[Wav2Vec2] Fix None loss in doc examples (#19218)

* pass sampled_negative_indices parameter to the model to avoid getting a None loss
* concerns doc examples for Wav2Vec2ForPreTraining and Wav2Vec2ConformerForPreTraining

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1421,7 +1421,7 @@ class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
         ```python
         >>> import torch
         >>> from transformers import AutoFeatureExtractor, Wav2Vec2ForPreTraining
-        >>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
+        >>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices
         >>> from datasets import load_dataset
 
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
@@ -1432,9 +1432,19 @@ class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
 
         >>> # compute masked indices
         >>> batch_size, raw_sequence_length = input_values.shape
-        >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
-        >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)
-        >>> mask_time_indices = torch.tensor(mask_time_indices, device=input_values.device, dtype=torch.long)
+        >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length).item()
+        >>> mask_time_indices = _compute_mask_indices(
+        ...     shape=(batch_size, sequence_length), mask_prob=0.2, mask_length=2
+        ... )
+        >>> sampled_negative_indices = _sample_negative_indices(
+        ...     features_shape=(batch_size, sequence_length),
+        ...     num_negatives=model.config.num_negatives,
+        ...     mask_time_indices=mask_time_indices,
+        ... )
+        >>> mask_time_indices = torch.tensor(data=mask_time_indices, device=input_values.device, dtype=torch.long)
+        >>> sampled_negative_indices = torch.tensor(
+        ...     data=sampled_negative_indices, device=input_values.device, dtype=torch.long
+        ... )
 
         >>> with torch.no_grad():
         ...     outputs = model(input_values, mask_time_indices=mask_time_indices)
@@ -1448,7 +1458,9 @@ class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
 
         >>> # for contrastive loss training model should be put into train mode
         >>> model = model.train()
-        >>> loss = model(input_values, mask_time_indices=mask_time_indices).loss
+        >>> loss = model(
+        ...     input_values, mask_time_indices=mask_time_indices, sampled_negative_indices=sampled_negative_indices
+        ... ).loss
         ```"""
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -1469,7 +1469,10 @@ class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel):
         ```python
         >>> import torch
         >>> from transformers import AutoFeatureExtractor, Wav2Vec2ConformerForPreTraining
-        >>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import _compute_mask_indices
+        >>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
+        ...     _compute_mask_indices,
+        ...     _sample_negative_indices,
+        ... )
         >>> from datasets import load_dataset
 
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
@@ -1480,9 +1483,19 @@ class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel):
 
         >>> # compute masked indices
         >>> batch_size, raw_sequence_length = input_values.shape
-        >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length)
-        >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2)
-        >>> mask_time_indices = torch.tensor(mask_time_indices, device=input_values.device, dtype=torch.long)
+        >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length).item()
+        >>> mask_time_indices = _compute_mask_indices(
+        ...     shape=(batch_size, sequence_length), mask_prob=0.2, mask_length=2
+        ... )
+        >>> sampled_negative_indices = _sample_negative_indices(
+        ...     features_shape=(batch_size, sequence_length),
+        ...     num_negatives=model.config.num_negatives,
+        ...     mask_time_indices=mask_time_indices,
+        ... )
+        >>> mask_time_indices = torch.tensor(data=mask_time_indices, device=input_values.device, dtype=torch.long)
+        >>> sampled_negative_indices = torch.tensor(
+        ...     data=sampled_negative_indices, device=input_values.device, dtype=torch.long
+        ... )
 
         >>> with torch.no_grad():
         ...     outputs = model(input_values, mask_time_indices=mask_time_indices)
@@ -1496,7 +1509,9 @@ class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel):
 
         >>> # for contrastive loss training model should be put into train mode
         >>> model = model.train()
-        >>> loss = model(input_values, mask_time_indices=mask_time_indices).loss
+        >>> loss = model(
+        ...     input_values, mask_time_indices=mask_time_indices, sampled_negative_indices=sampled_negative_indices
+        ... ).loss
         ```"""
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict