"""simple docstring"""






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

from ..utils.generic import ModelOutput





class 				_A		( snake_case__			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dataset
            lowercase														=			process
            lowercase														=			params

      def __len__(		self		):







            """simple docstring"""



            return len(self.dataset		)







      def __getitem__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.dataset[i]
            lowercase														=			self.process(UpperCAmelCase_	, **self.params		)
            return processed






class 				_A		( snake_case__			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None		):







            """simple docstring"""



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

            # Internal bookkeeping
            lowercase														=			None
            lowercase														=			None

      def __len__(		self		):







            """simple docstring"""



            return len(self.loader		)

      def __iter__(		self		):







            """simple docstring"""



            lowercase														=			iter(self.loader		)
            return self

      def        A__ (		self		):







            """simple docstring"""



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

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







      def        A__ (		self		):







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






class 				_A		( snake_case__			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None		):







            """simple docstring"""



            super().__init__(UpperCAmelCase_	, UpperCAmelCase_	, UpperCAmelCase_		)

      def __iter__(		self		):







            """simple docstring"""



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







      def        A__ (		self		):







            """simple docstring"""



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






class 				_A		( snake_case__			):

      def __iter__(		self		):







            """simple docstring"""



            lowercase														=			iter(self.loader		)
            return self







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			False
            lowercase														=			[]
            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:
                        lowercase														=			self.loader_batch_item()
                        lowercase														=			item.pop("""is_last"""		)
                        accumulator.append(UpperCAmelCase_		)
                        if is_last:
                              return accumulator

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






class 				_A		( snake_case__			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dataset
            lowercase														=			key

      def __len__(		self		):







            """simple docstring"""



            return len(self.dataset		)







      def __getitem__(		self	, __lowerCAmelCase		):







            """simple docstring"""



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






class 				_A		( snake_case__			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dataset
            lowercase														=			keya
            lowercase														=			keya

      def __len__(		self		):







            """simple docstring"""



            return len(self.dataset		)







      def __getitem__(		self	, __lowerCAmelCase		):







            """simple docstring"""



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




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 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:
                  lowercase														=			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]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # 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(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= 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(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


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

       testmod()



