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 ModuleTransfer:
src: nn.Module
dest: nn.Module
verbose: int = 1
src_skip: List = field(default_factory=list)
dest_skip: List = field(default_factory=list)
raise_if_mismatch: bool = True
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest... | class_definition | 2,187 | 3,429 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | null | 8,600 |
class FakeRegNetVisslWrapper(nn.Module):
"""
Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
"""
def __init__(self, model: nn.Module):
super().__init__()
feature_blocks: List[Tuple[str, nn.Module]] = []
# - get the stem
featur... | class_definition | 3,432 | 4,312 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | null | 8,601 |
class NameToFromModelFuncMap(dict):
"""
A Dictionary with some additional logic to return a function that creates the correct original model.
"""
def convert_name_to_timm(self, x: str) -> str:
x_split = x.split("-")
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])
def __... | class_definition | 4,315 | 4,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | null | 8,602 |
class NameToOurModelFuncMap(dict):
"""
A Dictionary with some additional logic to return the correct hugging face RegNet class reference.
"""
def __getitem__(self, x: str) -> Callable[[], nn.Module]:
if "seer" in x and "in1k" not in x:
val = RegNetModel
else:
val... | class_definition | 4,964 | 5,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | null | 8,603 |
class TFRegNetConvLayer(keras.layers.Layer):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
stride: int = 1,
groups: int = 1,
activation: Optional[str] = "relu",
**kwargs,
):
super().__init__(**kwargs)
... | class_definition | 1,653 | 3,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,604 |
class TFRegNetEmbeddings(keras.layers.Layer):
"""
RegNet Embeddings (stem) composed of a single aggressive convolution.
"""
def __init__(self, config: RegNetConfig, **kwargs):
super().__init__(**kwargs)
self.num_channels = config.num_channels
self.embedder = TFRegNetConvLayer(
... | class_definition | 3,495 | 4,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,605 |
class TFRegNetShortCut(keras.layers.Layer):
"""
RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
def __init__(self, in_channels: int, out_channels: int, stride: int = 2, **kwargs):
super().__... | class_definition | 4,991 | 6,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,606 |
class TFRegNetSELayer(keras.layers.Layer):
"""
Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
"""
def __init__(self, in_channels: int, reduced_channels: int, **kwargs):
super().__init__(**kwargs)
self.pooler = keras.lay... | class_definition | 6,332 | 7,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,607 |
class TFRegNetXLayer(keras.layers.Layer):
"""
RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1, **kwargs):
super().__init__(**kwargs)
... | class_definition | 7,978 | 9,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,608 |
class TFRegNetYLayer(keras.layers.Layer):
"""
RegNet's Y layer: an X layer with Squeeze and Excitation.
"""
def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1, **kwargs):
super().__init__(**kwargs)
should_apply_shortcut = in_channels != out_cha... | class_definition | 9,977 | 11,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,609 |
class TFRegNetStage(keras.layers.Layer):
"""
A RegNet stage composed by stacked layers.
"""
def __init__(
self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, **kwargs
):
super().__init__(**kwargs)
layer = TFRegNetXLayer if confi... | class_definition | 11,947 | 13,047 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,610 |
class TFRegNetEncoder(keras.layers.Layer):
def __init__(self, config: RegNetConfig, **kwargs):
super().__init__(**kwargs)
self.stages = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TF... | class_definition | 13,050 | 14,904 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,611 |
class TFRegNetMainLayer(keras.layers.Layer):
config_class = RegNetConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedder = TFRegNetEmbeddings(config, name="embedder")
self.encoder = TFRegNetEncoder(config, name="encoder")
... | class_definition | 14,927 | 17,534 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,612 |
class TFRegNetPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RegNetConfig
base_model_prefix = "regnet"
main_input_name = "pixel_values"
@property
def... | class_definition | 17,537 | 17,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,613 |
class TFRegNetModel(TFRegNetPreTrainedModel):
def __init__(self, config: RegNetConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.regnet = TFRegNetMainLayer(config, name="regnet")
@unpack_inputs
@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
@a... | class_definition | 19,394 | 21,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,614 |
class TFRegNetForImageClassification(TFRegNetPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: RegNetConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.regnet = TFRegNetMainLayer(config, name="regnet")
... | class_definition | 21,433 | 24,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | null | 8,615 |
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
name2module: Dict[str, nn.Module] = field(default_factory=OrderedDict)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor, name: str):
has_not_submodule... | class_definition | 1,550 | 2,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py | null | 8,616 |
class FakeRegNetVisslWrapper(nn.Module):
"""
Fake wrapper for RegNet that mimics what vissl does without the need to pass a config file.
