




from random import randint
from tempfile import TemporaryFile

import numpy as np

def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      int				)   ->					Dict:
      SCREAMING_SNAKE_CASE_      		=     0
      if start < end:
            SCREAMING_SNAKE_CASE_      		=     randint(__UpperCAmelCase     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     a[end]
            SCREAMING_SNAKE_CASE_      		=     a[pivot]
            SCREAMING_SNAKE_CASE_      		=     temp

            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     _in_place_partition(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase				)
            count += _in_place_quick_sort(__UpperCAmelCase     ,     __UpperCAmelCase     ,     p - 1				)
            count += _in_place_quick_sort(__UpperCAmelCase     ,     p + 1     ,     __UpperCAmelCase				)
      return count

def    UpperCAmelCase_				( __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Optional[int]				)   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     randint(__UpperCAmelCase     ,     __UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     a[end]
      SCREAMING_SNAKE_CASE_      		=     a[pivot]
      SCREAMING_SNAKE_CASE_      		=     temp
      SCREAMING_SNAKE_CASE_      		=     start - 1
      for index in range(__UpperCAmelCase     ,     __UpperCAmelCase				):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  SCREAMING_SNAKE_CASE_      		=     new_pivot_index + 1
                  SCREAMING_SNAKE_CASE_      		=     a[new_pivot_index]
                  SCREAMING_SNAKE_CASE_      		=     a[index]
                  SCREAMING_SNAKE_CASE_      		=     temp

      SCREAMING_SNAKE_CASE_      		=     a[new_pivot_index + 1]
      SCREAMING_SNAKE_CASE_      		=     a[end]
      SCREAMING_SNAKE_CASE_      		=     temp
      return new_pivot_index + 1, count


lowerCamelCase__					:  List[Any]      							=				TemporaryFile()
lowerCamelCase__					:  Optional[Any]      							=				100  # 1000 elements are to be sorted


lowerCamelCase__							,				lowerCamelCase__					:  List[str]      							=				0, 1  # mean and standard deviation
lowerCamelCase__					:  Tuple      							=				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
lowerCamelCase__					:  Union[str, Any]      							=				np.load(outfile)
lowerCamelCase__					:  Union[str, Any]      							=				len(M) - 1
lowerCamelCase__					:  Dict      							=				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)





print(z)





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

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

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

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

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

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # 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(__UpperCAmelCase     ,     padding='max_length'     ,     max_length=1_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader