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
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
_keys_to_ignore_on_load_missing = [r"dro... | class_definition | 55,281 | 59,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,200 |
class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
def __init__(self, config: AlbertConfig, *i... | class_definition | 59,309 | 63,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,201 |
class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"]
_keys_to_ignore_on_load_missing = [r"dropout"]
... | class_definition | 64,137 | 68,846 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_tf_albert.py | null | 3,202 |
class VideoMAEFeatureExtractor(VideoMAEImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use VideoMAEImageProcessor instead.",
FutureWa... | class_definition | 821 | 1,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/feature_extraction_videomae.py | null | 3,203 |
class VideoMAEImageProcessor(BaseImageProcessor):
r"""
Constructs a VideoMAE image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` para... | class_definition | 1,836 | 16,507 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/image_processing_videomae.py | null | 3,204 |
class VideoMAEDecoderOutput(ModelOutput):
"""
Class for VideoMAEDecoder's outputs, with potential hidden states and attentions.
Args:
logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(torch.Fl... | class_definition | 1,671 | 2,983 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,205 |
class VideoMAEForPreTrainingOutput(ModelOutput):
"""
Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions.
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`torch.FloatTensor` of shape `(batch_size, patch_siz... | class_definition | 2,997 | 4,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,206 |
class VideoMAEEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = VideoMAEPatchEmbeddings(config)
self.num_patches = self.patch_embeddings.num_patches
# fixed sin-cos embeddi... | class_definition | 5,180 | 6,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,207 |
class VideoMAEPatchEmbeddings(nn.Module):
"""
Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels,
height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder.
The seq_len (the number of patche... | class_definition | 6,265 | 8,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,208 |
class VideoMAESelfAttention(nn.Module):
def __init__(self, config: VideoMAEConfig) -> 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 n... | class_definition | 8,650 | 12,023 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,209 |
class VideoMAESdpaSelfAttention(VideoMAESelfAttention):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output... | class_definition | 12,026 | 13,582 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,210 |
class VideoMAESelfOutput(nn.Module):
"""
The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.dense = nn... | class_definition | 13,669 | 14,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,211 |
class VideoMAEAttention(nn.Module):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.attention = VideoMAESelfAttention(config)
self.output = VideoMAESelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if... | class_definition | 14,413 | 16,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,212 |
class VideoMAESdpaAttention(VideoMAEAttention):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__(config)
self.attention = VideoMAESdpaSelfAttention(config) | class_definition | 16,200 | 16,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,213 |
class VideoMAEIntermediate(nn.Module):
def __init__(self, config: VideoMAEConfig) -> 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]
e... | class_definition | 16,479 | 17,073 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,214 |
class VideoMAEOutput(nn.Module):
def __init__(self, config: VideoMAEConfig) -> 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:... | class_definition | 17,151 | 17,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,215 |
class VideoMAELayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VIDEOM... | class_definition | 17,875 | 19,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,216 |
class VideoMAEEncoder(nn.Module):
def __init__(self, config: VideoMAEConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
sel... | class_definition | 19,708 | 21,644 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,217 |
class VideoMAEPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VideoMAEConfig
base_model_prefix = "videomae"
main_input_name = "pixel_values"
supports_gradien... | class_definition | 21,647 | 22,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,218 |
class VideoMAEModel(VideoMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = VideoMAEEmbeddings(config)
self.encoder = VideoMAEEncoder(config)
if config.use_mean_pooling:
self.layernorm = None
... | class_definition | 24,538 | 31,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,219 |
class VideoMAEDecoder(nn.Module):
def __init__(self, config, num_patches):
super().__init__()
decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2
decoder_config = deepcopy(config)
decoder_config.hidden_size = config.decoder_hidden_size
deco... | class_definition | 31,036 | 33,692 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,220 |
class VideoMAEForPreTraining(VideoMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.videomae = VideoMAEModel(config)
self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False)
self.mask_t... | class_definition | 33,846 | 42,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,221 |
class VideoMAEForVideoClassification(VideoMAEPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.videomae = VideoMAEModel(config)
# Classifier head
self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean... | class_definition | 42,488 | 49,291 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/modeling_videomae.py | null | 3,222 |
class VideoMAEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a
VideoMAE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | class_definition | 785 | 6,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/videomae/configuration_videomae.py | null | 3,223 |
class Starcoder2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Starcoder2Model`]. It is used to instantiate a
Starcoder2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yie... | class_definition | 857 | 10,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/configuration_starcoder2.py | null | 3,224 |
class Starcoder2MLP(nn.Module):
def __init__(self, config: Starcoder2Config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = nn.Linear(embed_dim, config.intermediate_size, bias=config.use_bias)
self.c_proj = nn.Linear(config.intermediate_size, embed_dim, bias=config.us... | class_definition | 2,827 | 3,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,225 |
class Starcoder2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Starcoder2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(con... | class_definition | 6,917 | 10,694 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,226 |
class Starcoder2DecoderLayer(nn.Module):
def __init__(self, config: Starcoder2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Starcoder2Attention(config=config, layer_idx=layer_idx)
self.mlp = Starcoder2MLP(config)
self.input... | class_definition | 10,697 | 12,785 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,227 |
class Starcoder2RotaryEmbedding(nn.Module):
def __init__(self, config: Starcoder2Config, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope... | class_definition | 12,788 | 15,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,228 |
class Starcoder2PreTrainedModel(PreTrainedModel):
config_class = Starcoder2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Starcoder2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa =... | class_definition | 17,035 | 17,973 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,229 |
class Starcoder2Model(Starcoder2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Starcoder2DecoderLayer`]
Args:
config: Starcoder2Config
"""
def __init__(self, config: Starcoder2Config):
super().__init__(config)
se... | class_definition | 22,792 | 35,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,230 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 35,716 | 35,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,231 |
class Starcoder2ForCausalLM(Starcoder2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Starcoder2Model(config)
self.vocab_size = config.vocab_size
... | class_definition | 35,781 | 40,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,232 |
class Starcoder2ForSequenceClassification(Starcoder2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Starcoder2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initi... | class_definition | 41,766 | 45,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,233 |
class Starcoder2ForTokenClassification(Starcoder2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Starcoder2Model(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout ... | class_definition | 45,855 | 49,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py | null | 3,234 |
class Starcoder2MLP(nn.Module):
def __init__(self, config: Starcoder2Config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = nn.Linear(embed_dim, config.intermediate_size, bias=config.use_bias)
self.c_proj = nn.Linear(config.intermediate_size, embed_dim, bias=config.us... | class_definition | 1,900 | 2,711 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,235 |
class Starcoder2Attention(MistralAttention):
def __init__(self, config: Starcoder2Config, layer_idx: Optional[int] = None):
super().__init__()
self.residual_dropout = config.residual_dropout
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.u... | class_definition | 2,714 | 6,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,236 |
class Starcoder2DecoderLayer(MistralDecoderLayer):
def __init__(self, config: Starcoder2Config, layer_idx: int):
super().__init__(self)
self.self_attn = Starcoder2Attention(config=config, layer_idx=layer_idx)
self.mlp = Starcoder2MLP(config)
self.input_layernorm = nn.LayerNorm(config... | class_definition | 6,038 | 6,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,237 |
class Starcoder2Model(MistralModel):
def __init__(self, config: Starcoder2Config):
super().__init__(config)
self.layers = nn.ModuleList(
[Starcoder2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = nn.LayerNorm(config.hidden_s... | class_definition | 6,569 | 11,067 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,238 |
class Starcoder2ForCausalLM(MistralForCausalLM):
pass | class_definition | 11,070 | 11,127 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,239 |
class Starcoder2ForSequenceClassification(MistralForSequenceClassification):
pass | class_definition | 11,130 | 11,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,240 |
class Starcoder2ForTokenClassification(MistralForTokenClassification):
pass | class_definition | 11,218 | 11,297 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py | null | 3,241 |
class VitsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VitsModel`]. It is used to instantiate a VITS
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 814 | 13,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py | null | 3,242 |
class VitsTokenizer(PreTrainedTokenizer):
"""
Construct a VITS tokenizer. Also supports MMS-TTS.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_fil... | class_definition | 1,450 | 9,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py | null | 3,243 |
class VitsModelOutput(ModelOutput):
"""
Describes the outputs for the VITS model, with potential hidden states and attentions.
