'''simple docstring'''

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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

__a: str          =   16
__a: List[Any]          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Union[str, Any]  =					AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 lowercase__				:    List[str]  =					load_dataset('''glue'''		,     '''mrpc'''     )

 def tokenize_function(UpperCAmelCase     ):
  # max_length=None => use the model max length (it's actually the default)
  lowercase__				:    List[Any]  =					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():
  lowercase__				:    str  =					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
 lowercase__				:    Dict  =					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.
  lowercase__				:    List[str]  =					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":
   lowercase__				:    Optional[Any]  =					16
  elif accelerator.mixed_precision != "no":
   lowercase__				:    Optional[Any]  =					8
  else:
   lowercase__				:    Tuple  =					None

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

 # Instantiate dataloaders.
 lowercase__				:    Tuple  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=__UpperCamelCase		,     collate_fn=__UpperCamelCase		,     batch_size=__UpperCamelCase     )
 lowercase__				:    Optional[Any]  =					DataLoader(
     tokenized_datasets['''validation''']		,     shuffle=__UpperCamelCase		,     collate_fn=__UpperCamelCase		,     batch_size=__UpperCamelCase     )

 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

  __a: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     __UpperCamelCase     ) == "1":
  lowercase__				:    List[Any]  =					2
 # Initialize Accelerator

 # New Code #
 # We pass in "all" to `log_with` to grab all available trackers in the environment
 # Note: If using a custom `Tracker` class, should be passed in here such as:
 # >>> log_with = ["all", MyCustomTrackerClassInstance()]
 if args.with_tracking:
  lowercase__				:    Optional[int]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    List[str]  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    str  =					int(config['''num_epochs''']     )
 lowercase__				:    Any  =					int(config['''seed''']     )
 lowercase__				:    Union[str, Any]  =					int(config['''batch_size''']     )
 set_seed(__UpperCamelCase     )

 lowercase__ ,							lowercase__				:    Tuple  =					get_dataloaders(__UpperCamelCase		,     __UpperCamelCase     )
 lowercase__				:    Any  =					evaluate.load('''glue'''		,     '''mrpc'''     )

 # If the batch size is too big we use gradient accumulation
 lowercase__				:    Tuple  =					1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  lowercase__				:    Tuple  =					batch_size // MAX_GPU_BATCH_SIZE
  lowercase__				:    Optional[int]  =					MAX_GPU_BATCH_SIZE

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

 # Instantiate optimizer
 lowercase__				:    Union[str, Any]  =					AdamW(params=model.parameters()		,     lr=__UpperCamelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					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.
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					accelerator.prepare(
     __UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase		,     __UpperCamelCase     )

 # New Code #
 # We need to initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  lowercase__				:    Tuple  =					os.path.split(__UpperCamelCase     )[-1].split('''.'''     )[0]
  accelerator.init_trackers(__UpperCamelCase		,     __UpperCamelCase     )

 # Now we train the model
 for epoch in range(__UpperCamelCase     ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowercase__				:    Any  =					0
  for step, batch in enumerate(__UpperCamelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   lowercase__				:    Any  =					model(**__UpperCamelCase     )
   lowercase__				:    int  =					outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowercase__				:    Tuple  =					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` (the default).
   batch.to(accelerator.device     )
   with torch.no_grad():
    lowercase__				:    List[Any]  =					model(**__UpperCamelCase     )
   lowercase__				:    str  =					outputs.logits.argmax(dim=-1     )
   lowercase__ ,							lowercase__				:    List[Any]  =					accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
   metric.add_batch(
       predictions=__UpperCamelCase		,     references=__UpperCamelCase		,     )

  lowercase__				:    List[Any]  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     __UpperCamelCase     )

  # New Code #
  # To actually log, we call `Accelerator.log`
  # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': eval_metric['''accuracy'''],
           '''f1''': eval_metric['''f1'''],
           '''train_loss''': total_loss.item() / len(__UpperCamelCase     ),
           '''epoch''': epoch,
       }		,     step=__UpperCamelCase		,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
 if args.with_tracking:
  accelerator.end_training()
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					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.'''     )
 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(
     '''--project_dir'''		,     type=__UpperCamelCase		,     default='''logs'''		,     help='''Location on where to store experiment tracking logs` and relevent project information'''		,     )
 lowercase__				:    Tuple  =					parser.parse_args()
 lowercase__				:    Optional[int]  =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(__UpperCamelCase		,     __UpperCamelCase     )


if __name__ == "__main__":
  main()


import math
from datetime import datetime, timedelta


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     year % 1_9
      SCREAMING_SNAKE_CASE_      =     year % 4
      SCREAMING_SNAKE_CASE_      =     year % 7
      SCREAMING_SNAKE_CASE_      =     math.floor(year / 1_0_0 )
      SCREAMING_SNAKE_CASE_      =     math.floor((1_3 + 8 * leap_day_inhibits) / 2_5 )
      SCREAMING_SNAKE_CASE_      =     leap_day_inhibits / 4
      SCREAMING_SNAKE_CASE_      =     (
          1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
      ) % 3_0
      SCREAMING_SNAKE_CASE_      =     (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

      # days to be added to March 21
      SCREAMING_SNAKE_CASE_      =     (1_9 * metonic_cycle + secular_moon_shift) % 3_0

      # PHM -> Paschal Full Moon
      SCREAMING_SNAKE_CASE_      =     (
          2 * julian_leap_year
          + 4 * non_leap_year
          + 6 * days_to_add
          + century_starting_point
      ) % 7

      if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
            return datetime(__UpperCamelCase   ,					4   ,					1_9 )
      elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
            return datetime(__UpperCamelCase   ,					4   ,					1_8 )
      else:
            return datetime(__UpperCamelCase   ,					3   ,					2_2 ) + timedelta(
                days=int(days_to_add + days_from_phm_to_sunday ) )


if __name__ == "__main__":
      for year in (19_94, 20_00, 20_10, 20_21, 20_23):
            A   :     Dict       =				"will be" if year > datetime.now().year else "was"
            print(f"Easter in {year} {tense} {gauss_easter(year)}")


