text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
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|---|---|---|---|---|---|---|---|
class DPTViTHybridEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config, ... | class_definition | 5,079 | 9,542 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,300 |
class DPTViTEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings.
"""
def __init__(self, config):
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.patch_embeddings = DPTViTPatchEmbeddings(config)
... | class_definition | 9,545 | 11,754 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,301 |
class DPTViTPatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image... | class_definition | 11,757 | 13,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,302 |
class DPTViTSelfAttention(nn.Module):
def __init__(self, config: DPTConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a mu... | class_definition | 13,135 | 15,978 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,303 |
class DPTViTSelfOutput(nn.Module):
"""
The residual connection is defined in DPTLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: DPTConfig) -> None:
super().__init__()
self.dense = nn.Linear(conf... | class_definition | 16,060 | 16,706 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,304 |
class DPTViTAttention(nn.Module):
def __init__(self, config: DPTConfig) -> None:
super().__init__()
self.attention = DPTViTSelfAttention(config)
self.output = DPTViTSelfOutput(config)
self.pruned_heads = set()
# Copied from transformers.models.vit.modeling_vit.ViTAttention.prune... | class_definition | 16,709 | 18,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,305 |
class DPTViTIntermediate(nn.Module):
def __init__(self, config: DPTConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | class_definition | 18,635 | 19,222 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,306 |
class DPTViTOutput(nn.Module):
def __init__(self, config: DPTConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.... | class_definition | 19,300 | 19,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,307 |
class DPTViTLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: DPTConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = DPTViTAttenti... | class_definition | 20,012 | 21,700 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,308 |
class DPTViTEncoder(nn.Module):
def __init__(self, config: DPTConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([DPTViTLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | class_definition | 21,816 | 23,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,309 |
class DPTReassembleStage(nn.Module):
"""
This class reassembles the hidden states of the backbone into image-like feature representations at various
resolutions.
This happens in 3 stages:
1. Map the N + 1 tokens to a set of N tokens, by taking into account the readout ([CLS]) token according to
... | class_definition | 23,746 | 28,824 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,310 |
class DPTReassembleLayer(nn.Module):
def __init__(self, config, channels, factor):
super().__init__()
# projection
hidden_size = _get_backbone_hidden_size(config)
self.projection = nn.Conv2d(in_channels=hidden_size, out_channels=channels, kernel_size=1)
# up/down sampling de... | class_definition | 29,035 | 29,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,311 |
class DPTFeatureFusionStage(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList()
for _ in range(len(config.neck_hidden_sizes)):
self.layers.append(DPTFeatureFusionLayer(config))
def forward(self, hidden_states):
# reversing the hid... | class_definition | 29,909 | 30,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,312 |
class DPTPreActResidualLayer(nn.Module):
"""
ResidualConvUnit, pre-activate residual unit.
Args:
config (`[DPTConfig]`):
Model configuration class defining the model architecture.
"""
def __init__(self, config):
super().__init__()
self.use_batch_norm = config.u... | class_definition | 30,798 | 32,637 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,313 |
class DPTFeatureFusionLayer(nn.Module):
"""Feature fusion layer, merges feature maps from different stages.
Args:
config (`[DPTConfig]`):
Model configuration class defining the model architecture.
align_corners (`bool`, *optional*, defaults to `True`):
The align_corner s... | class_definition | 32,640 | 34,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,314 |
class DPTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DPTConfig
base_model_prefix = "dpt"
main_input_name = "pixel_values"
supports_gradient_checkpointing... | class_definition | 34,063 | 34,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,315 |
class DPTModel(DPTPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
# vit encoder
if config.is_hybrid:
self.embeddings = DPTViTHybridEmbeddings(config)
else:
self.embeddings = DPTViTEmb... | class_definition | 36,896 | 40,732 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,316 |
class DPTViTPooler(nn.Module):
def __init__(self, config: DPTConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state correspon... | class_definition | 40,810 | 41,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,317 |
class DPTNeck(nn.Module):
"""
DPTNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as
input and produces another list of tensors as output. For DPT, it includes 2 stages:
* DPTReassembleStage
* DPTFeatureFusionStage.