Args:
waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
The final audio waveform predicted by the model.
sequence_lengths (`torc... | class_definition | 1,472 | 3,386 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,244 |
class VitsTextEncoderOutput(ModelOutput):
"""
Describes the outputs for the VITS text encoder model, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output ... | class_definition | 3,400 | 5,345 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,245 |
class VitsWaveNet(torch.nn.Module):
def __init__(self, config: VitsConfig, num_layers: int):
super().__init__()
self.hidden_size = config.hidden_size
self.num_layers = num_layers
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self... | class_definition | 16,292 | 19,620 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,246 |
class VitsPosteriorEncoder(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.out_channels = config.flow_size
self.conv_pre = nn.Conv1d(config.spectrogram_bins, config.hidden_size, 1)
self.wavenet = VitsWaveNet(config, num_layers=config.posterior_encoder_num... | class_definition | 19,623 | 20,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,247 |
class HifiGanResidualBlock(nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
super().__init__()
self.leaky_relu_slope = leaky_relu_slope
self.convs1 = nn.ModuleList(
[
nn.Conv1d(
channels,
... | class_definition | 20,580 | 22,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,248 |
class VitsHifiGan(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.num_kernels = len(config.resblock_kernel_sizes)
self.num_upsamples = len(config.upsample_rates)
self.conv_pre = nn.Conv1d(
config.flow_size,
... | class_definition | 22,712 | 26,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,249 |
class VitsResidualCouplingLayer(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.half_channels = config.flow_size // 2
self.conv_pre = nn.Conv1d(self.half_channels, config.hidden_size, 1)
self.wavenet = VitsWaveNet(config, num_layers=config.prior_encoder_n... | class_definition | 26,451 | 27,776 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,250 |
class VitsResidualCouplingBlock(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.flows = nn.ModuleList()
for _ in range(config.prior_encoder_num_flows):
self.flows.append(VitsResidualCouplingLayer(config))
def forward(self, inputs, padding_mask, gl... | class_definition | 27,779 | 28,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,251 |
class VitsDilatedDepthSeparableConv(nn.Module):
def __init__(self, config: VitsConfig, dropout_rate=0.0):
super().__init__()
kernel_size = config.duration_predictor_kernel_size
channels = config.hidden_size
self.num_layers = config.depth_separable_num_layers
self.dropout = n... | class_definition | 28,547 | 30,525 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,252 |
class VitsConvFlow(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.filter_channels = config.hidden_size
self.half_channels = config.depth_separable_channels // 2
self.num_bins = config.duration_predictor_flow_bins
self.tail_bound = config.duration_... | class_definition | 30,528 | 32,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,253 |
class VitsElementwiseAffine(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.channels = config.depth_separable_channels
self.translate = nn.Parameter(torch.zeros(self.channels, 1))
self.log_scale = nn.Parameter(torch.zeros(self.channels, 1))
def forwar... | class_definition | 32,509 | 33,305 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,254 |
class VitsStochasticDurationPredictor(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.speaker_embedding_size
filter_channels = config.hidden_size
self.conv_pre = nn.Conv1d(filter_channels, filter_channels, 1)
self.conv_proj = nn.Conv1d(filter_ch... | class_definition | 33,308 | 37,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,255 |
class VitsDurationPredictor(nn.Module):
def __init__(self, config):
super().__init__()
kernel_size = config.duration_predictor_kernel_size
filter_channels = config.duration_predictor_filter_channels
self.dropout = nn.Dropout(config.duration_predictor_dropout)
self.conv_1 = n... | class_definition | 37,712 | 39,336 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,256 |
class VitsAttention(nn.Module):
"""Multi-headed attention with relative positional representation."""