"""
def __init__(self, model: nn.Module):
super().__init__()
feature_blocks: List[Tuple[str, nn.Module]] = []
# - get the stem
featur... | class_definition | 2,564 | 3,444 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py | null | 8,617 |
class FakeRegNetParams(RegNetParams):
"""
Used to instantiace a RegNet model from classy vision with the same depth as the 10B one but with super small
parameters, so we can trace it in memory.
"""
def get_expanded_params(self):
return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.... | class_definition | 3,447 | 3,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py | null | 8,618 |
class Identity(nn.Module):
"""Identity function."""
@nn.compact
def __call__(self, x, **kwargs):
return x | class_definition | 4,173 | 4,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,619 |
class FlaxRegNetConvLayer(nn.Module):
out_channels: int
kernel_size: int = 3
stride: int = 1
groups: int = 1
activation: Optional[str] = "relu"
dtype: jnp.dtype = jnp.float32
def setup(self):
self.convolution = nn.Conv(
self.out_channels,
kernel_size=(self.ke... | class_definition | 4,302 | 5,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,620 |
class FlaxRegNetEmbeddings(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embedder = FlaxRegNetConvLayer(
self.config.embedding_size,
kernel_size=3,
stride=2,
activation=self.config.hidden_act,
dtype=... | class_definition | 5,472 | 6,279 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,621 |
class FlaxRegNetShortCut(nn.Module):
"""
RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
"""
out_channels: int
stride: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.c... | class_definition | 6,383 | 7,335 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,622 |
class FlaxRegNetSELayerCollection(nn.Module):
in_channels: int
reduced_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.conv_1 = nn.Conv(
self.reduced_channels,
kernel_size=(1, 1),
kernel_init=nn.initializers.variance_scaling(2.0, mode="fan_... | class_definition | 7,338 | 8,431 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,623 |
class FlaxRegNetSELayer(nn.Module):
"""
Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
"""
in_channels: int
reduced_channels: int
dtype: jnp.dtype = jnp.float32
def setup(self):
self.pooler = partial(nn.avg_pool, p... | class_definition | 8,434 | 9,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,624 |
class FlaxRegNetXLayerCollection(nn.Module):
config: RegNetConfig
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
groups = max(1, self.out_channels // self.config.groups_width)
self.layer = [
FlaxRegNetConvLayer(
self.ou... | class_definition | 9,286 | 10,484 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,625 |
class FlaxRegNetXLayer(nn.Module):
"""
RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
shou... | class_definition | 10,487 | 11,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,626 |
class FlaxRegNetYLayerCollection(nn.Module):
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
groups = max(1, self.out_channels // self.config.groups_width)
self.layer = [
FlaxRegNetConvLayer(
... | class_definition | 11,799 | 13,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,627 |
class FlaxRegNetYLayer(nn.Module):
"""
RegNet's Y layer: an X layer with Squeeze and Excitation.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 1
dtype: jnp.dtype = jnp.float32
def setup(self):
should_apply_shortcut = self.in_channels != self.out_... | class_definition | 13,172 | 14,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,628 |
class FlaxRegNetStageLayersCollection(nn.Module):
"""
A RegNet stage composed by stacked layers.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
layer = FlaxRegNetXLayer if self.c... | class_definition | 14,436 | 15,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,629 |
class FlaxRegNetStage(nn.Module):
"""
A RegNet stage composed by stacked layers.
