'''simple docstring'''




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

from ..utils.generic import ModelOutput






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		dataset
              __UpperCAmelCase  :				Optional[int]       				=		process
              __UpperCAmelCase  :				Dict       				=		params






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







       def __getitem__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.dataset[i]
              __UpperCAmelCase  :				Optional[Any]       				=		self.process(UpperCamelCase_				,     **self.params      )
              return processed






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=None      ):
              __UpperCAmelCase  :				Optional[Any]       				=		loader
              __UpperCAmelCase  :				Tuple       				=		infer
              __UpperCAmelCase  :				int       				=		params
              if loader_batch_size == 1:
                     # Let's spare some time by deactivating altogether
                     __UpperCAmelCase  :				Optional[int]       				=		None
              __UpperCAmelCase  :				List[str]       				=		loader_batch_size

              # Internal bookkeeping
              __UpperCAmelCase  :				Optional[Any]       				=		None
              __UpperCAmelCase  :				Any       				=		None






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






       def __iter__(      self      ):
              __UpperCAmelCase  :				Tuple       				=		iter(self.loader      )
              return self






       def 			_snake_case						(      self      ):
              if isinstance(self._loader_batch_data				,     torch.Tensor      ):
                     # Batch data is simple tensor, just fetch the slice
                     __UpperCAmelCase  :				Union[str, 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  :				int       				=		element.to_tuple()
                                   if isinstance(element[0]				,     torch.Tensor      ):
                                          __UpperCAmelCase  :				str       				=		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 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  :				Optional[int]       				=		tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
                                   elif isinstance(element[0]				,     np.ndarray      ):
                                          __UpperCAmelCase  :				List[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
                                   __UpperCAmelCase  :				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

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







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






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=None      ):
              super().__init__(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      )






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







       def 			_snake_case						(      self      ):
              if self.subiterator is None:
                     __UpperCAmelCase  :				Any       				=		self.infer(next(self.iterator      )				,     **self.params      )
              try:
                     # Try to return next item
                     __UpperCAmelCase  :				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
                     __UpperCAmelCase  :				List[str]       				=		self.infer(next(self.iterator      )				,     **self.params      )
                     __UpperCAmelCase  :				List[Any]       				=		next(self.subiterator      )
              return processed






class 		__A       (__magic_name__   ):






       def __iter__(      self      ):
              __UpperCAmelCase  :				Tuple       				=		iter(self.loader      )
              return self







       def 			_snake_case						(      self      ):
              # 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  :				Union[str, Any]       				=		False
              __UpperCAmelCase  :				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  :				int       				=		self.loader_batch_item()
                            __UpperCAmelCase  :				Optional[int]       				=		item.pop("is_last"      )
                            accumulator.append(UpperCamelCase_      )
                            if is_last:
                                   return accumulator

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






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		dataset
              __UpperCAmelCase  :				List[str]       				=		key






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







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






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		dataset
              __UpperCAmelCase  :				Union[str, Any]       				=		keya
              __UpperCAmelCase  :				Optional[Any]       				=		keya






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







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







'''simple docstring'''




from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		prime_factors(lowerCamelCase__						)
       if is_square_free(lowerCamelCase__						):
              return -1 if len(lowerCamelCase__						) % 2 else 1
       return 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()






