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 WavLMNoLayerNormConvLayer(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 | 7,494 | 8,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,700 |
class WavLMLayerNormConvLayer(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 | 8,332 | 9,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,701 |
class WavLMGroupNormConvLayer(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 | 9,420 | 10,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,702 |
class WavLMPositionalConvEmbedding(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 | 10,431 | 12,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,703 |
class WavLMSamePadLayer(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.nu... | class_definition | 12,324 | 12,686 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,704 |
class WavLMFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [WavLMGroupNormConvLayer(config, layer_id=0)] + [
WavLMNoLayerNormConvLaye... | class_definition | 12,794 | 14,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,705 |
class WavLMFeatureExtractor(WavLMFeatureEncoder):
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].__na... | class_definition | 14,489 | 14,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,706 |
class WavLMFeatureProjection(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_dropo... | class_definition | 14,974 | 15,623 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,707 |
class WavLMAttention(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,
num_buckets: int = 320,
max_distance: int = 800,
has_relative_position_bias: boo... | class_definition | 15,626 | 22,695 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,708 |
class WavLMFeedForward(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):
se... | class_definition | 22,800 | 23,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,709 |
class WavLMEncoderLayer(nn.Module):
def __init__(self, config: WavLMConfig, has_relative_position_bias: bool = True):
super().__init__()
self.attention = WavLMAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_d... | class_definition | 23,770 | 25,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,710 |
class WavLMEncoderLayerStableLayerNorm(nn.Module):
def __init__(self, config: WavLMConfig, has_relative_position_bias: bool = True):
super().__init__()
self.attention = WavLMAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=con... | class_definition | 25,419 | 27,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,711 |
class WavLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = WavLMPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_... | class_definition | 27,009 | 30,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,712 |
class WavLMEncoderStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = WavLMPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout... | class_definition | 30,343 | 33,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,713 |
class WavLMGumbelVectorQuantizer(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 = config... | class_definition | 33,833 | 36,948 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,714 |
class WavLMAdapter(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_norm =... | class_definition | 37,049 | 38,246 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,715 |
class WavLMAdapterLayer(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 | 38,352 | 38,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,716 |
class WavLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = WavLMConfig
base_model_prefix = "wavlm"
main_input_name = "input_values"
supports_gradient_checkpo... | class_definition | 38,859 | 42,759 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,717 |
class WavLMModel(WavLMPreTrainedModel):
def __init__(self, config: WavLMConfig):
super().__init__(config)
self.config = config
self.feature_extractor = WavLMFeatureEncoder(config)
self.feature_projection = WavLMFeatureProjection(config)
# model only needs masking vector if m... | class_definition | 46,448 | 52,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,718 |
class WavLMForCTC(WavLMPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.wavlm = WavLMModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
... | class_definition | 53,039 | 59,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,719 |
class WavLMForSequenceClassification(WavLMPreTrainedModel):
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 WavLM adapters (config.add_ad... | class_definition | 60,041 | 65,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,720 |
class WavLMForAudioFrameClassification(WavLMPreTrainedModel):
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 WavLM adapters (config.a... | class_definition | 65,885 | 70,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,721 |
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 | 70,363 | 71,239 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,722 |
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 | 71,313 | 72,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,723 |
class WavLMForXVector(WavLMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wavlm = WavLMModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
self.layer_weights = nn.... | class_definition | 73,053 | 79,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/modeling_wavlm.py | null | 3,724 |
class TatoebaConverter:
"""
Convert Tatoeba-Challenge models to huggingface format.
