make muse warn immediately if gensim is not installed

flair/embeddings/token.py

@@ -2,7 +2,7 @@
 import logging
 import re
 import tempfile
-from collections import Counter, Mapping
+from collections import Counter
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
@@ -1283,6 +1283,8 @@ def __init__(
         self.static_embeddings = True
         self.__embedding_length: int = 300
         self.language_embeddings: Dict[str, Any] = {}
+        (KeyedVectors,) = lazy_import("word-embeddings", "gensim.models", "KeyedVectors")
+        self.kv = KeyedVectors
         super().__init__()
         self.eval()
 
@@ -1345,7 +1347,7 @@ def _add_embeddings_internal(self, sentences: List[Sentence]) -> List[Sentence]:
                 embeddings_file = cached_path(f"{hu_path}/muse.{language_code}.vec.gensim", cache_dir=cache_dir)
 
                 # load the model
-                self.language_embeddings[language_code] = gensim.models.KeyedVectors.load(str(embeddings_file))
+                self.language_embeddings[language_code] = self.kv.load(str(embeddings_file))
 
             for token, _token_idx in zip(sentence.tokens, range(len(sentence.tokens))):
                 word_embedding = self.get_cached_vec(language_code=language_code, word=token.text)
@@ -1401,7 +1403,7 @@ def __init__(
         else:
             if not language and model_file_path is None:
                 raise ValueError("Need to specify model_file_path if no language is give in BytePairEmbeddings")
-            BPEmb, = lazy_import("word-embeddings", "bpemb", "BPEmb")
+            (BPEmb,) = lazy_import("word-embeddings", "bpemb", "BPEmb")
 
             if language:
                 self.name: str = f"bpe-{language}-{syllables}-{dim}"
@@ -1504,7 +1506,14 @@ def from_params(cls, params):
             else:
                 embedding_file_path = None
                 dim = params["dim"]
-            return cls(name=params["name"], dim=dim, model_file_path=model_file_path, embedding_file_path=embedding_file_path, field=params.get("field"), preprocess=params.get("preprocess", True))
+            return cls(
+                name=params["name"],
+                dim=dim,
+                model_file_path=model_file_path,
+                embedding_file_path=embedding_file_path,
+                field=params.get("field"),
+                preprocess=params.get("preprocess", True),
+            )
 
     def to_params(self):
         return {
@@ -1541,7 +1550,7 @@ def state_dict(self, *args, **kwargs):
         return super().state_dict(*args, **kwargs)
 
     def _load_from_state_dict(
-            self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
     ):
         if not state_dict:
             # old embeddings do not have a torch-embedding and therefore do not store the weights in the saved torch state_dict