






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
#
########################################################################


a_     :   Optional[Any]							=    16
a_     :   Dict							=    32


def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[int] = 16   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						AutoTokenizer.from_pretrained("bert-base-cased"   )
						lowerCAmelCase__       =						load_dataset("glue"    ,   "mrpc"   )

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

						def collate_fn(UpperCamelCase_					:				str   ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowerCAmelCase__       =						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":
																		lowerCAmelCase__       =						16
												elif accelerator.mixed_precision != "no":
																		lowerCAmelCase__       =						8
												else:
																		lowerCAmelCase__       =						None

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

						# Instantiate dataloaders.
						lowerCAmelCase__       =						DataLoader(
						    tokenized_datasets["train"]    ,   shuffle=__snake_case    ,   collate_fn=__snake_case    ,   batch_size=__snake_case   )
						lowerCAmelCase__       =						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

	a_     :   Optional[int]							=    mocked_dataloaders  # noqa: F811


def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				str   ) ->   int:
						"""simple docstring"""







						if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,   __snake_case   ) == "1":
												lowerCAmelCase__       =						2
						# New Code #
						lowerCAmelCase__       =						int(args.gradient_accumulation_steps   )
						# Initialize accelerator
						lowerCAmelCase__       =						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
						lowerCAmelCase__       =						config["lr"]
						lowerCAmelCase__       =						int(config["num_epochs"]   )
						lowerCAmelCase__       =						int(config["seed"]   )
						lowerCAmelCase__       =						int(config["batch_size"]   )

						lowerCAmelCase__       =						evaluate.load("glue"    ,   "mrpc"   )

						set_seed(__snake_case   )
						lowerCAmelCase__      ,							lowerCAmelCase__       =						get_dataloaders(__snake_case    ,   __snake_case   )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowerCAmelCase__       =						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).
						lowerCAmelCase__       =						model.to(accelerator.device   )

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

						# Instantiate scheduler
						lowerCAmelCase__       =						get_linear_schedule_with_warmup(
						    optimizer=__snake_case    ,   num_warmup_steps=100    ,   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.
						lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						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   ):
																								lowerCAmelCase__       =						model(**__snake_case   )
																								lowerCAmelCase__       =						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():
																								lowerCAmelCase__       =						model(**__snake_case   )
																		lowerCAmelCase__       =						outputs.logits.argmax(dim=-1   )
																		lowerCAmelCase__      ,							lowerCAmelCase__       =						accelerator.gather_for_metrics((predictions, batch["labels"])   )
																		metric.add_batch(
																		    predictions=__snake_case    ,   references=__snake_case    ,   )

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


def        _a  (     ) ->   str:
						"""simple docstring"""







						lowerCAmelCase__       =						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."   )
						lowerCAmelCase__       =						parser.parse_args()
						lowerCAmelCase__       =						{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(__snake_case    ,   __snake_case   )


if __name__ == "__main__":
	main()














def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						len(UpperCamelCase_   ) - 1

						while left <= right:
												# avoid divided by 0 during interpolation
												if sorted_collection[left] == sorted_collection[right]:
																		if sorted_collection[left] == item:
																								return left
																		else:
																								return None

												lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
												    sorted_collection[right] - sorted_collection[left]
												)

												# out of range check
												if point < 0 or point >= len(UpperCamelCase_   ):
																		return None

												lowerCAmelCase__       =						sorted_collection[point]
												if current_item == item:
																		return point
												else:
																		if point < left:
																								lowerCAmelCase__       =						left
																								lowerCAmelCase__       =						point
																		elif point > right:
																								lowerCAmelCase__       =						right
																								lowerCAmelCase__       =						point
																		else:
																								if item < current_item:
																														lowerCAmelCase__       =						point - 1
																								else:
																														lowerCAmelCase__       =						point + 1
						return None


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																		return left
												else:
																		return None

						lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(UpperCamelCase_   ):
												return None

						if sorted_collection[point] == item:
												return point
						elif point < left:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						elif point > right:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						else:
												if sorted_collection[point] > item:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   point - 1   )
												else:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   point + 1    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						if collection != sorted(UpperCamelCase_   ):
												raise ValueError("Collection must be ascending sorted"   )
						return True


if __name__ == "__main__":
	import sys
	a_							=    0
	if debug == 1:
		a_							=    [10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
		a_							=    67

	a_							=    interpolation_search(collection, target)
	if result is not None:
		print(F"{target} found at positions: {result}")
	else:
		print('''Not found''')