Steps:
1. Convert numpy state dict to hf format (same code as OPUS-MT-Train conversion).
2. Rename opus model to huggingface format. This means replace each alpha3 code with an alpha2 code if a unique
... | class_definition | 1,290 | 13,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py | null | 3,725 |
class MarianSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weight =... | class_definition | 2,345 | 3,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,726 |
class MarianAttention(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,
c... | class_definition | 4,000 | 11,394 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,727 |
class MarianEncoderLayer(nn.Module):
def __init__(self, config: MarianConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_head... | class_definition | 11,499 | 14,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,728 |
class MarianDecoderLayer(nn.Module):
def __init__(self, config: MarianConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MARIAN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_head... | class_definition | 14,864 | 20,811 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,729 |
class MarianPreTrainedModel(PreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module: Union[nn.Linear, nn.Embedding, MarianSinusoidalPositionalEmbedding]):
std = self.config.init_std
if isinstance(mod... | class_definition | 20,814 | 21,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,730 |
class MarianEncoder(MarianPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MarianEncoderLayer`].
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: Maria... | class_definition | 29,741 | 37,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,731 |
class MarianDecoder(MarianPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MarianConfig, embed_token... | class_definition | 37,610 | 50,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,732 |
class MarianModel(MarianPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MarianConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
# We always use self.shared... | class_definition | 50,342 | 58,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,733 |
class MarianMTModel(MarianPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
"final_logits_bias",
"encoder.embed_positions.weight",
"decoder.embed_positions.weight",
]
_keys_to_ignore_on_save = ["model.encoder.embed_positions.weight... | class_definition | 58,732 | 69,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,734 |
class MarianDecoderWrapper(MarianPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | class_definition | 69,542 | 69,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,735 |
class MarianForCausalLM(MarianPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = MarianDeco... | class_definition | 70,127 | 79,484 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_marian.py | null | 3,736 |
class MarianConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarianModel`]. It is used to instantiate an
Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 1,095 | 7,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py | null | 3,737 |
class MarianOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
... | class_definition | 7,820 | 18,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/configuration_marian.py | null | 3,738 |
class OpusState:
def __init__(self, source_dir, eos_token_id=0):
npz_path = find_model_file(source_dir)
self.state_dict = np.load(npz_path)
cfg = load_config_from_state_dict(self.state_dict)
if cfg["dim-vocabs"][0] != cfg["dim-vocabs"][1]:
raise ValueError
if "Wpo... | class_definition | 17,085 | 25,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | null | 3,739 |
class FlaxMarianAttention(nn.Module):
config: MarianConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
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... | class_definition | 12,845 | 20,242 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,740 |
class FlaxMarianEncoderLayer(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.c... | class_definition | 20,342 | 22,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,741 |
class FlaxMarianEncoderLayerCollection(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMarianEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layer... | class_definition | 22,740 | 24,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,742 |
class FlaxMarianDecoderLayer(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMarianAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.c... | class_definition | 24,797 | 28,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,743 |
class FlaxMarianDecoderLayerCollection(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMarianDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layer... | class_definition | 28,461 | 31,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,744 |
class FlaxMarianEncoder(nn.Module):
config: MarianConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.max_source_positions = ... | class_definition | 31,187 | 33,155 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,745 |
class FlaxMarianDecoder(nn.Module):
config: MarianConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.max_target_positions = ... | class_definition | 33,158 | 35,507 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,746 |
class FlaxMarianModule(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.config.init... | class_definition | 35,510 | 37,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,747 |
class FlaxMarianPreTrainedModel(FlaxPreTrainedModel):
config_class = MarianConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: MarianConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.flo... | class_definition | 37,928 | 52,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,748 |
class FlaxMarianModel(FlaxMarianPreTrainedModel):
config: MarianConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxMarianModule | class_definition | 52,468 | 52,645 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,749 |
class FlaxMarianMTModule(nn.Module):
config: MarianConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxMarianModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.model... | class_definition | 52,758 | 55,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,750 |
class FlaxMarianMTModel(FlaxMarianPreTrainedModel):
module_class = FlaxMarianMTModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MarianConfig)
def decode(
sel... | class_definition | 55,441 | 63,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_flax_marian.py | null | 3,751 |
class MarianTokenizer(PreTrainedTokenizer):
r"""
Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | class_definition | 1,246 | 16,393 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py | null | 3,752 |
class TFMarianSinusoidalPositionalEmbedding(keras.layers.Layer):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
super().__init__(**kwargs)
if embedding_dim % 2 != 0:
raise NotImplement... | class_definition | 4,072 | 6,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,753 |
class TFMarianAttention(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,
):
super... | class_definition | 6,427 | 14,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,754 |
class TFMarianEncoderLayer(keras.layers.Layer):
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMarianAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, nam... | class_definition | 14,099 | 17,812 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,755 |
class TFMarianDecoderLayer(keras.layers.Layer):
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMarianAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 17,908 | 24,768 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,756 |
class TFMarianPreTrainedModel(TFPreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model" | class_definition | 24,771 | 24,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,757 |
class TFMarianEncoder(keras.layers.Layer):
config_class = MarianConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFMarianEncoderLayer`].
