
def    lowerCamelCase__				(				__lowerCAmelCase  :  list[int]    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	int(__lowerCAmelCase				)

			# Initialize Result
			lowerCAmelCase_						=	[]

			# Traverse through all denomination
			for denomination in reversed(__lowerCAmelCase				):
						# Find denominations
						while int(__lowerCAmelCase				) >= int(__lowerCAmelCase				):
									total_value -= int(__lowerCAmelCase				)
									answer.append(__lowerCAmelCase				)  # Append the "answers" array

			return answer


# Driver Code
if __name__ == "__main__":
			_A   =     []
			_A   =     "0"

			if (
			    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
			    == "y"
			):
						_A   =     int(input("Enter the number of denominations you want to add: ").strip())

						for i in range(0, n):
									denominations.append(int(input(f"""Denomination {i}: """).strip()))
						_A   =     input("Enter the change you want to make in Indian Currency: ").strip()
			else:
						# All denominations of Indian Currency if user does not enter
						_A   =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
						_A   =     input("Enter the change you want to make: ").strip()

			if int(value) == 0 or int(value) < 0:
						print("The total value cannot be zero or negative.")

			else:
						print(f"""Following is minimal change for {value}: """)
						_A   =     find_minimum_change(denominations, value)
						# Print result
						for i in range(len(answer)):
									print(answer[i], end=" ")





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