"""
config: RegNetConfig
in_channels: int
out_channels: int
stride: int = 2
depth: int = 2
dtype: jnp.dtype = jnp.float32
def setup(self):
self.layers = FlaxRegNetStageLayersCollection(
... | class_definition | 15,816 | 16,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,630 |
class FlaxRegNetStageCollection(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
in_out_channels = zip(self.config.hidden_sizes, self.config.hidden_sizes[1:])
stages = [
FlaxRegNetStage(
self.config,
self.config.emb... | class_definition | 16,599 | 18,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,631 |
class FlaxRegNetEncoder(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.stages = FlaxRegNetStageCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_state: jnp.ndarray,
output_hidden_states: bool = False,
ret... | class_definition | 18,133 | 19,090 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,632 |
class FlaxRegNetPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RegNetConfig
base_model_prefix = "regnet"
main_input_name = "pixel_values"
module_class: ... | class_definition | 19,231 | 22,011 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,633 |
class FlaxRegNetModule(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embedder = FlaxRegNetEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxRegNetEncoder(self.config, dtype=self.dtype)
# Adaptive ave... | class_definition | 22,113 | 24,034 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,634 |
class FlaxRegNetModel(FlaxRegNetPreTrainedModel):
module_class = FlaxRegNetModule | class_definition | 24,177 | 24,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,635 |
class FlaxRegNetClassifierCollection(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype, name="1")
def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
return self.classifier(x) | class_definition | 25,253 | 25,560 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,636 |
class FlaxRegNetForImageClassificationModule(nn.Module):
config: RegNetConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.regnet = FlaxRegNetModule(config=self.config, dtype=self.dtype)
if self.config.num_labels > 0:
self.classifier = FlaxRegNetClassifierCollection(sel... | class_definition | 25,714 | 26,923 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,637 |
class FlaxRegNetForImageClassification(FlaxRegNetPreTrainedModel):
module_class = FlaxRegNetForImageClassificationModule | class_definition | 27,125 | 27,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/regnet/modeling_flax_regnet.py | null | 8,638 |
class Pop2PianoLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the Pop2Piano style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilo... | class_definition | 8,738 | 9,852 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,639 |
class Pop2PianoDenseActDense(nn.Module):
def __init__(self, config: Pop2PianoConfig):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
se... | class_definition | 10,085 | 10,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,640 |
class Pop2PianoDenseGatedActDense(nn.Module):
def __init__(self, config: Pop2PianoConfig):
super().__init__()
self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model,... | class_definition | 11,050 | 12,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,641 |
class Pop2PianoLayerFF(nn.Module):
def __init__(self, config: Pop2PianoConfig):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = Pop2PianoDenseGatedActDense(config)
else:
self.DenseReluDense = Pop2PianoDenseActDense(config)
self.layer_norm = P... | class_definition | 12,432 | 13,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,642 |
class Pop2PianoAttention(nn.Module):
def __init__(
self,
config: Pop2PianoConfig,
has_relative_attention_bias=False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_... | class_definition | 13,230 | 24,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,643 |
class Pop2PianoLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = Pop2PianoAttention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
... | class_definition | 24,582 | 25,953 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,644 |
class Pop2PianoLayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = Pop2PianoAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=co... | class_definition | 26,060 | 27,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,645 |
class Pop2PianoBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(
Pop2PianoLayerSelfAttention(
... | class_definition | 27,587 | 31,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,646 |
class Pop2PianoPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Pop2PianoConfig
base_model_prefix = "transformer"
is_parallelizable = False
supports_gradient_... | class_definition | 31,785 | 36,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,647 |
class Pop2PianoStack(Pop2PianoPreTrainedModel):
# Copied from transformers.models.t5.modeling_t5.T5Stack.__init__ with T5->Pop2Piano,t5->pop2piano
def __init__(self, config, embed_tokens=None):
super().__init__(config)
self.embed_tokens = embed_tokens
self.is_decoder = config.is_decoder... | class_definition | 36,857 | 54,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,648 |
class Pop2PianoConcatEmbeddingToMel(nn.Module):
"""Embedding Matrix for `composer` tokens."""
def __init__(self, config):
super().__init__()
self.embedding = nn.Embedding(num_embeddings=config.composer_vocab_size, embedding_dim=config.d_model)
def forward(self, feature, index_value, embedd... | class_definition | 54,217 | 54,777 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,649 |
class Pop2PianoForConditionalGeneration(Pop2PianoPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: Pop2PianoConfig):
super().__init__(config)
self.config = config
self.model... | class_definition | 55,764 | 72,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/modeling_pop2piano.py | null | 8,650 |
class Pop2PianoFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Pop2Piano 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 informat... | class_definition | 1,289 | 19,837 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/feature_extraction_pop2piano.py | null | 8,651 |
class Pop2PianoTokenizer(PreTrainedTokenizer):
"""
Constructs a Pop2Piano tokenizer. This tokenizer does not require training.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods... | class_definition | 2,014 | 32,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/tokenization_pop2piano.py | null | 8,652 |
class Pop2PianoProcessor(ProcessorMixin):
r"""
Constructs an Pop2Piano processor which wraps a Pop2Piano Feature Extractor and Pop2Piano Tokenizer into a single
processor.
