



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

from ..utils.generic import ModelOutput







class       __lowerCAmelCase   (      A						):



   def __init__(						self       :  List[str]			,		A       :  List[Any]			,		A       :  Any			,		A       :  List[Any])       ->   Optional[Any]:



      """simple docstring"""






      _UpperCAmelCase			=       dataset
      _UpperCAmelCase			=       process
      _UpperCAmelCase			=       params



   def __len__(						self       :  Tuple)       ->   List[str]:



      """simple docstring"""






      return len(self.dataset)



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



      """simple docstring"""






      _UpperCAmelCase			=       self.dataset[i]
      _UpperCAmelCase			=       self.process(A			,		**self.params)
      return processed







class       __lowerCAmelCase   (      A						):



   def __init__(						self       :  Optional[Any]			,		A       :  Union[str, Any]			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Union[str, Any]=None)       ->   Tuple:



      """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       :  List[str])       ->   Tuple:



      """simple docstring"""






      return len(self.loader)



   def __iter__(						self       :  str)       ->   List[str]:



      """simple docstring"""






      _UpperCAmelCase			=       iter(self.loader)
      return self



   def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Tuple:



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



   def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   int:



      """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(A			,		**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(A			,		torch.Tensor):
            _UpperCAmelCase			=       processed
         else:
            _UpperCAmelCase			=       list(processed.keys())[0]
            _UpperCAmelCase			=       processed[key]
         if isinstance(A			,		A):
            _UpperCAmelCase			=       len(A)
         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       __lowerCAmelCase   (      A						):



   def __init__(						self       :  Tuple			,		A       :  List[Any]			,		A       :  Any			,		A       :  int			,		A       :  Dict=None)       ->   Any:



      """simple docstring"""






      super().__init__(A			,		A			,		A)



   def __iter__(						self       :  Dict)       ->   int:



      """simple docstring"""






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



   def    _lowerCamelCase	(						self       :  int)       ->   Any:



      """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       __lowerCAmelCase   (      A						):



   def __iter__(						self       :  List[Any])       ->   List[str]:



      """simple docstring"""






      _UpperCAmelCase			=       iter(self.loader)
      return self



   def    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



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







class       __lowerCAmelCase   (      A						):



   def __init__(						self       :  List[str]			,		A       :  Dataset			,		A       :  str)       ->   List[Any]:



      """simple docstring"""






      _UpperCAmelCase			=       dataset
      _UpperCAmelCase			=       key



   def __len__(						self       :  List[str])       ->   Any:



      """simple docstring"""






      return len(self.dataset)



   def __getitem__(						self       :  Tuple			,		A       :  Tuple)       ->   int:



      """simple docstring"""






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







class       __lowerCAmelCase   (      A						):



   def __init__(						self       :  int			,		A       :  Dataset			,		A       :  str			,		A       :  str)       ->   Optional[int]:



      """simple docstring"""






      _UpperCAmelCase			=       dataset
      _UpperCAmelCase			=       keya
      _UpperCAmelCase			=       keya



   def __len__(						self       :  str)       ->   Dict:



      """simple docstring"""






      return len(self.dataset)



   def __getitem__(						self       :  List[Any]			,		A       :  int)       ->   Dict:



      """simple docstring"""






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





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat





def 							A     (					)    ->			tuple[list[int], int]:







			'''simple docstring'''
			_UpperCAmelCase			=       [randint(-1_000							,       1_000   ) for i in range(10   )]
			_UpperCAmelCase			=       randint(-5_000							,       5_000   )
			return (arr, r)


UpperCAmelCase__       =	make_dataset()





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, ...]:







			'''simple docstring'''
			for triplet in permutations(_UpperCAmelCase							,       3   ):
						if sum(_UpperCAmelCase   ) == target:
									return tuple(sorted(_UpperCAmelCase   )   )
			return (0, 0, 0)





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, int, int]:







			'''simple docstring'''
			arr.sort()
			_UpperCAmelCase			=       len(_UpperCAmelCase   )
			for i in range(n - 1   ):
						_UpperCAmelCase	,					_UpperCAmelCase			=       i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)





def 							A     (					)    ->			tuple[float, float]:







			'''simple docstring'''
			_UpperCAmelCase			=       '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
			_UpperCAmelCase			=       '\ntriplet_sum1(*dataset)\n'
			_UpperCAmelCase			=       '\ntriplet_sum2(*dataset)\n'
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			return (min(_UpperCAmelCase   ), min(_UpperCAmelCase   ))


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

							testmod()
							UpperCAmelCase__       =	solution_times()
							print(f"""The time for naive implementation is {times[0]}.""")
							print(f"""The time for optimized implementation is {times[1]}.""")
