



"""simple docstring"""






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

from ..utils.generic import ModelOutput

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						Dict,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Dict,						lowerCamelCase							:						str  )->      Dict:
								lowerCamelCase__	:	Tuple				=dataset
								lowerCamelCase__	:	Optional[Any]				=process
								lowerCamelCase__	:	Any				=params



					def __len__( self							:						Any  )->      Optional[Any]:
								return len(self.dataset  )




					def __getitem__( self							:						str,						lowerCamelCase							:						List[str]  )->      List[Any]:
								lowerCamelCase__	:	str				=self.dataset[i]
								lowerCamelCase__	:	List[str]				=self.process(lowerCamelCase,						**self.params  )
								return processed





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						List[Any],						lowerCamelCase							:						Dict,						lowerCamelCase							:						str,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						Any=None  )->      int:
								lowerCamelCase__	:	str				=loader
								lowerCamelCase__	:	Union[str, Any]				=infer
								lowerCamelCase__	:	Optional[int]				=params
								if loader_batch_size == 1:
											# Let's spare some time by deactivating altogether
											lowerCamelCase__	:	int				=None
								lowerCamelCase__	:	Optional[Any]				=loader_batch_size

								# Internal bookkeeping
								lowerCamelCase__	:	Optional[Any]				=None
								lowerCamelCase__	:	str				=None



					def __len__( self							:						Optional[int]  )->      Tuple:
								return len(self.loader  )



					def __iter__( self							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Optional[int]				=iter(self.loader  )
								return self



					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								if isinstance(self._loader_batch_data,						torch.Tensor  ):
											# Batch data is simple tensor, just fetch the slice
											lowerCamelCase__	:	Dict				=self._loader_batch_data[self._loader_batch_index]
								else:
											# Batch data is assumed to be BaseModelOutput (or dict)
											lowerCamelCase__	:	Optional[int]				={}
											for k, element in self._loader_batch_data.items():
														if isinstance(lowerCamelCase,						lowerCamelCase  ):
																	# Convert ModelOutput to tuple first
																	lowerCamelCase__	:	Optional[int]				=element.to_tuple()
																	if isinstance(element[0],						torch.Tensor  ):
																				lowerCamelCase__	:	Any				=tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																	elif isinstance(element[0],						np.ndarray  ):
																				lowerCamelCase__	:	Union[str, Any]				=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  ):
																				lowerCamelCase__	:	List[str]				=tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																	elif isinstance(element[0],						np.ndarray  ):
																				lowerCamelCase__	:	Tuple				=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
																	lowerCamelCase__	:	List[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

																	lowerCamelCase__	:	str				=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
																	lowerCamelCase__	:	Union[str, Any]				=np.expand_dims(element[self._loader_batch_index],						0  )
														else:
																	# This is typically a list, so no need to `unsqueeze`.
																	lowerCamelCase__	:	Optional[int]				=element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
											lowerCamelCase__	:	Optional[int]				=self._loader_batch_data.__class__(lowerCamelCase  )
								self._loader_batch_index += 1
								return result




					def 	snake_case ( self							:						str  )->      int:
								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
								lowerCamelCase__	:	Union[str, Any]				=next(self.iterator  )
								lowerCamelCase__	:	List[str]				=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  ):
														lowerCamelCase__	:	Union[str, Any]				=processed
											else:
														lowerCamelCase__	:	Union[str, Any]				=list(processed.keys()  )[0]
														lowerCamelCase__	:	List[str]				=processed[key]
											if isinstance(lowerCamelCase,						lowerCamelCase  ):
														lowerCamelCase__	:	Dict				=len(lowerCamelCase  )
											else:
														lowerCamelCase__	:	Optional[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.
														lowerCamelCase__	:	List[Any]				=observed_batch_size
											# Setting internal index to unwrap the batch
											lowerCamelCase__	:	Any				=processed
											lowerCamelCase__	:	Any				=0
											return self.loader_batch_item()
								else:
											# We're not unrolling batches
											return processed





