"""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 checkpointing 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
#
########################################################################

__lowercase    =				16
__lowercase    =				32


def    lowerCAmelCase			(__UpperCamelCase							:    Accelerator							,					__UpperCamelCase							:    int = 1_6			):
							"""simple docstring"""


							__UpperCamelCase							=AutoTokenizer.from_pretrained('''bert-base-cased'''			)
							__UpperCamelCase							=load_dataset('''glue'''							,					'''mrpc'''			)

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

							def collate_fn(__UpperCamelCase							:    int			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__UpperCamelCase							=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":
																					__UpperCamelCase							=1_6
														elif accelerator.mixed_precision != "no":
																					__UpperCamelCase							=8
														else:
																					__UpperCamelCase							=None

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

							# Instantiate dataloaders.
							__UpperCamelCase							=DataLoader(
							    tokenized_datasets['''train''']							,					shuffle=__UpperCamelCase							,					collate_fn=__UpperCamelCase							,					batch_size=__UpperCamelCase			)
							__UpperCamelCase							=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

				__lowercase    =				mocked_dataloaders  # noqa: F811


def    lowerCAmelCase			(__UpperCamelCase							:    int							,					__UpperCamelCase							:    List[Any]			):
							"""simple docstring"""


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

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps							,					'''isdigit'''			):
														if args.checkpointing_steps == "epoch":
																					__UpperCamelCase							=args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					__UpperCamelCase							=int(args.checkpointing_steps			)
														else:
																					raise ValueError(
																					    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
							else:
														__UpperCamelCase							=None

							set_seed(__UpperCamelCase			)

							__UpperCamelCase	,      __UpperCamelCase							=get_dataloaders(__UpperCamelCase							,					__UpperCamelCase			)
							__UpperCamelCase							=evaluate.load('''glue'''							,					'''mrpc'''			)

							# If the batch size is too big we use gradient accumulation
							__UpperCamelCase							=1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														__UpperCamelCase							=batch_size // MAX_GPU_BATCH_SIZE
														__UpperCamelCase							=MAX_GPU_BATCH_SIZE

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

							# Instantiate optimizer
							__UpperCamelCase							=AdamW(params=model.parameters()							,					lr=__UpperCamelCase			)

							# Instantiate scheduler
							__UpperCamelCase							=get_linear_schedule_with_warmup(
							    optimizer=__UpperCamelCase							,					num_warmup_steps=1_0_0							,					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.
							__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=accelerator.prepare(
							    __UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase			)

							# New Code #
							# We need to keep track of how many total steps we have iterated over
							__UpperCamelCase							=0
							# We also need to keep track of the stating epoch so files are named properly
							__UpperCamelCase							=0

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							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			)
																					__UpperCamelCase							=os.path.basename(args.resume_from_checkpoint			)
														else:
																					# Get the most recent checkpoint
																					__UpperCamelCase							=[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
																					dirs.sort(key=os.path.getctime			)
																					__UpperCamelCase							=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														__UpperCamelCase							=os.path.splitext(__UpperCamelCase			)[0]

														if "epoch" in training_difference:
																					__UpperCamelCase							=int(training_difference.replace('''epoch_'''							,					''''''			)			) + 1
																					__UpperCamelCase							=None
														else:
																					__UpperCamelCase							=int(training_difference.replace('''step_'''							,					''''''			)			)
																					__UpperCamelCase							=resume_step // len(__UpperCamelCase			)
																					resume_step -= starting_epoch * len(__UpperCamelCase			)

    # Now we train the model
							for epoch in range(__UpperCamelCase							,					__UpperCamelCase			):
														model.train()
														# New Code #
														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
																					__UpperCamelCase							=accelerator.skip_first_batches(__UpperCamelCase							,					__UpperCamelCase			)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					__UpperCamelCase							=train_dataloader
														for step, batch in enumerate(__UpperCamelCase			):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device			)
																					__UpperCamelCase							=model(**__UpperCamelCase			)
																					__UpperCamelCase							=outputs.loss
																					__UpperCamelCase							=loss / gradient_accumulation_steps
																					accelerator.backward(__UpperCamelCase			)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(__UpperCamelCase							,					__UpperCamelCase			):
																												__UpperCamelCase							=F"""step_{overall_step}"""
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										__UpperCamelCase							=os.path.join(args.output_dir							,					__UpperCamelCase			)
																																			accelerator.save_state(__UpperCamelCase			)

														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():
																												__UpperCamelCase							=model(**__UpperCamelCase			)
																					__UpperCamelCase							=outputs.logits.argmax(dim=-1			)
																					__UpperCamelCase	,      __UpperCamelCase							=accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
																					metric.add_batch(
																					    predictions=__UpperCamelCase							,					references=__UpperCamelCase							,					)

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

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					__UpperCamelCase							=F"""epoch_{epoch}"""
																					if args.output_dir is not None:
																												__UpperCamelCase							=os.path.join(args.output_dir							,					__UpperCamelCase			)
																					accelerator.save_state(__UpperCamelCase			)


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=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(
							    '''--checkpointing_steps'''							,					type=__UpperCamelCase							,					default=__UpperCamelCase							,					help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''							,					)
							parser.add_argument(
							    '''--output_dir'''							,					type=__UpperCamelCase							,					default='''.'''							,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''							,					)
							parser.add_argument(
							    '''--resume_from_checkpoint'''							,					type=__UpperCamelCase							,					default=__UpperCamelCase							,					help='''If the training should continue from a checkpoint folder.'''							,					)
							__UpperCamelCase							=parser.parse_args()
							__UpperCamelCase							={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
							training_function(__UpperCamelCase							,					__UpperCamelCase			)


if __name__ == "__main__":
				main()




"""simple docstring"""

def    lowerCAmelCase			(__UpperCamelCase							:    list							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							for index, item in enumerate(__UpperCamelCase			):
														if item == target:
																					return index
							return -1


def    lowerCAmelCase			(__UpperCamelCase							:    list							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if not (0 <= high < len(__UpperCamelCase			) and 0 <= low < len(__UpperCamelCase			)):
														raise Exception('''Invalid upper or lower bound!'''			)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(__UpperCamelCase							,					low + 1							,					high - 1							,					__UpperCamelCase			)


if __name__ == "__main__":
				__lowercase    =				input('''Enter numbers separated by comma:\n''').strip()
				__lowercase    =				[int(item.strip()) for item in user_input.split(''',''')]

				__lowercase    =				int(input('''Enter a single number to be found in the list:\n''').strip())
				__lowercase    =				linear_search(sequence, target)
				if result != -1:
								print(f'''linear_search({sequence}, {target}) = {result}''')
				else:
								print(f'''{target} was not found in {sequence}''')



