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

from accelerate.utils.dataclasses import DistributedType




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]=2    ,  _UpperCamelCase     :				Optional[int]=3    ,  _UpperCamelCase     :				int=64    ,  _UpperCamelCase     :				Any=None)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						np.random.default_rng(_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						length
          _lowerCamelCase     :		Union[str, Any] 						=						rng.normal(size=(length,)).astype(np.floataa)
          _lowerCamelCase     :		Tuple 						=						a * self.x + b + rng.normal(scale=0.1    ,  size=(length,)).astype(np.floataa)



      def __len__(					self     :				Dict)				->Any:


          """simple docstring"""


          return self.length




      def __getitem__(					self     :				List[Any]    ,  _UpperCamelCase     :				str)				->Optional[int]:


          """simple docstring"""


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





class 	__snake_case	( torch.nn.Module		):





      '''simple docstring'''






      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Tuple=0    ,  _UpperCamelCase     :				str=0    ,  _UpperCamelCase     :				Optional[Any]=False)				->Dict:


          """simple docstring"""


          super().__init__()
          _lowerCamelCase     :		Optional[int] 						=						torch.nn.Parameter(torch.tensor([2, 3]).float())
          _lowerCamelCase     :		Optional[int] 						=						torch.nn.Parameter(torch.tensor([2, 3]).float())
          _lowerCamelCase     :		List[Any] 						=						True




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any]=None)				->Optional[int]:


          """simple docstring"""


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





class 	__snake_case	( torch.nn.Module		):





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  _UpperCamelCase     :				Optional[int]=0    ,  _UpperCamelCase     :				Any=0    ,  _UpperCamelCase     :				Optional[Any]=False)				->Optional[int]:


          """simple docstring"""


          super().__init__()
          _lowerCamelCase     :		Optional[Any] 						=						torch.nn.Parameter(torch.tensor(_UpperCamelCase).float())
          _lowerCamelCase     :		int 						=						torch.nn.Parameter(torch.tensor(_UpperCamelCase).float())
          _lowerCamelCase     :		List[Any] 						=						True




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				int=None)				->List[str]:


          """simple docstring"""


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







def 						A__  (					__A   , __A = 16	):







    '''simple docstring'''
    from datasets import load_dataset
    from transformers import AutoTokenizer

    _lowerCamelCase     :		Optional[int] 						=						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
    _lowerCamelCase     :		List[str] 						=						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
    _lowerCamelCase     :		int 						=						load_dataset("""csv"""   , data_files=__A	)
    _lowerCamelCase     :		int 						=						datasets["""train"""].unique("""label"""	)

    _lowerCamelCase     :		int 						=						{v: i for i, v in enumerate(__A	)}

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

    def collate_fn(__A	):
        # 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(__A   , padding="""max_length"""   , max_length=128   , return_tensors="""pt"""	)
        return tokenizer.pad(__A   , padding="""longest"""   , return_tensors="""pt"""	)

    # Instantiate dataloaders.
    _lowerCamelCase     :		Optional[int] 						=						DataLoader(tokenized_datasets["""train"""]   , shuffle=__A   , collate_fn=__A   , batch_size=2	)
    _lowerCamelCase     :		Optional[Any] 						=						DataLoader(tokenized_datasets["""validation"""]   , shuffle=__A   , collate_fn=__A   , batch_size=1	)

    return train_dataloader, eval_dataloader





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







def 						A__  (					__A	):







    '''simple docstring'''
    if not sequence:
        return []
    if len(__A	) == 1:
        return list(__A	)
    _lowerCamelCase     :		int 						=						0
    _lowerCamelCase     :		Dict 						=						len(__A	) - 1
    _lowerCamelCase     :		str 						=						0
    while mid <= high:
        if sequence[mid] == colors[0]:
            _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            _lowerCamelCase						,							_lowerCamelCase     :		str 						=						sequence[high], sequence[mid]
            high -= 1
        else:
            _lowerCamelCase     :		int 						=						F"""The elements inside the sequence must contains only {colors} values"""
            raise ValueError(__A	)
    return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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




