




'''simple docstring'''





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

from ..utils.generic import ModelOutput

class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= dataset
          _snake_case				= process
          _snake_case				= params






     def __len__(					self       )				->     List[str]:
          return len(self.dataset       )






     def __getitem__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          _snake_case				= self.dataset[i]
          _snake_case				= self.process(_SCREAMING_SNAKE_CASE   ,**self.params       )
          return processed







class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     List[str]:
          _snake_case				= loader
          _snake_case				= infer
          _snake_case				= params
          if loader_batch_size == 1:
               # Let's spare some time by deactivating altogether
               _snake_case				= None
          _snake_case				= loader_batch_size

          # Internal bookkeeping
          _snake_case				= None
          _snake_case				= None






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






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






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

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






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







class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Optional[Any]:
          super().__init__(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






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






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







class   _a							(		__lowerCAmelCase				):






     def __iter__(					self       )				->     Dict:
          _snake_case				= iter(self.loader       )
          return self






     def 							_lowercase      (					self       )				->     int:
          # 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.
          _snake_case				= False
          _snake_case				= []
          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:
                    _snake_case				= self.loader_batch_item()
                    _snake_case				= item.pop("is_last"       )
                    accumulator.append(_SCREAMING_SNAKE_CASE       )
                    if is_last:
                         return accumulator

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







class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     int:
          _snake_case				= dataset
          _snake_case				= key






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






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







class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Any:
          _snake_case				= dataset
          _snake_case				= keya
          _snake_case				= keya






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






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








'''simple docstring'''





from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     List[str]:
          _snake_case				= data
          _snake_case				= None







class   _a							:






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






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.head
          while temp is not None:
               print(temp.data   ,end=" "       )
               _snake_case				= temp.next
          print()






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= Node(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.head
          _snake_case				= new_node






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          if node_data_a == node_data_a:
               return
          else:
               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               _snake_case				= self.head
               while node_a is not None and node_a.data != node_data_a:
                    _snake_case				= node_a.next

               if node_a is None or node_a is None:
                    return

               _snake_case				,   _snake_case				= node_a.data, node_a.data


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		LinkedList()
   for i in range(5, 0, -1):
      ll.push(i)

   ll.print_list()

   ll.swap_nodes(1, 4)
   print('''After swapping''')
   ll.print_list()