[`Pop2PianoProcessor`] offers all the functionalities of [`Pop2PianoFeatureExtractor`] and [`Pop2PianoTokenizer`].
See the docs... | class_definition | 934 | 5,523 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/processing_pop2piano.py | null | 8,653 |
class Pop2PianoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Pop2PianoForConditionalGeneration`]. It is used
to instantiate a Pop2PianoForConditionalGeneration model according to the specified arguments, defining the model
architecture. Instantiating a ... | class_definition | 786 | 5,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pop2piano/configuration_pop2piano.py | null | 8,654 |
class MBart50Tokenizer(PreTrainedTokenizer):
"""
Construct a MBart50 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 reg... | class_definition | 1,518 | 16,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart50/tokenization_mbart50.py | null | 8,655 |
class MBart50TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" MBART tokenizer for mBART-50 (backed by HuggingFace's *tokenizers* library). Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedTo... | class_definition | 1,717 | 11,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart50/tokenization_mbart50_fast.py | null | 8,656 |
class TFRobertaPreLayerNormEmbeddings(keras.layers.Layer):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.padding_idx = 1
self.config = config
self.hidden_size = ... | class_definition | 2,187 | 6,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,657 |
class TFRobertaPreLayerNormPooler(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
... | class_definition | 6,499 | 7,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,658 |
class TFRobertaPreLayerNormSelfAttention(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) i... | class_definition | 7,608 | 14,470 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,659 |
class TFRobertaPreLayerNormSelfOutput(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
... | class_definition | 14,473 | 15,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,660 |
class TFRobertaPreLayerNormAttention(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFRobertaPreLayerNormSelfAttention(config, name="self")
self.dense_output = TFRobertaPreLayerNormSelfOutput(config, nam... | class_definition | 15,518 | 17,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,661 |
class TFRobertaPreLayerNormIntermediate(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dense = keras.layers.Dense(
... | class_definition | 17,913 | 19,318 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,662 |
class TFRobertaPreLayerNormOutput(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
... | class_definition | 19,321 | 20,365 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,663 |
class TFRobertaPreLayerNormLayer(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFRobertaPreLayerNormAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = ... | class_definition | 20,467 | 25,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,664 |
class TFRobertaPreLayerNormEncoder(keras.layers.Layer):
def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFRobertaPreLayerNormLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def ca... | class_definition | 25,390 | 28,516 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,665 |
class TFRobertaPreLayerNormMainLayer(keras.layers.Layer):
config_class = RobertaPreLayerNormConfig
def __init__(self, config, add_pooling_layer=True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.num_hidden_layers = config.... | class_definition | 28,539 | 38,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,666 |
class TFRobertaPreLayerNormPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RobertaPreLayerNormConfig
base_model_prefix = "roberta_prelayernorm" | class_definition | 39,116 | 39,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,667 |
class TFRobertaPreLayerNormModel(TFRobertaPreLayerNormPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roberta_prelayernorm = TFRobertaPreLayerNormMainLayer(config, name="roberta_prelayernorm")
@unpack_inputs
@add_start_docstr... | class_definition | 45,535 | 49,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,668 |
class TFRobertaPreLayerNormLMHead(keras.layers.Layer):
"""RobertaPreLayerNorm Head for masked language modeling."""