def __init__(self, config: VitsConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.dropout = config.attention_dropou... | class_definition | 39,339 | 47,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,257 |
class VitsFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.conv_1 = nn.Conv1d(config.hidden_size, config.ffn_dim, config.ffn_kernel_size)
self.conv_2 = nn.Conv1d(config.ffn_dim, config.hidden_size, config.ffn_kernel_size)
self.dropout = nn.Dropout(config.ac... | class_definition | 47,126 | 48,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,258 |
class VitsEncoderLayer(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.attention = VitsAttention(config)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.feed_forwar... | class_definition | 48,718 | 50,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,259 |
class VitsEncoder(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([VitsEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.layerdrop = config.layerdrop... | class_definition | 50,081 | 53,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,260 |
class VitsTextEncoder(nn.Module):
"""
Transformer encoder that uses relative positional representation instead of absolute positional encoding.
"""
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size,... | class_definition | 53,148 | 55,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,261 |
class VitsPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitsConfig
base_model_prefix = "vits"
main_input_name = "input_ids"
supports_gradient_checkpointing... | class_definition | 55,310 | 56,570 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,262 |
class VitsModel(VitsPreTrainedModel):
def __init__(self, config: VitsConfig):
super().__init__(config)
self.config = config
self.text_encoder = VitsTextEncoder(config)
self.flow = VitsResidualCouplingBlock(config)
self.decoder = VitsHifiGan(config)
if config.use_stoc... | class_definition | 59,080 | 66,510 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py | null | 3,263 |
class OlmoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-5):
"""
OlmoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 5,195 | 5,915 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,264 |
class OlmoeRotaryEmbedding(nn.Module):
def __init__(self, config: OlmoeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", co... | class_definition | 6,056 | 9,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,265 |
class OlmoeMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | class_definition | 11,200 | 11,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,266 |
class OlmoeAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: OlmoeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
log... | class_definition | 12,545 | 17,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,267 |
class OlmoeFlashAttention2(OlmoeAttention):
"""
OLMoE flash attention module. This module inherits from `OlmoeAttention` 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 deal with pa... | class_definition | 17,835 | 23,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,268 |
class OlmoeSdpaAttention(OlmoeAttention):
"""
OLMoE attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`OlmoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from... | class_definition | 23,402 | 28,336 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,269 |
class OlmoeSparseMoeBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_topk_prob = config.norm_topk_prob
self.gate = nn.Linear(config.hidden_size, self.num_experts, bias=Fals... | class_definition | 28,479 | 31,096 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,270 |
class OlmoeDecoderLayer(nn.Module):
def __init__(self, config: OlmoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = OLMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = OlmoeSparseMoeB... | class_definition | 31,099 | 35,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,271 |
class OlmoePreTrainedModel(PreTrainedModel):
config_class = OlmoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OlmoeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | class_definition | 36,331 | 37,254 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,272 |
class OlmoeModel(OlmoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OlmoeDecoderLayer`]
Args:
config: OlmoeConfig
"""
def __init__(self, config: OlmoeConfig):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 42,296 | 55,056 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,273 |
class OlmoeForCausalLM(OlmoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = OlmoeModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.voc... | class_definition | 55,059 | 61,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py | null | 3,274 |
class OlmoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OlmoeModel`]. It is used to instantiate an OLMoE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 689 | 9,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py | null | 3,275 |
class Speech2TextTokenizer(PreTrainedTokenizer):
"""
Construct an Speech2Text tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`):... | class_definition | 1,257 | 10,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py | null | 3,276 |
class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Speech2Text feature extractor.
This feature extractor inherits from [`Speech2TextFeatureExtractor`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.... | class_definition | 1,132 | 13,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py | null | 3,277 |
class Conv1dSubsampler(nn.Module):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config):
super(Conv1dSubsampler, self).__init__()
... | class_definition | 2,143 | 3,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,278 |
class Speech2TextSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = em... | class_definition | 3,556 | 6,971 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,279 |
class Speech2TextAttention(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 | 7,064 | 14,468 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,280 |
class Speech2TextEncoderLayer(nn.Module):
def __init__(self, config: Speech2TextConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = SPEECH_TO_TEXT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.enco... | class_definition | 14,660 | 17,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,281 |
class Speech2TextDecoderLayer(nn.Module):
def __init__(self, config: Speech2TextConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = SPEECH_TO_TEXT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.deco... | class_definition | 17,939 | 23,845 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,282 |
class Speech2TextPreTrainedModel(PreTrainedModel):
config_class = Speech2TextConfig
base_model_prefix = "model"
main_input_name = "input_features"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.... | class_definition | 23,848 | 25,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,283 |
class Speech2TextEncoder(Speech2TextPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`Speech2TextEncoderLayer`].
