
"""simple docstring"""






import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class 				lowercase__(				__lowercase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :str					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= dataset
			SCREAMING_SNAKE_CASE       :     List[str]				= process
			SCREAMING_SNAKE_CASE       :     List[Any]				= params





	def __len__(						self    :str					)							->			List[str]:







			'''simple docstring'''




			return len(self.dataset					)







	def __getitem__(						self    :Tuple		, lowerCamelCase_    :Union[str, Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= self.dataset[i]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.process(lowerCamelCase_		, **self.params					)
			return processed





class 				lowercase__(				__lowercase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Dict=None					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= loader
			SCREAMING_SNAKE_CASE       :     Optional[int]				= infer
			SCREAMING_SNAKE_CASE       :     Any				= params
			if loader_batch_size == 1:
					# Let's spare some time by deactivating altogether
					SCREAMING_SNAKE_CASE       :     Optional[int]				= None
			SCREAMING_SNAKE_CASE       :     Any				= loader_batch_size

			# Internal bookkeeping
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= None
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= None





	def __len__(						self    :int					)							->			str:







			'''simple docstring'''




			return len(self.loader					)





	def __iter__(						self    :List[Any]					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= iter(self.loader					)
			return self





	def   __lowerCAmelCase (						self    :str					)							->			List[str]:







			'''simple docstring'''




			if isinstance(self._loader_batch_data		, torch.Tensor					):
					# Batch data is simple tensor, just fetch the slice
					SCREAMING_SNAKE_CASE       :     List[Any]				= self._loader_batch_data[self._loader_batch_index]
			else:
					# Batch data is assumed to be BaseModelOutput (or dict)
					SCREAMING_SNAKE_CASE       :     Tuple				= {}
					for k, element in self._loader_batch_data.items():
							if isinstance(lowerCamelCase_		, lowerCamelCase_					):
									# Convert ModelOutput to tuple first
									SCREAMING_SNAKE_CASE       :     List[str]				= element.to_tuple()
									if isinstance(element[0]		, torch.Tensor					):
											SCREAMING_SNAKE_CASE       :     Optional[int]				= tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
									elif isinstance(element[0]		, np.ndarray					):
											SCREAMING_SNAKE_CASE       :     str				= tuple(np.expand_dims(el[self._loader_batch_index]		, 0					) for el in element					)
									continue
							if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowerCamelCase_		, lowerCamelCase_					):
									# Those are stored as lists of tensors so need specific unbatching.
									if isinstance(element[0]		, torch.Tensor					):
											SCREAMING_SNAKE_CASE       :     Optional[Any]				= tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
									elif isinstance(element[0]		, np.ndarray					):
											SCREAMING_SNAKE_CASE       :     int				= tuple(np.expand_dims(el[self._loader_batch_index]		, 0					) for el in element					)
									continue
							if element is None:
									# This can happen for optional data that get passed around
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= None
							elif isinstance(element[self._loader_batch_index]		, torch.Tensor					):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers

									SCREAMING_SNAKE_CASE       :     int				= element[self._loader_batch_index].unsqueeze(0					)
							elif isinstance(element[self._loader_batch_index]		, np.ndarray					):
									# Take correct batch data, but make it looked like batch_size=1
									# For compatibility with other methods within transformers
									SCREAMING_SNAKE_CASE       :     Tuple				= np.expand_dims(element[self._loader_batch_index]		, 0					)
							else:
									# This is typically a list, so no need to `unsqueeze`.
									SCREAMING_SNAKE_CASE       :     str				= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
					SCREAMING_SNAKE_CASE       :     Tuple				= self._loader_batch_data.__class__(lowerCamelCase_					)
			self._loader_batch_index += 1
			return result







	def   __lowerCAmelCase (						self    :Any					)							->			str:







			'''simple docstring'''




			if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
					# We are currently unrolling a batch so we just need to return
					# the current item within a batch
					return self.loader_batch_item()

			# We're out of items within a batch
			SCREAMING_SNAKE_CASE       :     Any				= next(self.iterator					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.infer(lowerCamelCase_		, **self.params					)
			# We now have a batch of "inferred things".
			if self.loader_batch_size is not None:
					# Try to infer the size of the batch
					if isinstance(lowerCamelCase_		, torch.Tensor					):
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= processed
					else:
							SCREAMING_SNAKE_CASE       :     Tuple				= list(processed.keys()					)[0]
							SCREAMING_SNAKE_CASE       :     List[str]				= processed[key]
					if isinstance(lowerCamelCase_		, lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     str				= len(lowerCamelCase_					)
					else:
							SCREAMING_SNAKE_CASE       :     Any				= first_tensor.shape[0]
					if 0 < observed_batch_size < self.loader_batch_size:
							# could be last batch so we can't unroll as many
							# elements.
							SCREAMING_SNAKE_CASE       :     int				= observed_batch_size
					# Setting internal index to unwrap the batch
					SCREAMING_SNAKE_CASE       :     Tuple				= processed
					SCREAMING_SNAKE_CASE       :     List[Any]				= 0
					return self.loader_batch_item()
			else:
					# We're not unrolling batches
					return processed





class 				lowercase__(				__lowercase   ):
	'''simple docstring'''

