


'''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
#
########################################################################


__snake_case							      =     16
__snake_case							      =     32


def 			a      (			__a ,					__a = 16   )  ->	int:



					'''simple docstring'''






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

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

					def collate_fn(__a   ):
										# 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[int]												= 16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase__   :str												= 8
										else:
															UpperCamelCase__   :List[str]												= None

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

					# Instantiate dataloaders.
					UpperCamelCase__   :Any												= DataLoader(
					    tokenized_datasets['''train'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=__a   )
					UpperCamelCase__   :List[Any]												= DataLoader(
					    tokenized_datasets['''validation'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=__a   )

					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

		__snake_case							      =     mocked_dataloaders  # noqa: F811


def 			a      (			__a ,					__a   )  ->	Any:



					'''simple docstring'''






					if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,					__a   ) == "1":
										UpperCamelCase__   :Union[str, Any]												= 2
					# Initialize accelerator
					UpperCamelCase__   :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
					UpperCamelCase__   :List[Any]												= config['''lr''']
					UpperCamelCase__   :Optional[Any]												= int(config['''num_epochs''']   )
					UpperCamelCase__   :List[str]												= int(config['''seed''']   )
					UpperCamelCase__   :Any												= int(config['''batch_size''']   )

					UpperCamelCase__   :Dict												= 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=__a   )
					def inner_training_loop(__a   ):
										# 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(__a   )

										# 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=__a   )

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

										# Instantiate optimizer
										UpperCamelCase__   :str												= AdamW(params=model.parameters() ,					lr=__a   )
										UpperCamelCase__					,				UpperCamelCase__   :List[str]												= get_dataloaders(__a ,					__a   )

										# Instantiate scheduler
										UpperCamelCase__   :Optional[int]												= get_linear_schedule_with_warmup(
										    optimizer=__a ,					num_warmup_steps=100 ,					num_training_steps=(len(__a   ) * 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__   :Optional[int]												= accelerator.prepare(
										    __a ,					__a ,					__a ,					__a ,					__a   )

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

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

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

										# 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 			a      (			)  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= argparse.ArgumentParser(description='''Simple example of training script.'''   )
					parser.add_argument(
					    '''--mixed_precision''' ,					type=__a ,					default=__a ,					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__   :Dict												= parser.parse_args()
					UpperCamelCase__   :Tuple												= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a ,					__a   )


if __name__ == "__main__":
		main()
"""simple docstring"""


import math






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					2
  _UpperCAmelCase         =					int(math.sqrt(lowercase						)						)  # Size of every segment
  _UpperCAmelCase         =					[True] * (end + 1)
  _UpperCAmelCase         =					[]

  while start <= end:
    if temp[start] is True:
      in_prime.append(lowercase						)
      for i in range(start * start      ,end + 1      ,lowercase						):
        _UpperCAmelCase         =					False
    start += 1
  prime += in_prime

  _UpperCAmelCase         =					end + 1
  _UpperCAmelCase         =					min(2 * end      ,lowercase						)

  while low <= n:
    _UpperCAmelCase         =					[True] * (high - low + 1)
    for each in in_prime:
      _UpperCAmelCase         =					math.floor(low / each						) * each
      if t < low:
        t += each

      for j in range(lowercase      ,high + 1      ,lowercase						):
        _UpperCAmelCase         =					False

    for j in range(len(lowercase						)						):
      if temp[j] is True:
        prime.append(j + low						)

    _UpperCAmelCase         =					high + 1
    _UpperCAmelCase         =					min(high + end      ,lowercase						)

  return prime

print(sieve(1_0**6))