Args:
config: Speech2TextConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__... | class_definition | 32,671 | 39,732 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,284 |
class Speech2TextDecoder(Speech2TextPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Speech2TextDecoderLayer`]
Args:
config: Speech2TextConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: Speec... | class_definition | 39,735 | 51,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,285 |
class Speech2TextModel(Speech2TextPreTrainedModel):
def __init__(self, config: Speech2TextConfig):
super().__init__(config)
self.encoder = Speech2TextEncoder(config)
self.decoder = Speech2TextDecoder(config)
# Initialize weights and apply final processing
self.post_init()
... | class_definition | 52,137 | 57,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,286 |
class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: Speech2TextConfig):
super().__init__(config)
self.model = Speech2TextModel(config)
self.lm_head = nn... | class_definition | 57,911 | 63,537 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py | null | 3,287 |
class TFConv1dSubsampler(keras.layers.Layer):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config: Speech2TextConfig, **kwargs):
super()._... | class_definition | 4,116 | 6,362 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,288 |
class TFSpeech2TextSinusoidalPositionalEmbedding(keras.layers.Layer):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None, **kwargs):
super().__init__(**kwargs)
self.offset = 2
... | class_definition | 6,365 | 9,623 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,289 |
class TFSpeech2TextAttention(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,
):
... | class_definition | 9,721 | 17,302 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,290 |
class TFSpeech2TextEncoderLayer(keras.layers.Layer):
def __init__(self, config: Speech2TextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFSpeech2TextAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attenti... | class_definition | 17,305 | 21,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,291 |
class TFSpeech2TextDecoderLayer(keras.layers.Layer):
def __init__(self, config: Speech2TextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFSpeech2TextAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attent... | class_definition | 21,009 | 27,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,292 |
class TFSpeech2TextPreTrainedModel(TFPreTrainedModel):
config_class = Speech2TextConfig
base_model_prefix = "model"
main_input_name = "input_features"
_keys_to_ignore_on_load_unexpected = [r"encoder.embed_positions.weights"]
def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
... | class_definition | 27,862 | 28,997 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,293 |
class TFSpeech2TextEncoder(keras.layers.Layer):
config_class = Speech2TextConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFSpeech2TextEncoderLayer`].
Args:
config: Speech2TextConfig
"""
def __init__(self, config: Speech... | class_definition | 37,300 | 45,526 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,294 |
class TFSpeech2TextDecoder(keras.layers.Layer):
config_class = Speech2TextConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFSpeech2TextDecoderLayer`]
Args:
config: Speech2TextConfig
"""
def __init__(self, config: Speech2TextConfig, **kwarg... | class_definition | 45,549 | 57,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,295 |
class TFSpeech2TextMainLayer(keras.layers.Layer):
config_class = Speech2TextConfig
def __init__(self, config: Speech2TextConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.encoder = TFSpeech2TextEncoder(config, name="encoder")
self.decoder = TFSpeech2TextDe... | class_definition | 57,127 | 61,858 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,296 |
class TFSpeech2TextModel(TFSpeech2TextPreTrainedModel):
def __init__(self, config: Speech2TextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFSpeech2TextMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_... | class_definition | 62,019 | 65,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,297 |
class TFSpeech2TextForConditionalGeneration(TFSpeech2TextPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config: Speech2TextConfig):
super().__init__(config)
self.model = TFSpeech2TextMainLayer(config, name="model")
self.lm_head = keras.layers.Dense(self.config.vocab_size,... | class_definition | 65,870 | 74,311 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py | null | 3,298 |
class Speech2TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Speech2TextModel`]. It is used to instantiate a
Speech2Text model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 788 | 9,774 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/configuration_speech_to_text.py | null | 3,299 |
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