Args:
conf... | class_definition | 41,355 | 43,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,318 |
class DPTDepthEstimationHead(nn.Module):
"""
Output head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples
the predictions to the input resolution after the first convolutional layer (details can be found in the paper's
supplementary material).
"""
... | class_definition | 43,441 | 44,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,319 |
class DPTForDepthEstimation(DPTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.backbone = None
if config.is_hybrid is False and (config.backbone_config is not None or config.backbone is not None):
self.backbone = load_backbone(config)
else:
... | class_definition | 45,102 | 50,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,320 |
class DPTSemanticSegmentationHead(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
features = config.fusion_hidden_size
self.head = nn.Sequential(
nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
nn.BatchNor... | class_definition | 50,619 | 51,425 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,321 |
class DPTAuxiliaryHead(nn.Module):
def __init__(self, config):
super().__init__()
features = config.fusion_hidden_size
self.head = nn.Sequential(
nn.Conv2d(features, features, kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(features),
nn.ReLU(),
... | class_definition | 51,428 | 51,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,322 |
class DPTForSemanticSegmentation(DPTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.dpt = DPTModel(config, add_pooling_layer=False)
# Neck
self.neck = DPTNeck(config)
# Segmentation head(s)
self.head = DPTSemanticSegmentationHead(config)... | class_definition | 52,115 | 57,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dpt/modeling_dpt.py | null | 6,323 |
class Wav2Vec2FeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Wav2Vec2 feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more informatio... | class_definition | 940 | 11,558 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/feature_extraction_wav2vec2.py | null | 6,324 |
class Wav2Vec2ProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {} | class_definition | 1,023 | 1,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/processing_wav2vec2.py | null | 6,325 |
class Wav2Vec2Processor(ProcessorMixin):
r"""
Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single
processor.
[`Wav2Vec2Processor`] offers all the functionalities of [`Wav2Vec2FeatureExtractor`] and [`PreTrainedTokenizer`].
See the docs... | class_definition | 1,106 | 7,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/processing_wav2vec2.py | null | 6,326 |
class TFWav2Vec2BaseModelOutput(ModelOutput):
"""
Output type of [`TFWav2Vec2BaseModelOutput`], with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last l... | class_definition | 1,602 | 3,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,327 |
class TFWav2Vec2GroupNorm(keras.layers.Layer):
"""
From tensorflow-addons https://www.tensorflow.org/addons/api_docs/python/tfa/layers/GroupNormalization
"""
def __init__(
self,
groups: int = 32,
axis: int = -1,
epsilon: float = 1e-3,
center: bool = True,
... | class_definition | 7,920 | 15,999 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,328 |
class TFWav2Vec2WeightNormConv1D(keras.layers.Conv1D):
"""Adapted from https://www.tensorflow.org/probability/api_docs/python/tfp/layers/weight_norm/WeightNorm"""
def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs):
super().__init__(
filters=filters,
ker... | class_definition | 16,002 | 18,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,329 |
class TFWav2Vec2NoLayerNormConvLayer(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
... | class_definition | 18,351 | 19,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,330 |
class TFWav2Vec2LayerNormConvLayer(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
... | class_definition | 19,466 | 20,922 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,331 |
class TFWav2Vec2GroupNormConvLayer(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
... | class_definition | 20,925 | 22,417 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,332 |
class TFWav2Vec2PositionalConvEmbedding(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.conv = TFWav2Vec2WeightNormConv1D(
filters=config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
... | class_definition | 22,420 | 23,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,333 |
class TFWav2Vec2SamePadLayer(keras.layers.Layer):
def __init__(self, num_conv_pos_embeddings, **kwargs):
super().__init__(**kwargs)
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def call(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = h... | class_definition | 23,598 | 23,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,334 |
class TFWav2Vec2FeatureEncoder(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
super().__init__(**kwargs)
if config.feat_extract_norm == "group":
conv_layers = [TFWav2Vec2GroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [
... | class_definition | 23,992 | 25,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,335 |
class TFWav2Vec2FeatureExtractor(TFWav2Vec2FeatureEncoder):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{se... | class_definition | 25,475 | 25,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,336 |
class TFWav2Vec2FeatureProjection(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.projection = keras.layers.Dense(
units... | class_definition | 25,882 | 27,291 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,337 |
