






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

from ..utils.generic import ModelOutput


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  dataset
     UpperCamelCase_						=  process
     UpperCamelCase_						=  params




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




  def __getitem__(  self		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  self.dataset[i]
     UpperCamelCase_						=  self.process(_UpperCAmelCase		,		**self.params			)
     return processed




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			Optional[int]:
     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[Any]:
     return len(self.loader			)




  def __iter__(  self			)      ->			List[Any]:
     UpperCamelCase_						=  iter(self.loader			)
     return self




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     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(_UpperCAmelCase		,		_UpperCAmelCase			):
              # 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(_UpperCAmelCase		,		_UpperCAmelCase			):
              # 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__(_UpperCAmelCase			)
     self._loader_batch_index += 1
     return result




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     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(_UpperCAmelCase		,		**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(_UpperCAmelCase		,		torch.Tensor			):
           UpperCamelCase_						=  processed
        else:
           UpperCamelCase_						=  list(processed.keys()			)[0]
           UpperCamelCase_						=  processed[key]
        if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
           UpperCamelCase_						=  len(_UpperCAmelCase			)
        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__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None			)      ->			List[str]:
     super().__init__(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




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




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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__     ):






  """simple docstring"""





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




  def  _UpperCAmelCase  (  self			)      ->			Any:
     # 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.
     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(_UpperCAmelCase			)
           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(_UpperCAmelCase		,		torch.Tensor			):
              UpperCamelCase_						=  processed
           else:
              UpperCamelCase_						=  list(processed.keys()			)[0]
              UpperCamelCase_						=  processed[key]
           if isinstance(_UpperCAmelCase		,		_UpperCAmelCase			):
              UpperCamelCase_						=  len(_UpperCAmelCase			)
           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(_UpperCAmelCase			)
              if is_last:
                 return accumulator
        else:
           UpperCamelCase_						=  processed
           UpperCamelCase_						=  item.pop('is_last'			)
           accumulator.append(_UpperCAmelCase			)
     return accumulator




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  dataset
     UpperCamelCase_						=  key




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




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




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  dataset
     UpperCamelCase_						=  keya
     UpperCamelCase_						=  keya




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




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













from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

snake_case__   :						List[str]													=   TypeVar("""T""")






def      _snake_case     (__lowercase):
   return (position - 1) // 2






def      _snake_case     (__lowercase):
   return (2 * position) + 1






def      _snake_case     (__lowercase):
   return (2 * position) + 2


class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __len__(  self			)      ->			int:
     return self.elements




  def __repr__(  self			)      ->			str:
     return str(self.heap			)




  def  _UpperCAmelCase  (  self			)      ->			bool:
     # Check if the priority queue is empty
     return self.elements == 0




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an element with given priority to the queue
     self.heap.append((elem, weight)			)
     UpperCamelCase_						=  self.elements
     self.elements += 1
     self._bubble_up(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			T:
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0		,		self.elements - 1			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[0]
        self._bubble_down(_UpperCAmelCase			)
     return elem




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Update the weight of the given key
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_						=  (elem, weight)
     if position > 0:
        UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_UpperCAmelCase			)
        else:
           self._bubble_down(_UpperCAmelCase			)
     else:
        self._bubble_down(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     UpperCamelCase_						=  self.position_map[elem]
     if curr_pos == 0:
        return None
     UpperCamelCase_						=  get_parent_position(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
        return self._bubble_up(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     UpperCamelCase_						=  self.position_map[elem]
     UpperCamelCase_		,   UpperCamelCase_						=  self.heap[curr_pos]
     UpperCamelCase_						=  get_child_left_position(_UpperCAmelCase			)
     UpperCamelCase_						=  get_child_right_position(_UpperCAmelCase			)
     if child_left_position < self.elements and child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     if child_left_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     else:
        return None
     if child_right_position < self.elements:
        UpperCamelCase_		,   UpperCamelCase_						=  self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_UpperCAmelCase		,		_UpperCAmelCase			)
           return self._bubble_down(_UpperCAmelCase			)
     return None




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Swap the nodes at the given positions
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_						=  self.heap[nodea_pos][0]
     UpperCamelCase_		,   UpperCamelCase_						=  (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     UpperCamelCase_						=  nodea_pos
     UpperCamelCase_						=  nodea_pos




class     _a							(	Generic[T]     ):






  """simple docstring"""





  def __init__(  self			)      ->			None:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  0




  def __repr__(  self			)      ->			str:
     return str(self.connections			)




  def __len__(  self			)      ->			int:
     return self.nodes




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			None:
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        UpperCamelCase_						=  {}
        self.nodes += 1




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			None:
     # Add an edge between 2 nodes in the graph
     self.add_node(_UpperCAmelCase			)
     self.add_node(_UpperCAmelCase			)
     UpperCamelCase_						=  weight
     UpperCamelCase_						=  weight






def      _snake_case     (__lowercase     ,   ):
   UpperCamelCase_						=  {node: maxsize for node in graph.connections}
   UpperCamelCase_						=  {node: None for node in graph.connections}

   UpperCamelCase_						=  MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__lowercase     ,   __lowercase)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   UpperCamelCase_						=  priority_queue.extract_min()
   UpperCamelCase_						=  0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__lowercase     ,   dist[neighbour])
         UpperCamelCase_						=  node

    # running prim's algorithm
   while not priority_queue.is_empty():
      UpperCamelCase_						=  priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            UpperCamelCase_						=  dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__lowercase     ,   dist[neighbour])
            UpperCamelCase_						=  node
   return dist, parent





