



'''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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


UpperCamelCase__:   Optional[Any]								=			16
UpperCamelCase__:   Tuple								=			32


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Tuple = 16  )      ->   Union[str, Any]:
							UpperCAmelCase :       List[Any] 							=		AutoTokenizer.from_pretrained('''bert-base-cased'''  )
							UpperCAmelCase :       int 							=		load_dataset('''glue'''   ,      '''mrpc'''  )

							def tokenize_function(_lowerCAmelCase  :						Optional[int]  ):
														# max_length=None => use the model max length (it's actually the default)
														UpperCAmelCase :       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():
														UpperCAmelCase :       List[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
							UpperCAmelCase :       List[str] 							=		tokenized_datasets.rename_column('''label'''   ,      '''labels'''  )

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

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

							# Instantiate dataloaders.
							UpperCAmelCase :       Union[str, Any] 							=		DataLoader(
							    tokenized_datasets['''train''']   ,      shuffle=_SCREAMING_SNAKE_CASE   ,      collate_fn=_SCREAMING_SNAKE_CASE   ,      batch_size=_SCREAMING_SNAKE_CASE  )
							UpperCAmelCase :       Optional[Any] 							=		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

						UpperCamelCase__:   Any								=			mocked_dataloaders  # noqa: F811


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   Union[str, Any]:


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

							UpperCAmelCase :       str 							=		evaluate.load('''glue'''   ,      '''mrpc'''  )

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=_SCREAMING_SNAKE_CASE  )
							def inner_training_loop(_lowerCAmelCase  :						Optional[Any]  ):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(_SCREAMING_SNAKE_CASE  )

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

														# Instantiate optimizer
														UpperCAmelCase :       Optional[int] 							=		AdamW(params=model.parameters()   ,      lr=_SCREAMING_SNAKE_CASE  )
														UpperCAmelCase							,		UpperCAmelCase :       List[Any] 							=		get_dataloaders(_SCREAMING_SNAKE_CASE   ,      _SCREAMING_SNAKE_CASE  )

														# Instantiate scheduler
														UpperCAmelCase :       Optional[Any] 							=		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.
														UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Union[str, 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  ):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device  )
																												UpperCAmelCase :       Tuple 							=		model(**_SCREAMING_SNAKE_CASE  )
																												UpperCAmelCase :       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():
																																			UpperCAmelCase :       int 							=		model(**_SCREAMING_SNAKE_CASE  )
																												UpperCAmelCase :       Optional[int] 							=		outputs.logits.argmax(dim=-1  )
																												UpperCAmelCase							,		UpperCAmelCase :       Optional[int] 							=		accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
																												metric.add_batch(
																												    predictions=_SCREAMING_SNAKE_CASE   ,      references=_SCREAMING_SNAKE_CASE   ,      )

																					UpperCAmelCase :       Dict 							=		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_     (					)      ->   Optional[Any]:
							UpperCAmelCase :       Tuple 							=		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.'''  )
							UpperCAmelCase :       int 							=		parser.parse_args()
							UpperCAmelCase :       Dict 							=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(_SCREAMING_SNAKE_CASE   ,      _SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
						main()












import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


      return field(default_factory=lambda: default ,						metadata=_SCREAMING_SNAKE_CASE	)



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			list_field(
         default=[]							,      metadata={
             "help": (
                 "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                 " of all available models"
             )
         }							,      )

     __snake_case    :	List[int]			=			list_field(
         default=[8]							,      metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

     __snake_case    :	List[int]			=			list_field(
         default=[8, 32, 128, 512]							,      metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,      )

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                 " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                 " for debugging / testing and on TPU."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }							,      )






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




           '''simple docstring'''


           warnings.warn(
               F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           if len(self.models       ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""       )
           return self.models






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           if not self.multi_process:
                 return False
           elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""       )
                 return False
           else:
                 return True


