






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		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  dataset
     UpperCamelCase_						=  process
     UpperCamelCase_						=  params




  def __len__(  self			)      ->			Optional[Any]:
     return len(self.dataset			)




  def __getitem__(  self		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  self.dataset[i]
     UpperCamelCase_						=  self.process(_UpperCAmelCase		,		**self.params			)
     return processed




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			Optional[int]:
     UpperCamelCase_						=  loader
     UpperCamelCase_						=  infer
     UpperCamelCase_						=  params
     if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        UpperCamelCase_						=  None
     UpperCamelCase_						=  loader_batch_size

     # Internal bookkeeping
     UpperCamelCase_						=  None
     UpperCamelCase_						=  None




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




  def __iter__(  self			)      ->			List[Any]:
     UpperCamelCase_						=  iter(self.loader			)
     return self




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




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     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_						=  next(self.iterator			)
     UpperCamelCase_						=  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_						=  processed
        else:
           UpperCamelCase_						=  list(processed.keys()			)[0]
           UpperCamelCase_						=  processed[key]
        if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
           UpperCamelCase_						=  len(_UpperCAmelCase			)
        else:
           UpperCamelCase_						=  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_						=  observed_batch_size
        # Setting internal index to unwrap the batch
        UpperCamelCase_						=  processed
        UpperCamelCase_						=  0
        return self.loader_batch_item()
     else:
        # We're not unrolling batches
        return processed




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			List[str]:
     super().__init__(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def __iter__(  self			)      ->			Tuple:
     UpperCamelCase_						=  iter(self.loader			)
     UpperCamelCase_						=  None
     return self




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     if self.subiterator is None:
        UpperCamelCase_						=  self.infer(next(self.iterator			)		,		**self.params			)
     try:
        # Try to return next item
        UpperCamelCase_						=  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_						=  self.infer(next(self.iterator			)		,		**self.params			)
        UpperCamelCase_						=  next(self.subiterator			)
     return processed




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





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




  def  _UpperCAmelCase  (  self			)      ->			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_						=  False
     UpperCamelCase_						=  []
     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_						=  self.loader_batch_item()
           UpperCamelCase_						=  item.pop('is_last'			)
           accumulator.append(_UpperCAmelCase			)
           if is_last:
              return accumulator

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




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  dataset
     UpperCamelCase_						=  key




  def __len__(  self			)      ->			Optional[int]:
     return len(self.dataset			)




  def __getitem__(  self		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     return self.dataset[i][self.key]




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  dataset
     UpperCamelCase_						=  keya
     UpperCamelCase_						=  keya




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




  def __getitem__(  self		,		_UpperCAmelCase			)      ->			List[str]:
     return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}











def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: int )	->			list:





				"""simple docstring"""


				__lowerCamelCase									=       word.split()

				def justify(UpperCamelCase__							: list					,	UpperCamelCase__							: int					,	UpperCamelCase__							: int ) -> str:
								__lowerCamelCase									=       max_width - width
								__lowerCamelCase									=       len(UpperCamelCase__ )
								if len(UpperCamelCase__ ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowerCamelCase									=       words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowerCamelCase									=       spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowerCamelCase									=       (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(UpperCamelCase__ ):
																num_spaces_between_words_list[i] += 1
												__lowerCamelCase									=       []
												for i in range(UpperCamelCase__ ):
																# add the word
																aligned_words_list.append(line[i] )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1] )
												# join the aligned words list to form a justified line
												return "".join(UpperCamelCase__ )

				__lowerCamelCase									=       []
				__lowerCamelCase									=       []
				__lowerCamelCase									=       0
				for word in words:
								if width + len(UpperCamelCase__ ) + len(UpperCamelCase__ ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(UpperCamelCase__ )
												width += len(UpperCamelCase__ )
								else:
												# justify the line and add it to result
												answer.append(justify(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ ) )
												# reset new line and new width
												__lowerCamelCase			,		__lowerCamelCase									=       [word], len(UpperCamelCase__ )
				__lowerCamelCase									=       max_width - width - len(UpperCamelCase__ )
				answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
				return answer


if __name__ == "__main__":
					from doctest import testmod

					testmod()


