

"""simple docstring"""





class  _lowerCAmelCase  :







 """simple docstring"""







 def __init__(      self							:      List[str]     ):
    __lowercase								=						0
    __lowercase								=						0
    __lowercase								=						{}


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      Any     ):
    if vertex not in self.adjacency:
       __lowercase								=						{}
       self.num_vertices += 1


 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      Union[str, Any]     ):
    self.add_vertex(UpperCAmelCase__     )
    self.add_vertex(UpperCAmelCase__     )

    if head == tail:
       return

    __lowercase								=						weight
    __lowercase								=						weight


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						self.get_edges()
    for edge in edges:
       __lowercase		,__lowercase		,__lowercase								=						edge
       edges.remove((tail, head, weight)     )
    for i in range(len(UpperCAmelCase__     )     ):
       __lowercase								=						list(edges[i]     )

    edges.sort(key=lambda UpperCAmelCase__     : e[2]     )
    for i in range(len(UpperCAmelCase__     ) - 1     ):
       if edges[i][2] >= edges[i + 1][2]:
          __lowercase								=						edges[i][2] + 1
    for edge in edges:
       __lowercase		,__lowercase		,__lowercase								=						edge
       __lowercase								=						weight
       __lowercase								=						weight


 def __str__(      self							:      Tuple     ):
    __lowercase								=						""
    for tail in self.adjacency:
       for head in self.adjacency[tail]:
          __lowercase								=						self.adjacency[head][tail]
          string += F"""{head} -> {tail} == {weight}\n"""
    return string.rstrip("\n"     )


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						[]
    for tail in self.adjacency:
       for head in self.adjacency[tail]:
          output.append((tail, head, self.adjacency[head][tail])     )
    return output


 def  _lowercase			(      self							:      List[str]     ):
    return self.adjacency.keys()


 @staticmethod
 def  _lowercase			(      UpperCAmelCase__							:      List[str]=None, UpperCAmelCase__							:      Dict=None     ):
    __lowercase								=						Graph()
    if vertices is None:
       __lowercase								=						[]
    if edges is None:
       __lowercase								=						[]
    for vertex in vertices:
       g.add_vertex(UpperCAmelCase__     )
    for edge in edges:
       g.add_edge(*UpperCAmelCase__     )
    return g




 class  _lowerCAmelCase  :







  """simple docstring"""







  def __init__(      self							:      List[Any]     ):
     __lowercase								=						{}
     __lowercase								=						{}


  def __len__(      self							:      int     ):
     return len(self.parent     )


  def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      Any     ):
     if item in self.parent:
        return self.find(UpperCAmelCase__     )

     __lowercase								=						item
     __lowercase								=						0
     return item


  def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      int     ):
     if item not in self.parent:
        return self.make_set(UpperCAmelCase__     )
     if item != self.parent[item]:
        __lowercase								=						self.find(self.parent[item]     )
     return self.parent[item]



  def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Optional[Any]     ):
     __lowercase								=						self.find(UpperCAmelCase__     )
     __lowercase								=						self.find(UpperCAmelCase__     )

     if roota == roota:
        return roota

     if self.rank[roota] > self.rank[roota]:
        __lowercase								=						roota
        return roota

     if self.rank[roota] < self.rank[roota]:
        __lowercase								=						roota
        return roota

     if self.rank[roota] == self.rank[roota]:
        self.rank[roota] += 1
        __lowercase								=						roota
        return roota
     return None







 @staticmethod
 def  _lowercase			(      UpperCAmelCase__							:      List[str]     ):
    __lowercase								=						graph.num_vertices

    __lowercase								=						Graph.UnionFind()
    __lowercase								=						[]
    while num_components > 1:
       __lowercase								=						{}
       for vertex in graph.get_vertices():
          __lowercase								=						-1

       __lowercase								=						graph.get_edges()
       for edge in edges:
          __lowercase		,__lowercase		,__lowercase								=						edge
          edges.remove((tail, head, weight)     )
       for edge in edges:
          __lowercase		,__lowercase		,__lowercase								=						edge
          __lowercase								=						union_find.find(UpperCAmelCase__     )
          __lowercase								=						union_find.find(UpperCAmelCase__     )
          if seta != seta:
             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                __lowercase								=						[head, tail, weight]

             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                __lowercase								=						[head, tail, weight]
       for vertex in cheap_edge:
          if cheap_edge[vertex] != -1:
             __lowercase		,__lowercase		,__lowercase								=						cheap_edge[vertex]
             if union_find.find(UpperCAmelCase__     ) != union_find.find(UpperCAmelCase__     ):
                union_find.union(UpperCAmelCase__, UpperCAmelCase__     )
                mst_edges.append(cheap_edge[vertex]     )
                __lowercase								=						num_components - 1
    __lowercase								=						Graph.build(edges=UpperCAmelCase__     )
    return mst







"""simple docstring"""





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

from ..utils.generic import ModelOutput




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[str], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Dict     ):
    __lowercase								=						dataset
    __lowercase								=						process
    __lowercase								=						params


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



 def __getitem__(      self							:      List[Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						self.dataset[i]
    __lowercase								=						self.process(UpperCAmelCase__, **self.params     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[Any]=None     ):
    __lowercase								=						loader
    __lowercase								=						infer
    __lowercase								=						params
    if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       __lowercase								=						None
    __lowercase								=						loader_batch_size

    # Internal bookkeeping
    __lowercase								=						None
    __lowercase								=						None


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


 def __iter__(      self							:      List[str]     ):
    __lowercase								=						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
       __lowercase								=						self._loader_batch_data[self._loader_batch_index]
    else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       __lowercase								=						{}
       for k, element in self._loader_batch_data.items():
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Convert ModelOutput to tuple first
             __lowercase								=						element.to_tuple()
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						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     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						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
             __lowercase								=						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

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



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







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      int, UpperCAmelCase__							:      str=None     ):
    super().__init__(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )


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



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







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __iter__(      self							:      int     ):
    __lowercase								=						iter(self.loader     )
    return self



 def  _lowercase			(      self							:      List[str]     ):
    # 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.
    __lowercase								=						False
    __lowercase								=						[]
    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:
          __lowercase								=						self.loader_batch_item()
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
          if is_last:
             return accumulator

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







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[Any], UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						key


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



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







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      str, UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						keya
    __lowercase								=						keya


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



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




