






'''simple docstring'''



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

from ..utils.generic import ModelOutput
class 							__SCREAMING_SNAKE_CASE       (							_SCREAMING_SNAKE_CASE     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         __a				     =    dataset
         __a				     =    process
         __a				     =    params




      def __len__(  self      )							->int:



         '''simple docstring'''
         return len(self.dataset      )






      def __getitem__(  self ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         __a				     =    self.dataset[i]
         __a				     =    self.process(A_ ,   **self.params      )
         return processed







class 							__SCREAMING_SNAKE_CASE       (							_SCREAMING_SNAKE_CASE     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase=None      )							->Optional[Any]:



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

         # Internal bookkeeping
         __a				     =    None
         __a				     =    None




      def __len__(  self      )							->int:



         '''simple docstring'''
         return len(self.loader      )




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



         '''simple docstring'''
         __a				     =    iter(self.loader      )
         return self




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         if isinstance(self._loader_batch_data ,   torch.Tensor      ):
            # Batch data is simple tensor, just fetch the slice
            __a				     =    self._loader_batch_data[self._loader_batch_index]
         else:
            # Batch data is assumed to be BaseModelOutput (or dict)
            __a				     =    {}
            for k, element in self._loader_batch_data.items():
               if isinstance(A_ ,   A_      ):
                  # Convert ModelOutput to tuple first
                  __a				     =    element.to_tuple()
                  if isinstance(element[0] ,   torch.Tensor      ):
                     __a				     =    tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
                  elif isinstance(element[0] ,   np.ndarray      ):
                     __a				     =    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      ):
                     __a				     =    tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
                  elif isinstance(element[0] ,   np.ndarray      ):
                     __a				     =    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
                  __a				     =    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

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






      def  __UpperCamelCase							(  self      )							->Tuple:



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







class 							__SCREAMING_SNAKE_CASE       (							_SCREAMING_SNAKE_CASE     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase=None      )							->Optional[Any]:



         '''simple docstring'''
         super().__init__(A_ ,   A_ ,   A_      )




      def __iter__(  self      )							->str:



         '''simple docstring'''
         __a				     =    iter(self.loader      )
         __a				     =    None
         return self






      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         if self.subiterator is None:
            __a				     =    self.infer(next(self.iterator      ) ,   **self.params      )
         try:
            # Try to return next item
            __a				     =    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
            __a				     =    self.infer(next(self.iterator      ) ,   **self.params      )
            __a				     =    next(self.subiterator      )
         return processed







class 							__SCREAMING_SNAKE_CASE       (							_SCREAMING_SNAKE_CASE     ):




      def __iter__(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    iter(self.loader      )
         return self






      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    False
         __a				     =    []
         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:
               __a				     =    self.loader_batch_item()
               __a				     =    item.pop('is_last'      )
               accumulator.append(A_      )
               if is_last:
                  return accumulator

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







class 							__SCREAMING_SNAKE_CASE       (							_SCREAMING_SNAKE_CASE     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    dataset
         __a				     =    key




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



         '''simple docstring'''
         return len(self.dataset      )






      def __getitem__(  self ,   lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         return self.dataset[i][self.key]







class 							__SCREAMING_SNAKE_CASE       (							_SCREAMING_SNAKE_CASE     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         __a				     =    dataset
         __a				     =    keya
         __a				     =    keya




      def __len__(  self      )							->Any:



         '''simple docstring'''
         return len(self.dataset      )






      def __getitem__(  self ,   lowerCamelCase      )							->int:



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







'''simple docstring'''



from maths.prime_factors import prime_factors



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    f"""Input value of [number={number}] must be an integer"""
      raise TypeError(SCREAMING_SNAKE_CASE__    )
   if number < 1:
      raise ValueError('Input must be a positive integer'    )
   return -1 if len(prime_factors(SCREAMING_SNAKE_CASE__    )    ) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()