Apply isort and black reformatting

Signed-off-by: titu1994 <[email protected]>

nemo/collections/asr/parts/submodules/ctc_greedy_decoding.py

@@ -25,7 +25,10 @@
 from nemo.utils import logging
 
 
-def pack_hypotheses(hypotheses: List[rnnt_utils.Hypothesis], logitlen: torch.Tensor,) -> List[rnnt_utils.Hypothesis]:
+def pack_hypotheses(
+    hypotheses: List[rnnt_utils.Hypothesis],
+    logitlen: torch.Tensor,
+) -> List[rnnt_utils.Hypothesis]:
 
     if logitlen is not None:
         if hasattr(logitlen, 'cpu'):
@@ -54,6 +57,7 @@ def _states_to_device(dec_state, device='cpu'):
 
     return dec_state
 
+
 _DECODER_LENGTHS_NONE_WARNING = "Passing in decoder_lengths=None for CTC decoding is likely to be an error, since it is unlikely that each element of your batch has exactly the same length. decoder_lengths will default to decoder_output.shape[0]."
 
 
@@ -110,8 +114,7 @@ class GreedyCTCInfer(Typing, ConfidenceMethodMixin):
 
     @property
     def input_types(self):
-        """Returns definitions of module input ports.
-        """
+        """Returns definitions of module input ports."""
         # Input can be of dimension -
         # ('B', 'T', 'D') [Log probs] or ('B', 'T') [Labels]
 
@@ -122,8 +125,7 @@ def input_types(self):
 
     @property
     def output_types(self):
-        """Returns definitions of module output ports.
-        """
+        """Returns definitions of module output ports."""
         return {"predictions": [NeuralType(elements_type=HypothesisType())]}
 
     def __init__(
@@ -331,8 +333,7 @@ class GreedyBatchedCTCInfer(Typing, ConfidenceMethodMixin):
 
     @property
     def input_types(self):
-        """Returns definitions of module input ports.
-        """
+        """Returns definitions of module input ports."""
         # Input can be of dimension -
         # ('B', 'T', 'D') [Log probs] or ('B', 'T') [Labels]
 
@@ -343,8 +344,7 @@ def input_types(self):
 
     @property
     def output_types(self):
-        """Returns definitions of module output ports.
-        """
+        """Returns definitions of module output ports."""
         return {"predictions": [NeuralType(elements_type=HypothesisType())]}
 
     def __init__(

tests/collections/asr/decoding/test_ctc_decoding.py

@@ -90,7 +90,9 @@ def test_constructor_subword(self, tmp_tokenizer):
         assert decoding is not None
 
     @pytest.mark.unit
-    def test_char_decoding_greedy_forward(self,):
+    def test_char_decoding_greedy_forward(
+        self,
+    ):
         cfg = CTCDecodingConfig(strategy='greedy')
         vocab = char_vocabulary()
         decoding = CTCDecoding(decoding_cfg=cfg, vocabulary=vocab)
@@ -198,7 +200,9 @@ def test_subword_decoding_greedy_forward_hypotheses(self, tmp_tokenizer, alignme
     @pytest.mark.parametrize('timestamps', [False, True])
     @pytest.mark.parametrize('preserve_frame_confidence', [False, True])
     @pytest.mark.parametrize('length_is_none', [False, True])
-    def test_batched_decoding_logprobs(self, tmp_tokenizer, alignments, timestamps, preserve_frame_confidence, length_is_none):
+    def test_batched_decoding_logprobs(
+        self, tmp_tokenizer, alignments, timestamps, preserve_frame_confidence, length_is_none
+    ):
         cfg = CTCBPEDecodingConfig(
             strategy='greedy',
             preserve_alignments=alignments,