	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict=None					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)





	def __iter__(						self    :Dict					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= iter(self.loader					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= None
			return self







	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			int:







			'''simple docstring'''




			if self.subiterator is None:
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.infer(next(self.iterator					)		, **self.params					)
			try:
					# Try to return next item
					SCREAMING_SNAKE_CASE       :     List[str]				= next(self.subiterator					)
			except StopIteration:
					# When a preprocess iterator ends, we can start lookig at the next item
					# ChunkIterator will keep feeding until ALL elements of iterator
					# all have created their subiterator and have been iterating against.
					#
					# Another way to look at it, is we're basically flattening lists of lists
					# into a single list, but with generators
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.infer(next(self.iterator					)		, **self.params					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= next(self.subiterator					)
			return processed





class 				lowercase__(				__lowercase   ):
	'''simple docstring'''

	def __iter__(						self    :Tuple					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= iter(self.loader					)
			return self







	def   __lowerCAmelCase (						self    :str					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= False
			SCREAMING_SNAKE_CASE       :     List[Any]				= []
			if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
					while self._loader_batch_index < self.loader_batch_size:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.loader_batch_item()
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= item.pop('''is_last'''					)
							accumulator.append(lowerCamelCase_					)
							if is_last:
									return accumulator

			while not is_last:
					SCREAMING_SNAKE_CASE       :     Any				= self.infer(next(self.iterator					)		, **self.params					)
					if self.loader_batch_size is not None:
							if isinstance(lowerCamelCase_		, torch.Tensor					):
									SCREAMING_SNAKE_CASE       :     str				= processed
							else:
									SCREAMING_SNAKE_CASE       :     Union[str, Any]				= list(processed.keys()					)[0]
									SCREAMING_SNAKE_CASE       :     Tuple				= processed[key]
							if isinstance(lowerCamelCase_		, lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     str				= len(lowerCamelCase_					)
							else:
									SCREAMING_SNAKE_CASE       :     Optional[int]				= first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
									# could be last batch so we can't unroll as many
									# elements.
									SCREAMING_SNAKE_CASE       :     Optional[int]				= observed_batch_size
							SCREAMING_SNAKE_CASE       :     Tuple				= processed
							SCREAMING_SNAKE_CASE       :     List[str]				= 0
							while self._loader_batch_index < self.loader_batch_size:
									SCREAMING_SNAKE_CASE       :     List[str]				= self.loader_batch_item()
									SCREAMING_SNAKE_CASE       :     str				= item.pop('''is_last'''					)
									accumulator.append(lowerCamelCase_					)
									if is_last:
											return accumulator
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= processed
							SCREAMING_SNAKE_CASE       :     List[Any]				= item.pop('''is_last'''					)
							accumulator.append(lowerCamelCase_					)
			return accumulator





class 				lowercase__(				__lowercase   ):
	'''simple docstring'''

	def __init__(						self    :Any		, lowerCamelCase_    :Dataset		, lowerCamelCase_    :str					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= dataset
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= key





	def __len__(						self    :Optional[int]					)							->			Dict:







			'''simple docstring'''




			return len(self.dataset					)







	def __getitem__(						self    :Dict		, lowerCamelCase_    :int					)							->			Union[str, Any]:







			'''simple docstring'''




			return self.dataset[i][self.key]





class 				lowercase__(				__lowercase   ):
	'''simple docstring'''

	def __init__(						self    :int		, lowerCamelCase_    :Dataset		, lowerCamelCase_    :str		, lowerCamelCase_    :str					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= dataset
			SCREAMING_SNAKE_CASE       :     Optional[int]				= keya
			SCREAMING_SNAKE_CASE       :     Dict				= keya





	def __len__(						self    :Tuple					)							->			Dict:







			'''simple docstring'''




			return len(self.dataset					)







	def __getitem__(						self    :str		, lowerCamelCase_    :Dict					)							->			List[Any]:







			'''simple docstring'''




			return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







"""simple docstring"""






def __A						(   a_ :							int     )->							list[int]:




		'''simple docstring'''






		if num <= 0:
				raise ValueError('''Input must be a positive integer'''     )

		SCREAMING_SNAKE_CASE       :     Optional[int]				= [True] * (num + 1)

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 2
		while p * p <= num:
				if primes[p]:
						for i in range(p * p		,		num + 1		,		a_     ):
								SCREAMING_SNAKE_CASE       :     Any				= False
				p += 1

		return [prime for prime in range(2		,		num + 1     ) if primes[prime]]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCamelCase__ :       str						     =			int(input("Enter a positive integer: ").strip())
					print(prime_sieve_eratosthenes(user_num))





