



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


########################################################################
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__SCREAMING_SNAKE_CASE     =	16
__SCREAMING_SNAKE_CASE     =	32




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			# Initialize accelerator
			if args.with_tracking:
						A				:       Union[str, Any]														=    Accelerator(
						    cpu=args.cpu			,     mixed_precision=args.mixed_precision			,     log_with="all"			,     project_dir=args.project_dir     )
			else:
						A				:       int														=    Accelerator(cpu=args.cpu			,     mixed_precision=args.mixed_precision     )

			if hasattr(args.checkpointing_steps			,     "isdigit"     ):
						if args.checkpointing_steps == "epoch":
									A				:       Union[str, Any]														=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									A				:       List[str]														=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						A				:       Optional[int]														=    None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			A				:       Optional[int]														=    config["lr"]
			A				:       List[str]														=    int(config["num_epochs"]     )
			A				:       Union[str, Any]														=    int(config["seed"]     )
			A				:       int														=    int(config["batch_size"]     )

			# We need to initialize the trackers we use, and also store our configuration
			if args.with_tracking:
						A				:       Union[str, Any]														=    os.path.split(_lowerCamelCase     )[-1].split("."     )[0]
						accelerator.init_trackers(_lowerCamelCase			,     _lowerCamelCase     )

			A				:       Union[str, Any]														=    AutoTokenizer.from_pretrained("bert-base-cased"     )
			A				:       int														=    load_dataset("glue"			,     "mrpc"     )
			A				:       List[Any]														=    evaluate.load("glue"			,     "mrpc"     )

			def tokenize_function(_lowerCamelCase     ):
						# max_length=None => use the model max length (it's actually the default)
						A				:       Dict														=    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():
						A				:       Tuple														=    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
			A				:       Tuple														=    tokenized_datasets.rename_column("label"			,     "labels"     )

			# If the batch size is too big we use gradient accumulation
			A				:       str														=    1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						A				:       str														=    batch_size // MAX_GPU_BATCH_SIZE
						A				:       Union[str, Any]														=    MAX_GPU_BATCH_SIZE

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

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

			# Instantiate dataloaders.
			A				:       List[Any]														=    DataLoader(
			    tokenized_datasets["train"]			,     shuffle=_lowerCamelCase			,     collate_fn=_lowerCamelCase			,     batch_size=_lowerCamelCase     )
			A				:       List[Any]														=    DataLoader(
			    tokenized_datasets["validation"]			,     shuffle=_lowerCamelCase			,     collate_fn=_lowerCamelCase			,     batch_size=_lowerCamelCase     )

			set_seed(_lowerCamelCase     )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A				:       Any														=    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).
			A				:       str														=    model.to(accelerator.device     )

			# Instantiate optimizer
			A				:       List[str]														=    AdamW(params=model.parameters()			,     lr=_lowerCamelCase     )

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

			# We need to keep track of how many total steps we have iterated over
			A				:       Optional[Any]														=    0
			# We also need to keep track of the stating epoch so files are named properly
			A				:       Union[str, Any]														=    0

			# Potentially load in the weights and states from a previous save
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""     )
									accelerator.load_state(args.resume_from_checkpoint     )
									A				:       int														=    os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									A				:       Union[str, Any]														=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									A				:       Optional[Any]														=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						A				:       int														=    os.path.splitext(_lowerCamelCase     )[0]

						if "epoch" in training_difference:
									A				:       Optional[Any]														=    int(training_difference.replace("epoch_"			,     ""     )     ) + 1
									A				:       str														=    None
						else:
									A				:       int														=    int(training_difference.replace("step_"			,     ""     )     )
									A				:       Dict														=    resume_step // len(_lowerCamelCase     )
									resume_step -= starting_epoch * len(_lowerCamelCase     )

    # Now we train the model
			for epoch in range(_lowerCamelCase			,     _lowerCamelCase     ):
						model.train()
						if args.with_tracking:
									A				:       Union[str, Any]														=    0
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									A				:       int														=    accelerator.skip_first_batches(_lowerCamelCase			,     _lowerCamelCase     )
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									A				:       Tuple														=    train_dataloader
						for step, batch in enumerate(_lowerCamelCase     ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device     )
									A				:       Tuple														=    model(**_lowerCamelCase     )
									A				:       Optional[Any]														=    outputs.loss
									A				:       List[str]														=    loss / gradient_accumulation_steps
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(_lowerCamelCase     )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(_lowerCamelCase			,     _lowerCamelCase     ):
												A				:       List[str]														=    f"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		A				:       Dict														=    os.path.join(args.output_dir			,     _lowerCamelCase     )
															accelerator.save_state(_lowerCamelCase     )

						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():
												A				:       Optional[int]														=    model(**_lowerCamelCase     )
									A				:       str														=    outputs.logits.argmax(dim=-1     )
									A							,			A				:       Optional[int]														=    accelerator.gather_for_metrics((predictions, batch["labels"])     )
									metric.add_batch(
									    predictions=_lowerCamelCase			,     references=_lowerCamelCase			,     )

						A				:       Tuple														=    metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""			,     _lowerCamelCase     )
						if args.with_tracking:
									accelerator.log(
									    {
									        "accuracy": eval_metric["accuracy"],
									        "f1": eval_metric["f1"],
									        "train_loss": total_loss.item() / len(_lowerCamelCase     ),
									        "epoch": epoch,
									    }			,     step=_lowerCamelCase			,     )

						if checkpointing_steps == "epoch":
									A				:       List[str]														=    f"""epoch_{epoch}"""
									if args.output_dir is not None:
												A				:       List[Any]														=    os.path.join(args.output_dir			,     _lowerCamelCase     )
									accelerator.save_state(_lowerCamelCase     )

			if args.with_tracking:
						accelerator.end_training()




def 						UpperCAmelCase (    ):
			A				:       str														=    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."			,     )
			parser.add_argument("--cpu"			,     action="store_true"			,     help="If passed, will train on the CPU."     )
			parser.add_argument(
			    "--checkpointing_steps"			,     type=_lowerCamelCase			,     default=_lowerCamelCase			,     help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."			,     )
			parser.add_argument(
			    "--resume_from_checkpoint"			,     type=_lowerCamelCase			,     default=_lowerCamelCase			,     help="If the training should continue from a checkpoint folder."			,     )
			parser.add_argument(
			    "--with_tracking"			,     action="store_true"			,     help="Whether to load in all available experiment trackers from the environment and use them for logging."			,     )
			parser.add_argument(
			    "--output_dir"			,     type=_lowerCamelCase			,     default="."			,     help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."			,     )
			parser.add_argument(
			    "--project_dir"			,     type=_lowerCamelCase			,     default="logs"			,     help="Location on where to store experiment tracking logs` and relevent project information"			,     )
			A				:       List[str]														=    parser.parse_args()
			A				:       Tuple														=    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(_lowerCamelCase			,     _lowerCamelCase     )


if __name__ == "__main__":
							main()




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

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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE     =	{
    """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_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      int=None						,						__lowerCamelCase   :      Optional[Any]=False						,						__lowerCamelCase   :      List[Any]=True						,						__lowerCamelCase   :      int=False						,						__lowerCamelCase   :      Optional[int]="<s>"						,						__lowerCamelCase   :      Union[str, Any]="</s>"						,						__lowerCamelCase   :      Dict="<unk>"						,						__lowerCamelCase   :      List[str]="<sep>"						,						__lowerCamelCase   :      Any="<pad>"						,						__lowerCamelCase   :      Optional[Any]="<cls>"						,						__lowerCamelCase   :      Union[str, Any]="<mask>"						,						__lowerCamelCase   :      List[str]=["<eop>", "<eod>"]						,						**__lowerCamelCase   :      Optional[Any]						,						)   ->					Optional[int]:
									A				:       Any														=    AddedToken(__lowerCamelCase						,						lstrip=__lowerCamelCase						,						rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase						,						__lowerCamelCase			) else mask_token

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

									A				:       str														=    3
									A				:       str														=    do_lower_case
									A				:       int														=    remove_space
									A				:       Optional[int]														=    keep_accents
									A				:       Tuple														=    vocab_file
									A				:       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."			)
									A				:       str														=    jieba
									A				:       Optional[Any]														=    str.maketrans(" \n"						,						"\u2582\u2583"			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Dict														=    [self.sep_token_id]
									A				:       Tuple														=    [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       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      List[int]						,						__lowerCamelCase   :      Optional[List[int]] = None			)   ->					List[int]:
									A				:       Tuple														=    [self.sep_token_id]
									A				:       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       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					Tuple[str]:
									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(__lowerCamelCase			):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
												return
									A				:       List[Any]														=    os.path.join(
									    __lowerCamelCase						,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

									return (out_vocab_file,)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Dict						,						*__lowerCamelCase   :      List[str]						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									A				:       Union[str, Any]														=    [
									    " ".join([x.translate(self.translator			) for x in self.jieba.cut(__lowerCamelCase						,						cut_all=__lowerCamelCase			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(__lowerCamelCase						,						*__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						*__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[int]			)   ->					Tuple:
									A				:       Optional[Any]														=    super()._decode(*__lowerCamelCase						,						**__lowerCamelCase			)
									A				:       Tuple														=    text.replace(" "						,						""			).replace("\u2582"						,						" "			).replace("\u2583"						,						"\n"			)
									return text