content: import pathlib
  import warnings
  
  import numpy as np
  from typing import Union, List
  from sklearn.metrics import pairwise_distances
  
  import floret
  
  from whatlies.embedding import Embedding
  from whatlies.embeddingset import EmbeddingSet
  
  from whatlies.language._common import SklearnTransformerMixin, HiddenPrints
  
  
  class FloretLanguage(SklearnTransformerMixin):
      """
      This object is used to lazily fetch [Embedding][whatlies.embedding.Embedding]s or
      [EmbeddingSet][whatlies.embeddingset.EmbeddingSet]s from a floret language
      backend.
  
      Important:
          The vectors are not given by this library they must be on disk upfront.
  
          To train your own floret vectors see the guide [here](https://github.com/explosion/floret/tree/main/python).
          In short, you can train your model via;
  
          ```python
          import floret
  
          model = floret.train_unsupervised("data.txt")
          model.save_model("vectors.bin")
          ```
  
          This language backend might require you to manually install extra dependencies
          unless you installed via either;
  
          ```
          pip install whatlies[floret]
          pip install whatlies[all]
          ```
  
      Arguments:
          path: path to the vectors on disk, be sure that it's on disk beforehand
  
      **Usage**:
  
      ```python
      > from whatlies.language import FloretLanguage
      > lang = FasttextLanguage("cc.en.300.bin")
      > lang['python']
      > lang = FasttextLanguage("cc.en.300.bin", size=10)
      > lang[['python', 'snake', 'dog']]
      ```
      """
  
      def __init__(self, path, size=None):
          self.size = size
          self.path = path
          # we have to use this class to prevent the warning hidden as a print statement from the fasttext lib
          with HiddenPrints():
              if isinstance(path, str):
                  self.model = floret.load_model(path)
              elif isinstance(path, pathlib.Path):
                  self.model = floret.load_model(str(path))
              else:
                  raise ValueError("Path must be `str` or `pathlib.Path`.")
  
      def __getitem__(self, query: Union[str, List[str]]):
          """
          Retreive a single embedding or a set of embeddings.
  
          Arguments:
              query: single string or list of strings
  
          **Usage**
          ```python
          > lang = FasttextLanguage("cc.en.300.bin")
          > lang['python']
          > lang[['python'], ['snake']]
          > lang[['nobody expects'], ['the spanish inquisition']]
          ```
          """
          if isinstance(query, str):
              vec = self.model.get_word_vector(query)
              return Embedding(query, vec)
          return EmbeddingSet(*[self[tok] for tok in query])
  
      def _prepare_queries(self, top_n, lower):
          queries = [w for w in self.model.get_words()]
          if lower:
              queries = [w for w in queries if w.is_lower]
          if top_n is not None:
              queries = queries[:top_n]
          if len(queries) == 0:
              raise ValueError(
                  f"Language model has no tokens for this setting. Consider raising top_n={top_n}"
              )
          return queries
  
      def _calculate_distances(self, emb, queries, metric):
          vec = emb.vector
          vector_matrix = np.array([self.model.get_word_vector(w) for w in queries])
          return pairwise_distances(vector_matrix, vec.reshape(1, -1), metric=metric)
  
      def embset_proximity(
          self,
          emb: Union[str, Embedding],
          max_proximity: float = 0.1,
          top_n=20_000,
          lower=True,
          metric="cosine",
      ):
          """
          Retreive an [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] or embeddings that are within a proximity.
  
          Arguments:
              emb: query to use
              max_proximity: the number of items you'd like to see returned
              top_n: likelihood limit that sets the subset of words to search
              metric: metric to use to calculate distance, must be scipy or sklearn compatible
              lower: only fetch lower case tokens
  
          Returns:
              An [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] containing the similar embeddings.
          """
          if isinstance(emb, str):
              emb = self[emb]
  
          queries = self._prepare_queries(top_n, lower)
          distances = self._calculate_distances(emb, queries, metric)
          return EmbeddingSet(
              {w: self[w] for w, d in zip(queries, distances) if d <= max_proximity}
          )
  
      def embset_similar(
          self,
          emb: Union[str, Embedding],
          n: int = 10,
          top_n=20_000,
          lower=False,
          metric="cosine",
      ):
          """
          Retreive an [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] that are the most similar to the passed query.
  
          Arguments:
              emb: query to use
              n: the number of items you'd like to see returned
              top_n: likelihood limit that sets the subset of words to search
              metric: metric to use to calculate distance, must be scipy or sklearn compatible
              lower: only fetch lower case tokens, note that the official english model only has lower case tokens
  
          Important:
              This method is incredibly slow at the moment without a good `top_n` setting due to
              [this bug](https://github.com/facebookresearch/fastText/issues/1040).
  
          Returns:
              An [EmbeddingSet][whatlies.embeddingset.EmbeddingSet] containing the similar embeddings.
          """
          embs = [w[0] for w in self.score_similar(emb, n, top_n, lower, metric)]
          return EmbeddingSet({w.name: w for w in embs})
  
      def score_similar(
          self,
          emb: Union[str, Embedding],
          n: int = 10,
          top_n=20_000,
          lower=False,
          metric="cosine",
      ):
          """
          Retreive a list of (Embedding, score) tuples that are the most similar to the passed query.
  
          Arguments:
              emb: query to use
              n: the number of items you'd like to see returned
              top_n: likelihood limit that sets the subset of words to search, to ignore set to `None`
              metric: metric to use to calculate distance, must be scipy or sklearn compatible
              lower: only fetch lower case tokens, note that the official english model only has lower case tokens
  
          Important:
              This method is incredibly slow at the moment without a good `top_n` setting due
              to [this bug](https://github.com/facebookresearch/fastText/issues/1040).
  
          Returns:
              An list of ([Embedding][whatlies.embedding.Embedding], score) tuples.
          """
          if isinstance(emb, str):
              emb = self[emb]
  
          queries = self._prepare_queries(top_n, lower)
          distances = self._calculate_distances(emb, queries, metric)
          by_similarity = sorted(zip(queries, distances), key=lambda z: z[1])
  
          if len(queries) < n:
              warnings.warn(
                  f"We could only find {len(queries)} feasible words. Consider changing `top_n` or `lower`",
                  UserWarning,
              )
  
          return [(self[q], float(d)) for q, d in by_similarity[:n]]
  