
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,   __SCREAMING_SNAKE_CASE							) == "1":
												lowercase_			:  Dict									= 2
						# Initialize accelerator
						lowercase_			:  Any									= Accelerator(cpu=args.cpu   ,   mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_			:  Optional[Any]									= config['''lr''']
						lowercase_			:  Optional[Any]									= int(config['''num_epochs''']							)
						lowercase_			:  str									= int(config['''seed''']							)
						lowercase_			:  Optional[Any]									= int(config['''batch_size''']							)

						lowercase_			:  int									= evaluate.load('''glue'''   ,   '''mrpc'''							)

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(__SCREAMING_SNAKE_CASE							)							)
						def inner_training_loop(__SCREAMING_SNAKE_CASE      :				Any							):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												lowercase_			:  Union[str, Any]									= observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												lowercase_			:  Tuple									= num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(__SCREAMING_SNAKE_CASE							)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase_			:  Dict									= AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,   return_dict=__SCREAMING_SNAKE_CASE							)

												# 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_			:  Optional[int]									= model.to(accelerator.device							)

												# Instantiate optimizer
												lowercase_			:  int									= AdamW(params=model.parameters()   ,   lr=__SCREAMING_SNAKE_CASE							)
												lowercase_		,				lowercase_			:  List[str]									= get_dataloaders(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Instantiate scheduler
												lowercase_			:  str									= get_linear_schedule_with_warmup(
												    optimizer=__SCREAMING_SNAKE_CASE   ,   num_warmup_steps=100   ,   num_training_steps=(len(__SCREAMING_SNAKE_CASE							) * 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_			:  Any									= accelerator.prepare(
												    __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)

												# Now we train the model
												for epoch in range(__SCREAMING_SNAKE_CASE							):
																		model.train()
																		for step, batch in enumerate(__SCREAMING_SNAKE_CASE							):
																								# And perform gradient accumulation
																								with accelerator.accumulate(__SCREAMING_SNAKE_CASE							):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device							)
																														lowercase_			:  List[str]									= model(**__SCREAMING_SNAKE_CASE							)
																														lowercase_			:  Any									= outputs.loss
																														accelerator.backward(__SCREAMING_SNAKE_CASE							)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

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

																		lowercase_			:  Union[str, Any]									= metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F'''epoch {epoch}:'''   ,   __SCREAMING_SNAKE_CASE							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()
def       snake_case_	(       ):



						"""simple docstring"""





						lowercase_			:  int									= argparse.ArgumentParser(description='''Simple example of training script.'''							)
						parser.add_argument(
						    '''--mixed_precision'''   ,   type=__SCREAMING_SNAKE_CASE   ,   default=__SCREAMING_SNAKE_CASE   ,   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.'''							)
						lowercase_			:  int									= parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						lowercase_			:  Union[str, Any]									= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
						training_function(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							main()



