


"""simple docstring"""




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

from ..utils.generic import ModelOutput
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	str					)		->      Any:
             a_ :		Dict							=  dataset
             a_ :		List[Any]							=  process
             a_ :		List[Any]							=  params
      def __len__(	self    :	int					)		->      str:
             return len(self.dataset					)

      def __getitem__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Union[str, Any]:
             a_ :		Optional[int]							=  self.dataset[i]
             a_ :		Union[str, Any]							=  self.process(SCREAMING_SNAKE_CASE__					,							**self.params					)
             return processed
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=None					)		->      Optional[int]:
             a_ :		Any							=  loader
             a_ :		str							=  infer
             a_ :		Optional[Any]							=  params
             if loader_batch_size == 1:
                    # Let's spare some time by deactivating altogether
                    a_ :		int							=  None
             a_ :		str							=  loader_batch_size

             # Internal bookkeeping
             a_ :		Optional[int]							=  None
             a_ :		List[str]							=  None
      def __len__(	self    :	Tuple					)		->      int:
             return len(self.loader					)
      def __iter__(	self    :	List[Any]					)		->      int:
             a_ :		Union[str, Any]							=  iter(self.loader					)
             return self
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Dict:
             if isinstance(self._loader_batch_data					,							torch.Tensor					):
                    # Batch data is simple tensor, just fetch the slice
                    a_ :		Optional[int]							=  self._loader_batch_data[self._loader_batch_index]
             else:
                    # Batch data is assumed to be BaseModelOutput (or dict)
                    a_ :		Optional[int]							=  {}
                    for k, element in self._loader_batch_data.items():
                           if isinstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
                                  # Convert ModelOutput to tuple first
                                  a_ :		Tuple							=  element.to_tuple()
                                  if isinstance(element[0]					,							torch.Tensor					):
                                         a_ :		str							=  tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
                                  elif isinstance(element[0]					,							np.ndarray					):
                                         a_ :		Optional[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(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
                                  # Those are stored as lists of tensors so need specific unbatching.
                                  if isinstance(element[0]					,							torch.Tensor					):
                                         a_ :		Union[str, Any]							=  tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
                                  elif isinstance(element[0]					,							np.ndarray					):
                                         a_ :		Tuple							=  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_ :		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

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Any:
             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_ :		Optional[Any]							=  next(self.iterator					)
             a_ :		Tuple							=  self.infer(SCREAMING_SNAKE_CASE__					,							**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(SCREAMING_SNAKE_CASE__					,							torch.Tensor					):
                           a_ :		int							=  processed
                    else:
                           a_ :		Dict							=  list(processed.keys()					)[0]
                           a_ :		List[Any]							=  processed[key]
                    if isinstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
                           a_ :		str							=  len(SCREAMING_SNAKE_CASE__					)
                    else:
                           a_ :		str							=  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_ :		Tuple							=  observed_batch_size
                    # Setting internal index to unwrap the batch
                    a_ :		List[Any]							=  processed
                    a_ :		List[str]							=  0
                    return self.loader_batch_item()
             else:
                    # We're not unrolling batches
                    return processed
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      List[str]:
             super().__init__(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def __iter__(	self    :	Any					)		->      int:
             a_ :		Union[str, Any]							=  iter(self.loader					)
             a_ :		Optional[int]							=  None
             return self

      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Any:
             if self.subiterator is None:
                    a_ :		Optional[Any]							=  self.infer(next(self.iterator					)					,							**self.params					)
             try:
                    # Try to return next item
                    a_ :		List[str]							=  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_ :		Any							=  self.infer(next(self.iterator					)					,							**self.params					)
                    a_ :		Union[str, Any]							=  next(self.subiterator					)
             return processed
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __iter__(	self    :	Tuple					)		->      Optional[Any]:
             a_ :		Any							=  iter(self.loader					)
             return self

      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Tuple:
             # 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.
             a_ :		List[Any]							=  False
             a_ :		List[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:
                           a_ :		Dict							=  self.loader_batch_item()
                           a_ :		Dict							=  item.pop('is_last'					)
                           accumulator.append(SCREAMING_SNAKE_CASE__					)
                           if is_last:
                                  return accumulator

             while not is_last:
                    a_ :		List[Any]							=  self.infer(next(self.iterator					)					,							**self.params					)
                    if self.loader_batch_size is not None:
                           if isinstance(SCREAMING_SNAKE_CASE__					,							torch.Tensor					):
                                  a_ :		int							=  processed
                           else:
                                  a_ :		Union[str, Any]							=  list(processed.keys()					)[0]
                                  a_ :		int							=  processed[key]
                           if isinstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
                                  a_ :		str							=  len(SCREAMING_SNAKE_CASE__					)
                           else:
                                  a_ :		List[str]							=  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_ :		Any							=  observed_batch_size
                           a_ :		Optional[int]							=  processed
                           a_ :		Tuple							=  0
                           while self._loader_batch_index < self.loader_batch_size:
                                  a_ :		Optional[Any]							=  self.loader_batch_item()
                                  a_ :		Any							=  item.pop('is_last'					)
                                  accumulator.append(SCREAMING_SNAKE_CASE__					)
                                  if is_last:
                                         return accumulator
                    else:
                           a_ :		Tuple							=  processed
                           a_ :		List[Any]							=  item.pop('is_last'					)
                           accumulator.append(SCREAMING_SNAKE_CASE__					)
             return accumulator
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Dataset					,							SCREAMING_SNAKE_CASE__    :	str					)		->      Tuple:
             a_ :		Union[str, Any]							=  dataset
             a_ :		Union[str, Any]							=  key
      def __len__(	self    :	str					)		->      Any:
             return len(self.dataset					)

      def __getitem__(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Tuple:
             return self.dataset[i][self.key]
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def __init__(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Dataset					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	str					)		->      Dict:
             a_ :		Tuple							=  dataset
             a_ :		Optional[Any]							=  keya
             a_ :		List[Any]							=  keya
      def __len__(	self    :	Optional[int]					)		->      Union[str, Any]:
             return len(self.dataset					)

      def __getitem__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Tuple					)		->      Dict:
             return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






from string import ascii_uppercase

UpperCAmelCase_				:			Dict   =	{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_				:			Optional[int]   =	dict(enumerate(ascii_uppercase))







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Tuple							=  len(__A    )
       a_ :		int							=  0
       while True:
              if x == i:
                     a_ :		Tuple							=  0
              if len(__A    ) == len(__A    ):
                     break
              key += key[i]
              i += 1
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Optional[int]							=  ''
       a_ :		Any							=  0
       for letter in message:
              if letter == " ":
                     cipher_text += " "
              else:
                     a_ :		Optional[Any]							=  (dicta[letter] - dicta[key_new[i]]) % 26
                     i += 1
                     cipher_text += dicta[x]
       return cipher_text







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      str    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  ''
       a_ :		Optional[Any]							=  0
       for letter in cipher_text:
              if letter == " ":
                     or_txt += " "
              else:
                     a_ :		Union[str, Any]							=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
                     i += 1
                     or_txt += dicta[x]
       return or_txt







def 	SCREAMING_SNAKE_CASE_						(      )     ->		None:

       """simple docstring"""

       a_ :		Tuple							=  'THE GERMAN ATTACK'
       a_ :		Dict							=  'SECRET'
       a_ :		Optional[Any]							=  generate_key(__A  ,   __A    )
       a_ :		Union[str, Any]							=  cipher_text(__A  ,   __A    )
       print(F"""Encrypted Text = {s}"""    )
       print(F"""Original Text = {original_text(__A  ,   __A    )}"""    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()