class TFWav2Vec2Attention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
sup... | class_definition | 27,390 | 34,968 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,338 |
class TFWav2Vec2FeedForward(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.intermediate_dropout = keras.layers.Dropout(config.activation_dropout)
self.intermediate_dense = keras.layers.Dense(
units=config.intermediate_... | class_definition | 34,971 | 36,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,339 |
class TFWav2Vec2EncoderLayer(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.attention = TFWav2Vec2Attention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dr... | class_definition | 36,859 | 39,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,340 |
class TFWav2Vec2EncoderLayerStableLayerNorm(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.attention = TFWav2Vec2Attention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=conf... | class_definition | 39,328 | 41,770 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,341 |
class TFWav2Vec2Encoder(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed")
self.layer_norm = keras.layers.LayerNormalization(... | class_definition | 41,773 | 45,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,342 |
class TFWav2Vec2EncoderStableLayerNorm(keras.layers.Layer):
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.pos_conv_embed = TFWav2Vec2PositionalConvEmbedding(config, name="pos_conv_embed")
self.layer_norm = keras.layers.Laye... | class_definition | 45,197 | 48,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,343 |
class TFWav2Vec2MainLayer(keras.layers.Layer):
config_class = Wav2Vec2Config
def __init__(self, config: Wav2Vec2Config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.feature_extractor = TFWav2Vec2FeatureEncoder(config, name="feature_extractor")
self.feature_pro... | class_definition | 48,669 | 54,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,344 |
class TFWav2Vec2PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2Config
base_model_prefix = "wav2vec2"
main_input_name = "input_values"
@property
... | class_definition | 54,764 | 57,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,345 |
class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
@add_start_docstrings_to_model_forward(WAV_2_VEC... | class_definition | 63,921 | 66,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,346 |
class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
def __init__(self, config: Wav2Vec2Config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
self.dropout = keras.layers.Dropout(config.final_dropout)
self.l... | class_definition | 66,985 | 73,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,347 |
class TFWav2Vec2ForSequenceClassification(TFWav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2 = TFWav2Vec2MainLayer(config, name="wav2vec2")
self.num_layers = config.num_hidden_layers + 1
with tf.name_scope(self._name_scope()):
i... | class_definition | 73,521 | 78,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py | null | 6,348 |
class Wav2Vec2ForPreTrainingOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ForPreTraining`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the con... | class_definition | 2,804 | 5,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,349 |
class Wav2Vec2NoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 12,320 | 13,049 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,350 |
class Wav2Vec2LayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 13,052 | 14,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,351 |
class Wav2Vec2GroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 14,034 | 14,931 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,352 |
class Wav2Vec2PositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
... | class_definition | 14,934 | 16,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,353 |
class Wav2Vec2SamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self... | class_definition | 16,730 | 17,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,354 |
class Wav2Vec2FeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [Wav2Vec2GroupNormConvLayer(config, layer_id=0)] + [
Wav2Vec2NoLayerNor... | class_definition | 17,098 | 18,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,355 |
class Wav2Vec2FeatureExtractor(Wav2Vec2FeatureEncoder):
def __init__(self, config):
super().__init__(config)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self.__class__.__bases__[0... | class_definition | 18,835 | 19,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,356 |
class Wav2Vec2FeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_dr... | class_definition | 19,218 | 19,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,357 |
class Wav2Vec2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | class_definition | 19,960 | 27,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,358 |
class Wav2Vec2FlashAttention2(Wav2Vec2Attention):
"""
Wav2Vec2 flash attention module. This module inherits from `Wav2Vec2Attention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and ... | class_definition | 27,454 | 33,914 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,359 |
class Wav2Vec2SdpaAttention(Wav2Vec2Attention):
# Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with Bart->Wav2Vec2
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch... | class_definition | 33,917 | 39,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,360 |
