




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

from ..utils.generic import ModelOutput







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Tuple		,				A_   :    Any		,				A_   :    Tuple		,				A_   :    Tuple    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       dataset
       lowerCamelCase_							=       process
       lowerCamelCase_							=       params

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

       """simple docstring"""






       return len(self.dataset    )





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

       """simple docstring"""






       lowerCamelCase_							=       self.dataset[i]
       lowerCamelCase_							=       self.process(A_		,				**self.params    )
       return processed







class     A(		UpperCamelCase  ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_							=       loader
       lowerCamelCase_							=       infer
       lowerCamelCase_							=       params
       if loader_batch_size == 1:
             # Let's spare some time by deactivating altogether
             lowerCamelCase_							=       None
       lowerCamelCase_							=       loader_batch_size

       # Internal bookkeeping
       lowerCamelCase_							=       None
       lowerCamelCase_							=       None

 def __len__(							self   :    Any    )  ->      int:

       """simple docstring"""






       return len(self.loader    )

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

       """simple docstring"""






       lowerCamelCase_							=       iter(self.loader    )
       return self

 def 							a__					(							self   :    Optional[int]    )  ->      Optional[int]:

       """simple docstring"""






       if isinstance(self._loader_batch_data		,				torch.Tensor    ):
             # Batch data is simple tensor, just fetch the slice
             lowerCamelCase_							=       self._loader_batch_data[self._loader_batch_index]
       else:
             # Batch data is assumed to be BaseModelOutput (or dict)
             lowerCamelCase_							=       {}
             for k, element in self._loader_batch_data.items():
                   if isinstance(A_		,				A_    ):
                         # Convert ModelOutput to tuple first
                         lowerCamelCase_							=       element.to_tuple()
                         if isinstance(element[0]		,				torch.Tensor    ):
                               lowerCamelCase_							=       tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
                         elif isinstance(element[0]		,				np.ndarray    ):
                               lowerCamelCase_							=       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(A_		,				A_    ):
                         # Those are stored as lists of tensors so need specific unbatching.
                         if isinstance(element[0]		,				torch.Tensor    ):
                               lowerCamelCase_							=       tuple(el[self._loader_batch_index].unsqueeze(0    ) for el in element    )
                         elif isinstance(element[0]		,				np.ndarray    ):
                               lowerCamelCase_							=       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_							=       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_							=       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_							=       np.expand_dims(element[self._loader_batch_index]		,				0    )
                   else:
                         # This is typically a list, so no need to `unsqueeze`.
                         lowerCamelCase_							=       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
             lowerCamelCase_							=       self._loader_batch_data.__class__(A_    )
       self._loader_batch_index += 1
       return result





 def 							a__					(							self   :    Dict    )  ->      Any:

       """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
       lowerCamelCase_							=       next(self.iterator    )
       lowerCamelCase_							=       self.infer(A_		,				**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(A_		,				torch.Tensor    ):
                   lowerCamelCase_							=       processed
             else:
                   lowerCamelCase_							=       list(processed.keys()    )[0]
                   lowerCamelCase_							=       processed[key]
             if isinstance(A_		,				A_    ):
                   lowerCamelCase_							=       len(A_    )
             else:
                   lowerCamelCase_							=       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_							=       observed_batch_size
             # Setting internal index to unwrap the batch
             lowerCamelCase_							=       processed
             lowerCamelCase_							=       0
             return self.loader_batch_item()
       else:
             # We're not unrolling batches
             return processed







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Dict		,				A_   :    int		,				A_   :    Optional[int]		,				A_   :    Union[str, Any]		,				A_   :    Optional[int]=None    )  ->      Any:

       """simple docstring"""






       super().__init__(A_		,				A_		,				A_    )

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

       """simple docstring"""






       lowerCamelCase_							=       iter(self.loader    )
       lowerCamelCase_							=       None
       return self





 def 							a__					(							self   :    Optional[Any]    )  ->      Optional[Any]:

       """simple docstring"""






       if self.subiterator is None:
             lowerCamelCase_							=       self.infer(next(self.iterator    )		,				**self.params    )
       try:
             # Try to return next item
             lowerCamelCase_							=       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_							=       self.infer(next(self.iterator    )		,				**self.params    )
             lowerCamelCase_							=       next(self.subiterator    )
       return processed







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __iter__(							self   :    Any    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       iter(self.loader    )
       return self





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

       """simple docstring"""






       lowerCamelCase_							=       False
       lowerCamelCase_							=       []
       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_							=       self.loader_batch_item()
                   lowerCamelCase_							=       item.pop('is_last'    )
                   accumulator.append(A_    )
                   if is_last:
                         return accumulator

       while not is_last:
             lowerCamelCase_							=       self.infer(next(self.iterator    )		,				**self.params    )
             if self.loader_batch_size is not None:
                   if isinstance(A_		,				torch.Tensor    ):
                         lowerCamelCase_							=       processed
                   else:
                         lowerCamelCase_							=       list(processed.keys()    )[0]
                         lowerCamelCase_							=       processed[key]
                   if isinstance(A_		,				A_    ):
                         lowerCamelCase_							=       len(A_    )
                   else:
                         lowerCamelCase_							=       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_							=       observed_batch_size
                   lowerCamelCase_							=       processed
                   lowerCamelCase_							=       0
                   while self._loader_batch_index < self.loader_batch_size:
                         lowerCamelCase_							=       self.loader_batch_item()
                         lowerCamelCase_							=       item.pop('is_last'    )
                         accumulator.append(A_    )
                         if is_last:
                               return accumulator
             else:
                   lowerCamelCase_							=       processed
                   lowerCamelCase_							=       item.pop('is_last'    )
                   accumulator.append(A_    )
       return accumulator







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Tuple		,				A_   :    str		,				A_   :    List[Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       dataset
       lowerCamelCase_							=       key

 def __len__(							self   :    int    )  ->      Any:

       """simple docstring"""






       return len(self.dataset    )





 def __getitem__(							self   :    Tuple		,				A_   :    int    )  ->      int:

       """simple docstring"""






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







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    List[Any]		,				A_   :    Any		,				A_   :    Optional[Any]		,				A_   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       dataset
       lowerCamelCase_							=       keya
       lowerCamelCase_							=       keya

 def __len__(							self   :    Optional[Any]    )  ->      Optional[Any]:

       """simple docstring"""






       return len(self.dataset    )





 def __getitem__(							self   :    Dict		,				A_   :    List[Any]    )  ->      Tuple:

       """simple docstring"""






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







'''simple docstring'''






from __future__ import annotations

from fractions import Fraction





def lowerCamelCase__     (		__lowerCamelCase			:				int			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   return (
       num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
   )





def lowerCamelCase__     (		__lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[int]				       =[]
   _UpperCAmelCase  :  Union[str, Any]				       =1_1
   _UpperCAmelCase  :  Union[str, Any]				       =int('1' + '0' * digit_len				)
   for num in range(__lowerCamelCase			,       __lowerCamelCase				):
      while den <= 9_9:
         if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
            if is_digit_cancelling(__lowerCamelCase			,       __lowerCamelCase				):
               solutions.append(f"{num}/{den}"				)
         den += 1
      num += 1
      _UpperCAmelCase  :  Optional[int]				       =1_0
   return solutions





def lowerCamelCase__     (		__lowerCamelCase			:				int = 2				):







   '''simple docstring'''




   _UpperCAmelCase  :  Any				       =1.0
   for fraction in fraction_list(__lowerCamelCase				):
      _UpperCAmelCase  :  List[str]				       =Fraction(__lowerCamelCase				)
      result *= frac.denominator / frac.numerator
   return int(__lowerCamelCase				)


if __name__ == "__main__":
 print(solution())

