
"""simple docstring"""




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

from ..utils.generic import ModelOutput







class   _lowercase						(       A_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Tuple 							=						dataset
          UpperCAmelCase__							: Optional[int] 							=						process
          UpperCAmelCase__							: Optional[int] 							=						params



     def __len__(      self  )->					str:
          return len(self.dataset  )



     def __getitem__(      self ,			__UpperCamelCase  )->					Tuple:
          UpperCAmelCase__							: Dict 							=						self.dataset[i]
          UpperCAmelCase__							: Tuple 							=						self.process(__UpperCamelCase ,			**self.params  )
          return processed







class   _lowercase						(       A_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None  )->					List[str]:
          UpperCAmelCase__							: List[str] 							=						loader
          UpperCAmelCase__							: Tuple 							=						infer
          UpperCAmelCase__							: str 							=						params
          if loader_batch_size == 1:
               # Let's spare some time by deactivating altogether
               UpperCAmelCase__							: Tuple 							=						None
          UpperCAmelCase__							: Tuple 							=						loader_batch_size

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



     def __len__(      self  )->					List[Any]:
          return len(self.loader  )



     def __iter__(      self  )->					int:
          UpperCAmelCase__							: Tuple 							=						iter(self.loader  )
          return self



     def  lowerCAmelCase__       (      self  )->					List[str]:
          if isinstance(self._loader_batch_data ,			torch.Tensor  ):
               # Batch data is simple tensor, just fetch the slice
               UpperCAmelCase__							: List[Any] 							=						self._loader_batch_data[self._loader_batch_index]
          else:
               # Batch data is assumed to be BaseModelOutput (or dict)
               UpperCAmelCase__							: Any 							=						{}
               for k, element in self._loader_batch_data.items():
                    if isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
                         # Convert ModelOutput to tuple first
                         UpperCAmelCase__							: Any 							=						element.to_tuple()
                         if isinstance(element[0] ,			torch.Tensor  ):
                              UpperCAmelCase__							: Dict 							=						tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
                         elif isinstance(element[0] ,			np.ndarray  ):
                              UpperCAmelCase__							: 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(__UpperCamelCase ,			__UpperCamelCase  ):
                         # Those are stored as lists of tensors so need specific unbatching.
                         if isinstance(element[0] ,			torch.Tensor  ):
                              UpperCAmelCase__							: List[Any] 							=						tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
                         elif isinstance(element[0] ,			np.ndarray  ):
                              UpperCAmelCase__							: Optional[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
                         UpperCAmelCase__							: 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

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



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







class   _lowercase						(       A_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None  )->					Optional[Any]:
          super().__init__(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __iter__(      self  )->					Dict:
          UpperCAmelCase__							: Optional[Any] 							=						iter(self.loader  )
          UpperCAmelCase__							: Dict 							=						None
          return self



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







class   _lowercase						(       A_				):


     '''simple docstring'''





     def __iter__(      self  )->					Any:
          UpperCAmelCase__							: int 							=						iter(self.loader  )
          return self



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          # 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.
          UpperCAmelCase__							: Optional[int] 							=						False
          UpperCAmelCase__							: Union[str, 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:
                    UpperCAmelCase__							: Dict 							=						self.loader_batch_item()
                    UpperCAmelCase__							: Union[str, Any] 							=						item.pop("is_last"  )
                    accumulator.append(__UpperCamelCase  )
                    if is_last:
                         return accumulator

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







class   _lowercase						(       A_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Any 							=						dataset
          UpperCAmelCase__							: Optional[Any] 							=						key



     def __len__(      self  )->					Dict:
          return len(self.dataset  )



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







class   _lowercase						(       A_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					int:
          UpperCAmelCase__							: int 							=						dataset
          UpperCAmelCase__							: Dict 							=						keya
          UpperCAmelCase__							: List[str] 							=						keya



     def __len__(      self  )->					Union[str, Any]:
          return len(self.dataset  )



     def __getitem__(      self ,			__UpperCamelCase  )->					Optional[Any]:
          return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





"""simple docstring"""




def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if a < 0:
          raise ValueError("Input value must be a positive integer"		)
     elif isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
          raise TypeError("Input value must be a 'int' type"		)
     return bin(lowerCAmelCase		).count("1"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