class Wav2Vec2FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
... | class_definition | 39,968 | 40,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,361 |
class Wav2Vec2EncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = WAV2VEC2_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
... | class_definition | 40,941 | 42,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,362 |
class Wav2Vec2EncoderLayerStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = WAV2VEC2_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attent... | class_definition | 42,303 | 44,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,363 |
class Wav2Vec2Encoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = Wav2Vec2PositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.h... | class_definition | 44,034 | 47,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,364 |
class Wav2Vec2EncoderStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = Wav2Vec2PositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.D... | class_definition | 47,873 | 51,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,365 |
class Wav2Vec2GumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See `[CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
def __init__(self, config):
super().__init__()
self.num_groups = co... | class_definition | 51,881 | 55,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,366 |
class Wav2Vec2Adapter(nn.Module):
def __init__(self, config):
super().__init__()
# feature dim might need to be down-projected
if config.output_hidden_size != config.hidden_size:
self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
self.proj_layer_nor... | class_definition | 55,301 | 56,504 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,367 |
class Wav2Vec2AdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.output_hidden_size,
2 * config.output_hidden_size,
config.adapter_kernel_size,
stride=config.adapter_stride,
padding=1,
... | class_definition | 56,507 | 57,014 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,368 |
class Wav2Vec2AttnAdapterLayer(nn.Module):
def __init__(self, config):
"""
Implements adapter modules directly with 3D tensor weight as parameters and without using ModuleList to speed
up training throughput.
"""
super().__init__()
self.input_dim = config.adapter_attn... | class_definition | 57,017 | 57,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,369 |
class Wav2Vec2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2Config
base_model_prefix = "wav2vec2"
main_input_name = "input_values"
supports_gradien... | class_definition | 57,901 | 72,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,370 |
class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
def __init__(self, config: Wav2Vec2Config):
super().__init__(config)
self.config = config
self.feature_extractor = Wav2Vec2FeatureEncoder(config)
self.feature_projection = Wav2Vec2FeatureProjection(config)
# model only needs mask... | class_definition | 76,049 | 82,370 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,371 |
class Wav2Vec2ForPreTraining(Wav2Vec2PreTrainedModel):
def __init__(self, config: Wav2Vec2Config):
super().__init__(config)
self.wav2vec2 = Wav2Vec2Model(config)
self.dropout_features = nn.Dropout(config.feat_quantizer_dropout)
self.quantizer = Wav2Vec2GumbelVectorQuantizer(config)
... | class_definition | 82,485 | 93,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,372 |
class Wav2Vec2ForMaskedLM(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
warnings.warn(
"The class `Wav2Vec2ForMaskedLM` is deprecated. Please use `Wav2Vec2ForCTC` instead.", FutureWarning
)
self.wav2vec2 = Wav2Vec2Model(config)
s... | class_definition | 93,133 | 94,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,373 |
class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.wav2vec2 = Wav2Vec2Model(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size ... | class_definition | 95,232 | 102,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,374 |
class Wav2Vec2ForSequenceClassification(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Sequence classification does not support the use of Wav2Vec2 adapters (conf... | class_definition | 102,279 | 107,386 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,375 |
class Wav2Vec2ForAudioFrameClassification(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Audio frame classification does not support the use of Wav2Vec2 adapters ... | class_definition | 107,559 | 111,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,376 |
class AMSoftmaxLoss(nn.Module):
def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
super(AMSoftmaxLoss, self).__init__()
self.scale = scale
self.margin = margin
self.num_labels = num_labels
self.weight = nn.Parameter(torch.randn(input_dim, num_labels), require... | class_definition | 111,992 | 112,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,377 |
class TDNNLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
self.out_conv_dim = config.tdnn_dim[layer_id]
self.kernel_size = config.tdnn_kernel[layer_id]
... | class_definition | 112,871 | 114,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,378 |
class Wav2Vec2ForXVector(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2 = Wav2Vec2Model(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_w... | class_definition | 114,482 | 120,654 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py | null | 6,379 |
class FlaxWav2Vec2BaseModelOutput(ModelOutput):
"""
Output type of [`FlaxWav2Vec2BaseModelOutput`], with potential hidden states and attentions.
