Apply isort and black reformatting

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

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

@@ -1018,7 +1018,9 @@ class CTCDecoding(AbstractCTCDecoding):
     """
 
     def __init__(
-        self, decoding_cfg, vocabulary,
+        self,
+        decoding_cfg,
+        vocabulary,
     ):
         blank_id = len(vocabulary)
         self.vocabulary = vocabulary

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

@@ -25,7 +25,10 @@
 from nemo.utils import logging, logging_mode
 
 
-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'):
@@ -108,8 +111,7 @@ 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]
 
@@ -120,8 +122,7 @@ 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__(
@@ -145,7 +146,9 @@ def __init__(
 
     @typecheck()
     def forward(
-        self, decoder_output: torch.Tensor, decoder_lengths: torch.Tensor,
+        self,
+        decoder_output: torch.Tensor,
+        decoder_lengths: torch.Tensor,
     ):
         """Returns a list of hypotheses given an input batch of the encoder hidden embedding.
         Output token is generated auto-repressively.
@@ -330,8 +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]
 
@@ -342,8 +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__(
@@ -367,7 +368,9 @@ def __init__(
 
     @typecheck()
     def forward(
-        self, decoder_output: torch.Tensor, decoder_lengths: torch.Tensor,
+        self,
+        decoder_output: torch.Tensor,
+        decoder_lengths: torch.Tensor,
     ):
         """Returns a list of hypotheses given an input batch of the encoder hidden embedding.
         Output token is generated auto-repressively.

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)

tests/collections/asr/test_asr_ctc_encoder_model_bpe.py

@@ -309,7 +309,10 @@ def test_ASRDatasetConfig_for_AudioToBPEDataset(self):
         REMAP_ARGS = {'trim_silence': 'trim', 'labels': 'tokenizer'}
 
         result = assert_dataclass_signature_match(
-            audio_to_text.AudioToBPEDataset, configs.ASRDatasetConfig, ignore_args=IGNORE_ARGS, remap_args=REMAP_ARGS,
+            audio_to_text.AudioToBPEDataset,
+            configs.ASRDatasetConfig,
+            ignore_args=IGNORE_ARGS,
+            remap_args=REMAP_ARGS,
         )
         signatures_match, cls_subset, dataclass_subset = result
 

tests/collections/asr/test_asr_ctcencdec_model.py

@@ -279,7 +279,10 @@ def test_ASRDatasetConfig_for_AudioToCharDataset(self):
         REMAP_ARGS = {'trim_silence': 'trim'}
 
         result = assert_dataclass_signature_match(
-            audio_to_text.AudioToCharDataset, configs.ASRDatasetConfig, ignore_args=IGNORE_ARGS, remap_args=REMAP_ARGS,
+            audio_to_text.AudioToCharDataset,
+            configs.ASRDatasetConfig,
+            ignore_args=IGNORE_ARGS,
+            remap_args=REMAP_ARGS,
         )
         signatures_match, cls_subset, dataclass_subset = result
 

tests/collections/asr/test_asr_hybrid_rnnt_ctc_model_bpe.py

@@ -64,12 +64,18 @@ def hybrid_asr_model(test_data_dir):
 
     decoder = {
         '_target_': 'nemo.collections.asr.modules.RNNTDecoder',
-        'prednet': {'pred_hidden': model_defaults['pred_hidden'], 'pred_rnn_layers': 1,},
+        'prednet': {
+            'pred_hidden': model_defaults['pred_hidden'],
+            'pred_rnn_layers': 1,
+        },
     }
 
     joint = {
         '_target_': 'nemo.collections.asr.modules.RNNTJoint',
-        'jointnet': {'joint_hidden': 32, 'activation': 'relu',},
+        'jointnet': {
+            'joint_hidden': 32,
+            'activation': 'relu',
+        },
     }
 
     decoding = {'strategy': 'greedy_batch', 'greedy': {'max_symbols': 30}}
@@ -111,7 +117,8 @@ def hybrid_asr_model(test_data_dir):
 
 class TestEncDecHybridRNNTCTCBPEModel:
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.with_downloads()
     @pytest.mark.unit
@@ -125,7 +132,8 @@ def test_constructor(self, hybrid_asr_model):
 
     @pytest.mark.with_downloads()
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.unit
     def test_forward(self, hybrid_asr_model):
@@ -160,7 +168,8 @@ def test_forward(self, hybrid_asr_model):
 
     @pytest.mark.with_downloads()
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.unit
     def test_save_restore_artifact(self, hybrid_asr_model):
@@ -178,7 +187,8 @@ def test_save_restore_artifact(self, hybrid_asr_model):
 
     @pytest.mark.with_downloads()
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.unit
     def test_save_restore_artifact_spe(self, hybrid_asr_model, test_data_dir):
@@ -224,7 +234,8 @@ def test_save_restore_artifact_agg(self, hybrid_asr_model, test_data_dir):
 
     @pytest.mark.with_downloads()
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.unit
     def test_vocab_change(self, test_data_dir, hybrid_asr_model):
@@ -255,7 +266,8 @@ def test_vocab_change(self, test_data_dir, hybrid_asr_model):
 
     @pytest.mark.with_downloads()
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.unit
     def test_decoding_change(self, hybrid_asr_model):
@@ -309,7 +321,8 @@ def test_decoding_change(self, hybrid_asr_model):
         assert hybrid_asr_model.cur_decoder == "ctc"
 
     @pytest.mark.skipif(
-        not NUMBA_RNNT_LOSS_AVAILABLE, reason='RNNTLoss has not been compiled with appropriate numba version.',
+        not NUMBA_RNNT_LOSS_AVAILABLE,
+        reason='RNNTLoss has not been compiled with appropriate numba version.',
     )
     @pytest.mark.unit
     def test_decoding_type_change(self, hybrid_asr_model):