Args:
config: MarianConfig
"""
def __init__(self, config: MarianConfig, embed_tokens... | class_definition | 33,659 | 41,267 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,758 |
class TFMarianDecoder(keras.layers.Layer):
config_class = MarianConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens: output embedding
"""
def __init__(self, config: MarianC... | class_definition | 41,290 | 53,239 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,759 |
class TFMarianMainLayer(keras.layers.Layer):
config_class = MarianConfig
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_mo... | class_definition | 53,262 | 58,728 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,760 |
class TFMarianModel(TFMarianPreTrainedModel):
def __init__(self, config: MarianConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMarianMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
... | class_definition | 58,876 | 62,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,761 |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | class_definition | 62,782 | 63,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,762 |
class TFMarianMTModel(TFMarianPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwarg... | class_definition | 63,730 | 72,679 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py | null | 3,763 |
class ViltImageProcessor(BaseImageProcessor):
r"""
Constructs a ViLT 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` parameter in... | class_definition | 3,990 | 23,155 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py | null | 3,764 |
class ViltProcessor(ProcessorMixin):
r"""
Constructs a ViLT processor which wraps a BERT tokenizer and ViLT image processor into a single processor.
[`ViltProcessor`] offers all the functionalities of [`ViltImageProcessor`] and [`BertTokenizerFast`]. See the
docstring of [`~ViltProcessor.__call__`] and... | class_definition | 899 | 6,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/processing_vilt.py | null | 3,765 |
class ViltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 781 | 6,787 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/configuration_vilt.py | null | 3,766 |
class ViltFeatureExtractor(ViltImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use ViltImageProcessor instead.",
FutureWarning,
)... | class_definition | 809 | 1,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/feature_extraction_vilt.py | null | 3,767 |
class ViltForImagesAndTextClassificationOutput(ModelOutput):
"""
Class for outputs of [`ViltForImagesAndTextClassification`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
... | class_definition | 1,555 | 3,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,768 |
class ViltEmbeddings(nn.Module):
"""
Construct the text and patch embeddings.
Text embeddings are equivalent to BERT embeddings.
Patch embeddings are equivalent to ViT embeddings.
