text: from copy import deepcopy
  from typing import Dict, Generic, List, TypeVar
  
  import torch
  
  from allennlp.data.vocabulary import Vocabulary
  
  DataArray = TypeVar(
      "DataArray", torch.Tensor, Dict[str, torch.Tensor], Dict[str, Dict[str, torch.Tensor]]
  )
  
  
  class Field(Generic[DataArray]):
      """
      A `Field` is some piece of a data instance that ends up as an tensor in a model (either as an
      input or an output).  Data instances are just collections of fields.
  
      Fields go through up to two steps of processing: (1) tokenized fields are converted into token
      ids, (2) fields containing token ids (or any other numeric data) are padded (if necessary) and
      converted into tensors.  The `Field` API has methods around both of these steps, though they
      may not be needed for some concrete `Field` classes - if your field doesn't have any strings
      that need indexing, you don't need to implement `count_vocab_items` or `index`.  These
      methods `pass` by default.
  
      Once a vocabulary is computed and all fields are indexed, we will determine padding lengths,
      then intelligently batch together instances and pad them into actual tensors.
      """
  
      __slots__ = []  # type: ignore
  
      def count_vocab_items(self, counter: Dict[str, Dict[str, int]]):
          """
          If there are strings in this field that need to be converted into integers through a
          :class:`Vocabulary`, here is where we count them, to determine which tokens are in or out
          of the vocabulary.
  
          If your `Field` does not have any strings that need to be converted into indices, you do
          not need to implement this method.
  
          A note on this `counter`: because `Fields` can represent conceptually different things,
          we separate the vocabulary items by `namespaces`.  This way, we can use a single shared
          mechanism to handle all mappings from strings to integers in all fields, while keeping
          words in a `TextField` from sharing the same ids with labels in a `LabelField` (e.g.,
          "entailment" or "contradiction" are labels in an entailment task)
  
          Additionally, a single `Field` might want to use multiple namespaces - `TextFields` can
          be represented as a combination of word ids and character ids, and you don't want words and
          characters to share the same vocabulary - "a" as a word should get a different id from "a"
          as a character, and the vocabulary sizes of words and characters are very different.
  
          Because of this, the first key in the `counter` object is a `namespace`, like "tokens",
          "token_characters", "tags", or "labels", and the second key is the actual vocabulary item.
          """
          pass
  
      def index(self, vocab: Vocabulary):
          """
          Given a :class:`Vocabulary`, converts all strings in this field into (typically) integers.
          This `modifies` the `Field` object, it does not return anything.
  
          If your `Field` does not have any strings that need to be converted into indices, you do
          not need to implement this method.
          """
          pass
  
      def get_padding_lengths(self) -> Dict[str, int]:
          """
          If there are things in this field that need padding, note them here.  In order to pad a
          batch of instance, we get all of the lengths from the batch, take the max, and pad
          everything to that length (or use a pre-specified maximum length).  The return value is a
          dictionary mapping keys to lengths, like `{'num_tokens': 13}`.
  
          This is always called after :func:`index`.
          """
          raise NotImplementedError
  
      def as_tensor(self, padding_lengths: Dict[str, int]) -> DataArray:
          """
          Given a set of specified padding lengths, actually pad the data in this field and return a
          torch Tensor (or a more complex data structure) of the correct shape.  We also take a
          couple of parameters that are important when constructing torch Tensors.
  
          # Parameters
  
          padding_lengths : `Dict[str, int]`
              This dictionary will have the same keys that were produced in
              :func:`get_padding_lengths`.  The values specify the lengths to use when padding each
              relevant dimension, aggregated across all instances in a batch.
          """
          raise NotImplementedError
  
      def empty_field(self) -> "Field":
          """
          So that `ListField` can pad the number of fields in a list (e.g., the number of answer
          option `TextFields`), we need a representation of an empty field of each type.  This
          returns that.  This will only ever be called when we're to the point of calling
          :func:`as_tensor`, so you don't need to worry about `get_padding_lengths`,
          `count_vocab_items`, etc., being called on this empty field.
  
          We make this an instance method instead of a static method so that if there is any state
          in the Field, we can copy it over (e.g., the token indexers in `TextField`).
          """
          raise NotImplementedError
  
      def batch_tensors(self, tensor_list: List[DataArray]) -> DataArray:  # type: ignore
          """
          Takes the output of `Field.as_tensor()` from a list of `Instances` and merges it into
          one batched tensor for this `Field`.  The default implementation here in the base class
          handles cases where `as_tensor` returns a single torch tensor per instance.  If your
          subclass returns something other than this, you need to override this method.
  
          This operation does not modify `self`, but in some cases we need the information
          contained in `self` in order to perform the batching, so this is an instance method, not
          a class method.
          """
  
          return torch.stack(tensor_list)
  
      def __eq__(self, other) -> bool:
          if isinstance(self, other.__class__):
              # With the way "slots" classes work, self.__slots__ only gives the slots defined
              # by the current class, but not any of its base classes. Therefore to truly
              # check for equality we have to check through all of the slots in all of the
              # base classes as well.
              for class_ in self.__class__.mro():
                  for attr in getattr(class_, "__slots__", []):
                      if getattr(self, attr) != getattr(other, attr):
                          return False
              # It's possible that a subclass was not defined as a slots class, in which
              # case we'll need to check __dict__.
              if hasattr(self, "__dict__"):
                  return self.__dict__ == other.__dict__
              return True
          return NotImplemented
  
      def __len__(self):
          raise NotImplementedError
  
      def duplicate(self):
          return deepcopy(self)
  