



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

from accelerate.utils.dataclasses import DistributedType


class       _lowercase   :
				'''simple docstring'''







				def __init__( self     , snake_case__=2     , snake_case__=3     , snake_case__=64     , snake_case__=None    ):

									'''simple docstring'''


									UpperCamelCase_		  =   np.random.default_rng(snake_case__    )
									UpperCamelCase_		  =   length
									UpperCamelCase_		  =   rng.normal(size=(length,)    ).astype(np.floataa    )
									UpperCamelCase_		  =   a * self.x + b + rng.normal(scale=0.1     , size=(length,)    ).astype(np.floataa    )

				def __len__( self    ):

									'''simple docstring'''


									return self.length

				def __getitem__( self     , snake_case__    ):

									'''simple docstring'''


									return {"x": self.x[i], "y": self.y[i]}




class       _lowercase   (torch.nn.Module      ):
				'''simple docstring'''







				def __init__( self     , snake_case__=0     , snake_case__=0     , snake_case__=False    ):

									'''simple docstring'''


									super().__init__()
									UpperCamelCase_		  =   torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
									UpperCamelCase_		  =   torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
									UpperCamelCase_		  =   True

				def       _lowerCamelCase					( self     , snake_case__=None    ):

									'''simple docstring'''


									if self.first_batch:
														print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
														UpperCamelCase_		  =   False
									return x * self.a[0] + self.b[0]




class       _lowercase   (torch.nn.Module      ):
				'''simple docstring'''







				def __init__( self     , snake_case__=0     , snake_case__=0     , snake_case__=False    ):

									'''simple docstring'''


									super().__init__()
									UpperCamelCase_		  =   torch.nn.Parameter(torch.tensor(snake_case__    ).float()    )
									UpperCamelCase_		  =   torch.nn.Parameter(torch.tensor(snake_case__    ).float()    )
									UpperCamelCase_		  =   True

				def       _lowerCamelCase					( self     , snake_case__=None    ):

									'''simple docstring'''


									if self.first_batch:
														print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
														UpperCamelCase_		  =   False
									return x * self.a + self.b




def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase = 16):
					from datasets import load_dataset
					from transformers import AutoTokenizer

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

					UpperCamelCase_		  =   {v: i for i, v in enumerate(_lowerCAmelCase)}

					def tokenize_function(_lowerCAmelCase):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase_		  =   tokenizer(
										    examples["sentence1"]   ,	examples["sentence2"]   ,	truncation=_lowerCAmelCase   ,	max_length=_lowerCAmelCase   ,	padding="max_length")
										if "label" in examples:
															UpperCamelCase_		  =   [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
					UpperCamelCase_		  =   datasets.map(
					    _lowerCAmelCase   ,	batched=_lowerCAmelCase   ,	remove_columns=["sentence1", "sentence2", "label"]   ,	)

					def collate_fn(_lowerCAmelCase):
										# 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=1_28   ,	return_tensors="pt")
										return tokenizer.pad(_lowerCAmelCase   ,	padding="longest"   ,	return_tensors="pt")

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

					return train_dataloader, eval_dataloader





UpperCAmelCase   :							Any							 =0  # The first color of the flag.
UpperCAmelCase   :							Optional[int]							 =1  # The second color of the flag.
UpperCAmelCase   :							Optional[Any]							 =2  # The third color of the flag.
UpperCAmelCase   :							Union[str, Any]							 =(red, white, blue)



def    _lowerCAmelCase    (_lowerCAmelCase):
					if not sequence:
										return []
					if len(_lowerCAmelCase) == 1:
										return list(_lowerCAmelCase)
					UpperCamelCase_		  =   0
					UpperCamelCase_		  =   len(_lowerCAmelCase) - 1
					UpperCamelCase_		  =   0
					while mid <= high:
										if sequence[mid] == colors[0]:
															UpperCamelCase_  ,    UpperCamelCase_		  =   sequence[mid], sequence[low]
															low += 1
															mid += 1
										elif sequence[mid] == colors[1]:
															mid += 1
										elif sequence[mid] == colors[2]:
															UpperCamelCase_  ,    UpperCamelCase_		  =   sequence[high], sequence[mid]
															high -= 1
										else:
															UpperCamelCase_		  =   f"""The elements inside the sequence must contains only {colors} values"""
															raise ValueError(_lowerCAmelCase)
					return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	UpperCAmelCase   :							Tuple							 =input("""Enter numbers separated by commas:\n""").strip()
	UpperCAmelCase   :							Any							 =[int(item.strip()) for item in user_input.split(""",""")]
	print(F"{dutch_national_flag_sort(unsorted)}")
