
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
# and perform 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
#
########################################################################


lowercase__          =16
lowercase__          =32




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Dict = 1_6  ):






 """simple docstring"""




 __a			:  List[str]		    =  AutoTokenizer.from_pretrained('''bert-base-cased'''  )
 __a			:  str		    =  load_dataset('''glue'''  ,       '''mrpc'''  )

 def tokenize_function(lowerCAmelCase__							:    List[str]  ):
  # max_length=None => use the model max length (it's actually the default)
  __a			:  Tuple		    =  tokenizer(examples['''sentence1''']  ,       examples['''sentence2''']  ,       truncation=__snake_case  ,       max_length=__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():
  __a			:  Optional[Any]		    =  datasets.map(
      __snake_case  ,       batched=__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
 __a			:  Dict		    =  tokenized_datasets.rename_column('''label'''  ,       '''labels'''  )

 def collate_fn(lowerCAmelCase__							:    Union[str, Any]  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __a			:  Optional[Any]		    =  1_2_8 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[Any]		    =  1_6
  elif accelerator.mixed_precision != "no":
   __a			:  Any		    =  8
  else:
   __a			:  List[str]		    =  None

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

 # Instantiate dataloaders.
 __a			:  List[Any]		    =  DataLoader(
     tokenized_datasets['''train''']  ,       shuffle=__snake_case  ,       collate_fn=__snake_case  ,       batch_size=__snake_case  )
 __a			:  Optional[int]		    =  DataLoader(
     tokenized_datasets['''validation''']  ,       shuffle=__snake_case  ,       collate_fn=__snake_case  ,       batch_size=__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__          =mocked_dataloaders  # noqa: F811




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Tuple  ):






 """simple docstring"""




 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,       __snake_case  ) == "1":
  __a			:  Optional[Any]		    =  2
 # New Code #
 __a			:  Union[str, Any]		    =  int(args.gradient_accumulation_steps  )
 # Initialize accelerator
 __a			:  str		    =  Accelerator(
     cpu=args.cpu  ,       mixed_precision=args.mixed_precision  ,       gradient_accumulation_steps=__snake_case  )
 if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
  raise NotImplementedError(
      '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''  )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __a			:  str		    =  config["lr"]
 __a			:  Dict		    =  int(config['''num_epochs''']  )
 __a			:  List[Any]		    =  int(config['''seed''']  )
 __a			:  Tuple		    =  int(config['''batch_size''']  )

 __a			:  List[Any]		    =  evaluate.load('''glue'''  ,       '''mrpc'''  )

 set_seed(__snake_case  )
 __a			:  Dict		    =  get_dataloaders(__snake_case  ,       __snake_case  )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __a			:  List[Any]		    =  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,       return_dict=__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).
 __a			:  Optional[Any]		    =  model.to(accelerator.device  )

 # Instantiate optimizer
 __a			:  int		    =  AdamW(params=model.parameters()  ,       lr=__snake_case  )

 # Instantiate scheduler
 __a			:  Optional[int]		    =  get_linear_schedule_with_warmup(
     optimizer=__snake_case  ,       num_warmup_steps=1_0_0  ,       num_training_steps=(len(__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.
 __a			:  Any		    =  accelerator.prepare(
     __snake_case  ,       __snake_case  ,       __snake_case  ,       __snake_case  ,       __snake_case  )

 # Now we train the model
 for epoch in range(__snake_case  ):
  model.train()
  for step, batch in enumerate(__snake_case  ):
   # 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(__snake_case  ):
    __a			:  Optional[Any]		    =  model(**__snake_case  )
    __a			:  Dict		    =  output.loss
    accelerator.backward(__snake_case  )
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

  __a			:  Optional[int]		    =  metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"epoch {epoch}:"  ,       __snake_case  )




def        __UpperCamelCase					(    ):






 """simple docstring"""




 __a			:  Dict		    =  argparse.ArgumentParser(description='''Simple example of training script.'''  )
 parser.add_argument(
     '''--mixed_precision'''  ,       type=__snake_case  ,       default=__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.'''  ,       )
 # New Code #
 parser.add_argument(
     '''--gradient_accumulation_steps'''  ,       type=__snake_case  ,       default=1  ,       help='''The number of minibatches to be ran before gradients are accumulated.'''  ,       )
 parser.add_argument('''--cpu'''  ,       action='''store_true'''  ,       help='''If passed, will train on the CPU.'''  )
 __a			:  List[str]		    =  parser.parse_args()
 __a			:  List[Any]		    =  {"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
 training_function(__snake_case  ,       __snake_case  )


if __name__ == "__main__":
      main()


from math import pi, sqrt




def        __UpperCamelCase					(    lowerCAmelCase__							:    float  ):
 if num <= 0:
  raise ValueError('''math domain error'''  )
 if num > 1_71.5:
  raise OverflowError('''math range error'''  )
 elif num - int(lowerCAmelCase__  ) not in (0, 0.5):
  raise NotImplementedError('''num must be an integer or a half-integer'''  )
 elif num == 0.5:
  return sqrt(lowerCAmelCase__  )
 else:
  return 1.0 if num == 1 else (num - 1) * gamma(num - 1  )




def        __UpperCamelCase					(    ):
 assert gamma(0.5  ) == sqrt(lowerCAmelCase__  )
 assert gamma(1  ) == 1.0
 assert gamma(2  ) == 1.0


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      lowercase__          =1.0
      while num:
            lowercase__          =float(input('Gamma of: '))
            print(F"""gamma({num}) = {gamma(num)}""")
            print('\nEnter 0 to exit...')
