'''simple docstring'''

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

from ..utils.generic import ModelOutput


class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      dataset
     lowerCAmelCase_			=      process
     lowerCAmelCase_			=      params





  def __len__(	self ):
     return len(self.dataset )







  def __getitem__(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.dataset[i]
     lowerCAmelCase_			=      self.process(_lowerCamelCase    ,     **self.params )
     return processed






class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None ):
     lowerCAmelCase_			=      loader
     lowerCAmelCase_			=      infer
     lowerCAmelCase_			=      params
     if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        lowerCAmelCase_			=      None
     lowerCAmelCase_			=      loader_batch_size

     # Internal bookkeeping
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      None





  def __len__(	self ):
     return len(self.loader )





  def __iter__(	self ):
     lowerCAmelCase_			=      iter(self.loader )
     return self





  def 						UpperCAmelCase_	(	self ):
     if isinstance(self._loader_batch_data    ,     torch.Tensor ):
        # Batch data is simple tensor, just fetch the slice
        lowerCAmelCase_			=      self._loader_batch_data[self._loader_batch_index]
     else:
        # Batch data is assumed to be BaseModelOutput (or dict)
        lowerCAmelCase_			=      {}
        for k, element in self._loader_batch_data.items():
           if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
              # Convert ModelOutput to tuple first
              lowerCAmelCase_			=      element.to_tuple()
              if isinstance(element[0]    ,     torch.Tensor ):
                 lowerCAmelCase_			=      tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
              elif isinstance(element[0]    ,     np.ndarray ):
                 lowerCAmelCase_			=      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(_lowerCamelCase    ,     _lowerCamelCase ):
              # Those are stored as lists of tensors so need specific unbatching.
              if isinstance(element[0]    ,     torch.Tensor ):
                 lowerCAmelCase_			=      tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
              elif isinstance(element[0]    ,     np.ndarray ):
                 lowerCAmelCase_			=      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
              lowerCAmelCase_			=      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

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







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






class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None ):
     super().__init__(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )





  def __iter__(	self ):
     lowerCAmelCase_			=      iter(self.loader )
     lowerCAmelCase_			=      None
     return self







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






class     __UpperCAmelCase       ( __a		):





  def __iter__(	self ):
     lowerCAmelCase_			=      iter(self.loader )
     return self







  def 						UpperCAmelCase_	(	self ):
     # 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.
     lowerCAmelCase_			=      False
     lowerCAmelCase_			=      []
     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:
           lowerCAmelCase_			=      self.loader_batch_item()
           lowerCAmelCase_			=      item.pop('''is_last''' )
           accumulator.append(_lowerCamelCase )
           if is_last:
              return accumulator

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






class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      dataset
     lowerCAmelCase_			=      key





  def __len__(	self ):
     return len(self.dataset )







  def __getitem__(	self    ,     _lowerCamelCase ):
     return self.dataset[i][self.key]






class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      dataset
     lowerCAmelCase_			=      keya
     lowerCAmelCase_			=      keya





  def __len__(	self ):
     return len(self.dataset )







  def __getitem__(	self    ,     _lowerCamelCase ):
     return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






'''simple docstring'''

from __future__ import annotations

from typing import Any



def snake_case_      (					__snake_case  :							list[Any])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0)



def snake_case_      (					__snake_case  :							list[Any]	,   __snake_case  :							list[Any]	,   __snake_case  :							int)			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   create_state_space_tree(__snake_case	,   __snake_case	,   index + 1)
   current_subsequence.append(sequence[index])
   create_state_space_tree(__snake_case	,   __snake_case	,   index + 1)
   current_subsequence.pop()


if __name__ == "__main__":
 A_     :						list[Any]    							=[3, 1, 2, 4]
 generate_all_subsequences(seq)

 seq.clear()
 seq.extend(['''A''', '''B''', '''C'''])
 generate_all_subsequences(seq)