"""
def __init__(self, config):
super().__init__()
# text embeddings
self.text_embedding... | class_definition | 3,264 | 10,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,769 |
class TextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddi... | class_definition | 10,142 | 13,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,770 |
class ViltPatchEmbeddings(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_si... | class_definition | 13,044 | 14,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,771 |
class ViltSelfAttention(nn.Module):
def __init__(self, config):
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 multiple of the number ... | class_definition | 14,367 | 17,264 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,772 |
class ViltSelfOutput(nn.Module):
"""
The residual connection is defined in ViltLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViltConfig) -> None:
super().__init__()
self.dense = nn.Linear(conf... | class_definition | 17,347 | 17,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,773 |
class ViltAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = ViltSelfAttention(config)
self.output = ViltSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, in... | class_definition | 17,996 | 19,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,774 |
class ViltIntermediate(nn.Module):
def __init__(self, config: ViltConfig) -> 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 | 19,609 | 20,195 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,775 |
class ViltOutput(nn.Module):
def __init__(self, config: ViltConfig) -> 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.T... | class_definition | 20,274 | 20,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,776 |
class ViltLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = ViltAttention(config)
self... | class_definition | 20,806 | 22,394 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,777 |
class ViltEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViltLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 22,397 | 24,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,778 |
class ViltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViltConfig
base_model_prefix = "vilt"
supports_gradient_checkpointing = True
_no_split_modules = ["... | class_definition | 24,288 | 25,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,779 |
class ViltModel(ViltPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = ViltEmbeddings(config)
self.encoder = ViltEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.l... | class_definition | 32,929 | 39,367 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,780 |
class ViltPooler(nn.Module):
def __init__(self, config):
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 corresponding
... | class_definition | 39,370 | 39,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,781 |
class ViltForMaskedLM(ViltPreTrainedModel):
_tied_weights_keys = ["mlm_score.decoder.weight", "mlm_score.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.vilt = ViltModel(config)
self.mlm_score = ViltMLMHead(config)
# Initialize weights and apply final ... | class_definition | 40,050 | 45,779 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,782 |
class ViltPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.tran... | class_definition | 45,782 | 46,452 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,783 |
class ViltMLMHead(nn.Module):
def __init__(self, config, weight=None):
super().__init__()
self.config = config
self.transform = ViltPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros... | class_definition | 46,455 | 47,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,784 |
class ViltForQuestionAnswering(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vilt = ViltModel(config)
# Classifier head
self.classifier = nn.Sequential(
nn.Linear(config.hidden_size, config.hi... | class_definition | 47,440 | 51,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,785 |
class ViltForImageAndTextRetrieval(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.vilt = ViltModel(config)
# Classifier head
self.rank_output = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final processing
self.po... | class_definition | 51,888 | 55,378 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,786 |
class ViltForImagesAndTextClassification(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vilt = ViltModel(config)
# Classifier head
num_images = config.num_images
self.classifier = nn.Sequential(
... | class_definition | 55,589 | 61,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,787 |
class ViltForTokenClassification(ViltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vilt = ViltModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.... | class_definition | 61,819 | 64,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/modeling_vilt.py | null | 3,788 |
class FlaxElectraForPreTrainingOutput(ModelOutput):
"""
Output type of [`ElectraForPreTraining`].
Args:
logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
... | class_definition | 1,934 | 3,256 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,789 |
class FlaxElectraEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
... | class_definition | 6,348 | 8,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,790 |
class FlaxElectraSelfAttention(nn.Module):
config: ElectraConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.head_dim = self.config.hidden_size // self.config.num_attention_heads
if self.config.hidden_size % self.config.num_a... | class_definition | 8,286 | 16,183 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,791 |
class FlaxElectraSelfOutput(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dt... | class_definition | 16,282 | 17,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,792 |
class FlaxElectraAttention(nn.Module):
config: ElectraConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = FlaxElectraSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
self.output = FlaxElectraSelfOutput(self.config, dtype=self.dtype)
... | class_definition | 17,200 | 18,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,793 |
class FlaxElectraIntermediate(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 18,717 | 19,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,794 |
class FlaxElectraOutput(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=... | class_definition | 19,396 | 20,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,795 |
class FlaxElectraLayer(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxElectraAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
self.intermediate = FlaxElectraIntermediate(self.confi... | class_definition | 20,314 | 22,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,796 |
class FlaxElectraLayerCollection(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxElectraCheckpointLayer = remat(FlaxElectraLayer, static_argnums... | class_definition | 22,576 | 25,602 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,797 |
class FlaxElectraEncoder(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = FlaxElectraLayerCollection(
self.config,
dtype=self.dtype,
gradient_ch... | class_definition | 25,698 | 26,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,798 |
class FlaxElectraGeneratorPredictions(nn.Module):
config: ElectraConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dense = nn.Dense(self.config.embedding_size, dtype=self.dtype)
def __call__(s... | class_definition | 26,947 | 27,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/electra/modeling_flax_electra.py | null | 3,799 |
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