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the ... | class_definition | 1,488 | 3,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,380 |
class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput):
"""
Output type of [`FlaxWav2Vec2ForPreTrainingOutput`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`):
Total loss as the sum of the contrasti... | class_definition | 3,192 | 5,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,381 |
class FlaxWav2Vec2LayerNormConvLayer(nn.Module):
config: Wav2Vec2Config
layer_id: int = 0
dtype: jnp.dtype = jnp.float32
def setup(self):
self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1
self.out_conv_dim = self.config.conv_dim[self.layer_id]
s... | class_definition | 14,669 | 15,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,382 |
class FlaxConvWithWeightNorm(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
features=self.config.hidden_size,
kernel_size=(self.config.num_conv_pos_embeddings,),
kernel_init=jax.nn.initializers.he_normal(),... | class_definition | 15,760 | 17,362 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,383 |
class FlaxWav2Vec2PositionalConvEmbedding(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype)
self.activation = ACT2FN[self.config.feat_extract_activation]
self.num_pad_remove = 1 if sel... | class_definition | 17,365 | 18,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,384 |
class FlaxConvLayersCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
if self.config.feat_extract_norm == "layer":
self.layers = [
FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
... | class_definition | 18,138 | 19,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,385 |
class FlaxWav2Vec2FeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype)
def __call__(self, input_values, freeze_featu... | class_definition | 19,075 | 19,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,386 |
class FlaxWav2Vec2FeatureProjection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.projection = nn.Dense(
self.config.hidden_size,
kernel_ini... | class_definition | 19,650 | 20,450 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,387 |
class FlaxWav2Vec2Attention(nn.Module):
config: Wav2Vec2Config
embed_dim: int
num_heads: int
dropout: float = 0.0
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if sel... | class_definition | 20,453 | 23,618 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,388 |
class FlaxWav2Vec2FeedForward(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout)
self.intermediate_dense = nn.Dense(
self.config.intermediate_size,
kernel_ini... | class_definition | 23,621 | 24,968 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,389 |
class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxWav2Vec2Attention(
config=self.config,
embed_dim=self.config.hidden_size,
num_heads=self.config.num_attention_hea... | class_definition | 24,971 | 26,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,390 |
class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_hidde... | class_definition | 26,491 | 28,034 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,391 |
class FlaxWav2Vec2StableLayerNormEncoder(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype)
self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.... | class_definition | 28,037 | 30,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,392 |
class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
... | class_definition | 30,122 | 33,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,393 |
class FlaxWav2Vec2Adapter(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
# hidden_states require down-projection if feature dims don't match
if self.config.output_hidden_size != self.config.hidden_size:
self.proj = nn.Dense(
se... | class_definition | 33,708 | 34,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,394 |
class FlaxWav2Vec2AdapterLayer(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv = nn.Conv(
features=2 * self.config.output_hidden_size,
kernel_size=(self.config.adapter_kernel_size,),
strides=(self.config.adapter_stride... | class_definition | 34,832 | 35,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,395 |
class FlaxWav2Vec2AdapterLayersCollection(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = [
FlaxWav2Vec2AdapterLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_adapter_layers)
]
def... | class_definition | 35,485 | 35,959 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,396 |
class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2Config
base_model_prefix: str = "wav2vec2"
main_input_name = "input_values"
mod... | class_definition | 35,962 | 39,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,397 |
class FlaxWav2Vec2Module(nn.Module):
config: Wav2Vec2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.feature_extractor = FlaxWav2Vec2FeatureEncoder(self.config, dtype=self.dtype)
self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype)
self.... | class_definition | 39,407 | 44,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,398 |
class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel):
module_class = FlaxWav2Vec2Module | class_definition | 44,302 | 44,393 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py | null | 6,399 |
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