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

from ..utils.generic import ModelOutput



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      dataset
       UpperCamelCase__										=      process
       UpperCamelCase__										=      params






  def __len__(self	)		->      Optional[Any]:





       '''simple docstring'''





       return len(self.dataset	)






  def __getitem__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.dataset[i]
       UpperCamelCase__										=      self.process(SCREAMING_SNAKE_CASE_   ,				**self.params	)
       return processed



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None	)		->      List[Any]:





       '''simple docstring'''





       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	)		->      int:





       '''simple docstring'''





       return len(self.loader	)






  def __iter__(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      iter(self.loader	)
       return self






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       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(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
                      # 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(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
                      # 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__(SCREAMING_SNAKE_CASE_	)
       self._loader_batch_index += 1
       return result






  def 		_a					(self	)		->      Tuple:





       '''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
       UpperCamelCase__										=      next(self.iterator	)
       UpperCamelCase__										=      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	):
                 UpperCamelCase__										=      processed
            else:
                 UpperCamelCase__										=      list(processed.keys()	)[0]
                 UpperCamelCase__										=      processed[key]
            if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__										=      len(SCREAMING_SNAKE_CASE_	)
            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							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None	)		->      Union[str, Any]:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def __iter__(self	)		->      str:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       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							):






  def __iter__(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      iter(self.loader	)
       return self






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       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(SCREAMING_SNAKE_CASE_	)
                 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(SCREAMING_SNAKE_CASE_   ,				torch.Tensor	):
                      UpperCamelCase__										=      processed
                 else:
                      UpperCamelCase__										=      list(processed.keys()	)[0]
                      UpperCamelCase__										=      processed[key]
                 if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
                      UpperCamelCase__										=      len(SCREAMING_SNAKE_CASE_	)
                 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(SCREAMING_SNAKE_CASE_	)
                      if is_last:
                           return accumulator
            else:
                 UpperCamelCase__										=      processed
                 UpperCamelCase__										=      item.pop('''is_last'''	)
                 accumulator.append(SCREAMING_SNAKE_CASE_	)
       return accumulator



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      dataset
       UpperCamelCase__										=      key






  def __len__(self	)		->      str:





       '''simple docstring'''





       return len(self.dataset	)






  def __getitem__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





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



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      dataset
       UpperCamelCase__										=      keya
       UpperCamelCase__										=      keya






  def __len__(self	)		->      Tuple:





       '''simple docstring'''





       return len(self.dataset	)






  def __getitem__(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





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




from __future__ import annotations







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      get_failure_array(A							)

     # 2) Step through text searching for pattern
     UpperCamelCase__						,       UpperCamelCase__										=      0, 0  # index into text, pattern
     while i < len(A							):
          if pattern[j] == text[i]:
               if j == (len(A							) - 1):
                    return True
               j += 1

          # if this is a prefix in our pattern
          # just go back far enough to continue
          elif j > 0:
               UpperCamelCase__										=      failure[j - 1]
               continue
          i += 1
     return False







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      [0]
     UpperCamelCase__										=      0
     UpperCamelCase__										=      1
     while j < len(A							):
          if pattern[i] == pattern[j]:
               i += 1
          elif i > 0:
               UpperCamelCase__										=      failure[i - 1]
               continue
          j += 1
          failure.append(A							)
     return failure


if __name__ == "__main__":
     # Test 1)
     __magic_name__								='''abc1abc12'''
     __magic_name__								='''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     __magic_name__								='''alskfjaldsk23adsfabcabc'''
     assert kmp(pattern, texta) and not kmp(pattern, texta)

     # Test 2)
     __magic_name__								='''ABABX'''
     __magic_name__								='''ABABZABABYABABX'''
     assert kmp(pattern, text)

     # Test 3)
     __magic_name__								='''AAAB'''
     __magic_name__								='''ABAAAAAB'''
     assert kmp(pattern, text)

     # Test 4)
     __magic_name__								='''abcdabcy'''
     __magic_name__								='''abcxabcdabxabcdabcdabcy'''
     assert kmp(pattern, text)

     # Test 5)
     __magic_name__								='''aabaabaaa'''
     assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]