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						Optional[int],						lowerCamelCase							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						str,						lowerCamelCase							:						Optional[int]=None  )->      List[str]:
								super().__init__(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def __iter__( self							:						int  )->      int:
								lowerCamelCase__	:	List[Any]				=iter(self.loader  )
								lowerCamelCase__	:	Optional[int]				=None
								return self




					def 	snake_case ( self							:						List[Any]  )->      List[str]:
								if self.subiterator is None:
											lowerCamelCase__	:	List[str]				=self.infer(next(self.iterator  ),						**self.params  )
								try:
											# Try to return next item
											lowerCamelCase__	:	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
											lowerCamelCase__	:	int				=self.infer(next(self.iterator  ),						**self.params  )
											lowerCamelCase__	:	Union[str, Any]				=next(self.subiterator  )
								return processed





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __iter__( self							:						int  )->      Union[str, Any]:
								lowerCamelCase__	:	int				=iter(self.loader  )
								return self




					def 	snake_case ( self							:						List[Any]  )->      List[str]:
								# Extremely similar to PipelineIterator in its unpacking mechanism
								# BUT, we have an extra required item which is the presence of `is_last`
								# That is because everything is flattened by `PipelineChunkIterator` we
								# need to keep track of how to regroup here in the original `process`
								# boundaries so that `process` and `postprocess` see the same data.

								# This iterator accumulates items (possibly while unbatching) until it
								# its a `is_last` and then just passes it on to the caller.
								lowerCamelCase__	:	Dict				=False
								lowerCamelCase__	:	str				=[]
								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:
														lowerCamelCase__	:	Tuple				=self.loader_batch_item()
														lowerCamelCase__	:	List[Any]				=item.pop('''is_last'''  )
														accumulator.append(lowerCamelCase  )
														if is_last:
																	return accumulator

								while not is_last:
											lowerCamelCase__	:	Optional[int]				=self.infer(next(self.iterator  ),						**self.params  )
											if self.loader_batch_size is not None:
														if isinstance(lowerCamelCase,						torch.Tensor  ):
																	lowerCamelCase__	:	Optional[int]				=processed
														else:
																	lowerCamelCase__	:	Any				=list(processed.keys()  )[0]
																	lowerCamelCase__	:	Union[str, Any]				=processed[key]
														if isinstance(lowerCamelCase,						lowerCamelCase  ):
																	lowerCamelCase__	:	List[Any]				=len(lowerCamelCase  )
														else:
																	lowerCamelCase__	:	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.
																	lowerCamelCase__	:	Optional[int]				=observed_batch_size
														lowerCamelCase__	:	Optional[int]				=processed
														lowerCamelCase__	:	Dict				=0
														while self._loader_batch_index < self.loader_batch_size:
																	lowerCamelCase__	:	Tuple				=self.loader_batch_item()
																	lowerCamelCase__	:	str				=item.pop('''is_last'''  )
																	accumulator.append(lowerCamelCase  )
																	if is_last:
																				return accumulator
											else:
														lowerCamelCase__	:	str				=processed
														lowerCamelCase__	:	Dict				=item.pop('''is_last'''  )
														accumulator.append(lowerCamelCase  )
								return accumulator





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						Optional[Any],						lowerCamelCase							:						Dataset,						lowerCamelCase							:						str  )->      Union[str, Any]:
								lowerCamelCase__	:	int				=dataset
								lowerCamelCase__	:	Optional[Any]				=key



					def __len__( self							:						int  )->      Optional[Any]:
								return len(self.dataset  )




					def __getitem__( self							:						Tuple,						lowerCamelCase							:						List[str]  )->      List[str]:
								return self.dataset[i][self.key]





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					def __init__( self							:						List[str],						lowerCamelCase							:						Dataset,						lowerCamelCase							:						str,						lowerCamelCase							:						str  )->      str:
								lowerCamelCase__	:	str				=dataset
								lowerCamelCase__	:	Tuple				=keya
								lowerCamelCase__	:	Any				=keya



					def __len__( self							:						Optional[int]  )->      List[Any]:
								return len(self.dataset  )




					def __getitem__( self							:						Union[str, Any],						lowerCamelCase							:						Tuple  )->      Union[str, Any]:
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 4000000			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				=[]
			lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=0, 1
			while b <= n:
						if b % 2 == 0:
									even_fibs.append(__lowerCamelCase			)
						lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=b, a + b
			return sum(__lowerCamelCase			)


if __name__ == "__main__":
		print(f'{solution() = }')