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.dense = keras.l... | class_definition | 49,639 | 52,081 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,669 |
class TFRobertaPreLayerNormForMaskedLM(TFRobertaPreLayerNormPreTrainedModel, TFMaskedLanguageModelingLoss):
# 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"lm_head.decoder.weight"]
# Copied f... | class_definition | 52,224 | 56,450 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,670 |
class TFRobertaPreLayerNormForCausalLM(TFRobertaPreLayerNormPreTrainedModel, TFCausalLanguageModelingLoss):
# 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"lm_head.decoder.weight"]
def __init... | class_definition | 56,630 | 63,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,671 |
class TFRobertaPreLayerNormClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer... | class_definition | 63,615 | 65,181 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,672 |
class TFRobertaPreLayerNormForSequenceClassification(
TFRobertaPreLayerNormPreTrainedModel, TFSequenceClassificationLoss
):
# 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"lm_head"]
def _... | class_definition | 65,435 | 69,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,673 |
class TFRobertaPreLayerNormForMultipleChoice(TFRobertaPreLayerNormPreTrainedModel, 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"lm_head"]
_keys_to_ignore_on_load_missing = [r"dr... | class_definition | 69,546 | 73,891 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,674 |
class TFRobertaPreLayerNormForTokenClassification(TFRobertaPreLayerNormPreTrainedModel, 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"lm_head"]
_keys_to_ignore_on... | class_definition | 74,152 | 78,076 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,675 |
class TFRobertaPreLayerNormForQuestionAnswering(TFRobertaPreLayerNormPreTrainedModel, 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"lm_head"]
def __init__(self, co... | class_definition | 78,395 | 83,039 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py | null | 8,676 |
class RobertaPreLayerNormConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RobertaPreLayerNormModel`] or a [`TFRobertaPreLayerNormModel`]. It is
used to instantiate a RoBERTa-PreLayerNorm model according to the specified arguments, defining the model architectu... | class_definition | 1,159 | 7,228 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py | null | 8,677 |
class RobertaPreLayerNormOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return Order... | class_definition | 7,347 | 7,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py | null | 8,678 |
class RobertaPreLayerNormEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.... | class_definition | 1,997 | 6,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,679 |
class RobertaPreLayerNormSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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.... | class_definition | 6,294 | 13,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,680 |
class RobertaPreLayerNormSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) ... | class_definition | 13,669 | 14,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,681 |
class RobertaPreLayerNormAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = RobertaPreLayerNormSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = RobertaPreLayerNormSelfOutput(config)
self.L... | class_definition | 14,192 | 16,550 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,682 |
class RobertaPreLayerNormIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
... | class_definition | 16,553 | 17,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,683 |
class RobertaPreLayerNormOutput(nn.Module):
def __init__(self, config):
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.Tensor... | class_definition | 17,275 | 17,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,684 |
class RobertaPreLayerNormLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = RobertaPreLayerNormAttention(config)
self.is_decoder = config.is_decoder
self.... | class_definition | 17,894 | 21,876 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,685 |
class RobertaPreLayerNormEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([RobertaPreLayerNormLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,... | class_definition | 21,975 | 25,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,686 |
class RobertaPreLayerNormPooler(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: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply t... | class_definition | 25,862 | 26,436 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,687 |
class RobertaPreLayerNormPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RobertaPreLayerNormConfig
base_model_prefix = "roberta_prelayernorm"
supports_gradient_c... | class_definition | 26,439 | 27,774 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,688 |
class RobertaPreLayerNormModel(RobertaPreLayerNormPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you ... | class_definition | 31,706 | 41,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,689 |
class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning(
"If you ... | class_definition | 41,555 | 48,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,690 |
class RobertaPreLayerNormForMaskedLM(RobertaPreLayerNormPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.__init__ with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->rober... | class_definition | 48,547 | 52,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,691 |
class RobertaPreLayerNormLMHead(nn.Module):
"""RobertaPreLayerNorm Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm... | class_definition | 53,016 | 54,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,692 |
class RobertaPreLayerNormForSequenceClassification(RobertaPreLayerNormPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False)... | class_definition | 54,356 | 58,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,693 |
class RobertaPreLayerNormForMultipleChoice(RobertaPreLayerNormPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta_prelayernorm = RobertaPreLayerNormModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.... | class_definition | 58,936 | 62,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,694 |
class RobertaPreLayerNormForTokenClassification(RobertaPreLayerNormPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False)
classifier_dropout = (
... | class_definition | 62,998 | 66,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,695 |
class RobertaPreLayerNormClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if confi... | class_definition | 66,340 | 67,125 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,696 |
class RobertaPreLayerNormForQuestionAnswering(RobertaPreLayerNormPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Line... | class_definition | 67,443 | 71,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py | null | 8,697 |
class FlaxRobertaPreLayerNormEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.... | class_definition | 6,208 | 8,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,698 |
class FlaxRobertaPreLayerNormSelfAttention(nn.Module):
config: RobertaPreLayerNormConfig
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_... | class_definition | 8,173 | 16,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,699 |
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