





'''simple docstring'''






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

from ..utils.generic import ModelOutput
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= dataset
			lowerCAmelCase  :     Optional[int]       						= process
			lowerCAmelCase  :     str       						= params



	def __len__( self						):
			return len(self.dataset						)





	def __getitem__( self		,   a_						):
			lowerCAmelCase  :     Tuple       						= self.dataset[i]
			lowerCAmelCase  :     Union[str, Any]       						= self.process(lowerCamelCase_		,   **self.params						)
			return processed






class 		lowerCamelCase	(   _A							):



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

			# Internal bookkeeping
			lowerCAmelCase  :     Tuple       						= None
			lowerCAmelCase  :     Union[str, Any]       						= None



	def __len__( self						):
			return len(self.loader						)



	def __iter__( self						):
			lowerCAmelCase  :     List[str]       						= iter(self.loader						)
			return self



	def 	_lowerCamelCase     ( self						):



			if isinstance(self._loader_batch_data		,   torch.Tensor						):
					# Batch data is simple tensor, just fetch the slice
					lowerCAmelCase  :     Optional[Any]       						= self._loader_batch_data[self._loader_batch_index]
			else:
					# Batch data is assumed to be BaseModelOutput (or dict)
					lowerCAmelCase  :     Tuple       						= {}
					for k, element in self._loader_batch_data.items():
							if isinstance(lowerCamelCase_		,   lowerCamelCase_						):
									# Convert ModelOutput to tuple first
									lowerCAmelCase  :     str       						= element.to_tuple()
									if isinstance(element[0]		,   torch.Tensor						):
											lowerCAmelCase  :     Dict       						= tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
									elif isinstance(element[0]		,   np.ndarray						):
											lowerCAmelCase  :     List[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  :     Tuple       						= tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
									elif isinstance(element[0]		,   np.ndarray						):
											lowerCAmelCase  :     str       						= 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  :     Optional[int]       						= 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  :     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
									lowerCAmelCase  :     int       						= np.expand_dims(element[self._loader_batch_index]		,   0						)
							else:
									# This is typically a list, so no need to `unsqueeze`.
									lowerCAmelCase  :     List[Any]       						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
					lowerCAmelCase  :     List[str]       						= self._loader_batch_data.__class__(lowerCamelCase_						)
			self._loader_batch_index += 1
			return result





	def 	_lowerCamelCase     ( self						):



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






class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_		,   a_=None						):
			super().__init__(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_						)



	def __iter__( self						):
			lowerCAmelCase  :     List[Any]       						= iter(self.loader						)
			lowerCAmelCase  :     Any       						= None
			return self





	def 	_lowerCamelCase     ( self						):



			if self.subiterator is None:
					lowerCAmelCase  :     Any       						= self.infer(next(self.iterator						)		,   **self.params						)
			try:
					# Try to return next item
					lowerCAmelCase  :     Tuple       						= 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  :     Dict       						= self.infer(next(self.iterator						)		,   **self.params						)
					lowerCAmelCase  :     List[Any]       						= next(self.subiterator						)
			return processed






class 		lowerCamelCase	(   _A							):



	def __iter__( self						):
			lowerCAmelCase  :     Any       						= iter(self.loader						)
			return self





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= False
			lowerCAmelCase  :     Tuple       						= []
			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  :     Union[str, Any]       						= self.loader_batch_item()
							lowerCAmelCase  :     List[Any]       						= item.pop("is_last"						)
							accumulator.append(lowerCamelCase_						)
							if is_last:
									return accumulator

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






class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     int       						= dataset
			lowerCAmelCase  :     int       						= key



	def __len__( self						):
			return len(self.dataset						)





	def __getitem__( self		,   a_						):
			return self.dataset[i][self.key]






class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Dict       						= dataset
			lowerCAmelCase  :     Union[str, Any]       						= keya
			lowerCAmelCase  :     str       						= keya



	def __len__( self						):
			return len(self.dataset						)





	def __getitem__( self		,   a_						):
			return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}




'''simple docstring'''

def __UpperCamelCase( _A       :					float	,      _A       :					list[float]     ):
   '''simple docstring'''
   if discount_rate < 0:
      raise ValueError('''Discount rate cannot be negative'''     )
   if not cash_flows:
      raise ValueError('''Cash flows list cannot be empty'''     )
   UpperCAmelCase__				:					str 					=	sum(
       cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_A     )     )
   return round(_A	,      ndigits=2     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




