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 TFRemBertForCausalLM(TFRemBertPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if not config.is_decoder:
logger.warning("If you want to use `TFRemBertForCausalLM` as a standalo... | class_definition | 54,963 | 61,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 10,000 |
class TFRemBertForSequenceClassification(TFRemBertPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.rembert = TFRemBertMainLayer(config, name="re... | class_definition | 61,251 | 64,775 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 10,001 |
class TFRemBertForMultipleChoice(TFRemBertPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.rembert = TFRemBertMainLayer(config, name="rembert")
self.dropout = keras.layers.Dropout(rate=conf... | class_definition | 65,012 | 69,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 10,002 |
class TFRemBertForTokenClassification(TFRemBertPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.rembert = TFRemBertMainLayer(config, name="rembert"... | class_definition | 69,759 | 73,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 10,003 |
class TFRemBertForQuestionAnswering(TFRemBertPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.rembert = TFRemBertMainLayer(config, add_pooling_layer=... | class_definition | 73,386 | 77,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 10,004 |
class SegGptImageProcessor(BaseImageProcessor):
r"""
Constructs a SegGpt image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden ... | class_definition | 3,210 | 31,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/image_processing_seggpt.py | null | 10,005 |
class SegGptConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SegGptModel`]. It is used to instantiate a SegGPT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar... | class_definition | 783 | 6,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/configuration_seggpt.py | null | 10,006 |
class SegGptEncoderOutput(ModelOutput):
"""
Output type of [`SegGptEncoderOutput`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, patch_height, patch_width, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
hidden_states... | class_definition | 1,376 | 2,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,007 |
class SegGptImageSegmentationOutput(ModelOutput):
"""
Output type of [`SegGptImageSegmentationOutput`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
The loss value.
pred_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, ... | class_definition | 2,921 | 4,049 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,008 |
class SegGptPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | class_definition | 4,139 | 5,860 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,009 |
class SegGptEmbeddings(nn.Module):
"""
Construct the embeddings from patch, position embeddings for input and prompt.
"""
def __init__(self, config: SegGptConfig) -> None:
super().__init__()
self.mask_token = nn.Parameter(torch.zeros(1, 1, 1, config.hidden_size))
self.segment_t... | class_definition | 5,863 | 9,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,010 |
class SegGptAttention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) ... | class_definition | 9,636 | 16,309 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,011 |
class SegGptMlp(nn.Module):
def __init__(self, config):
super().__init__()
self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
self.act = ACT2FN[config.hidden_act]
def forward(self, hidden_states: torch.Tensor) -> t... | class_definition | 16,403 | 16,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,012 |
class SegGptDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> tor... | class_definition | 18,152 | 18,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,013 |
class SegGptLayer(nn.Module):
def __init__(self, config: SegGptConfig, drop_path_rate: float) -> None:
super().__init__()
self.attention = SegGptAttention(config)
self.mlp = SegGptMlp(config)
self.drop_path = SegGptDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
... | class_definition | 18,635 | 20,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,014 |
class SegGptEncoder(nn.Module):
def __init__(self, config: SegGptConfig) -> None:
super().__init__()
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
self.layers = nn.ModuleList([SegGptLayer(config, dpr[i]) for i in ran... | class_definition | 20,763 | 23,581 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,015 |
class SegGptLayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
width, channels) while channels_first corresponds to inputs with shape ... | class_definition | 23,685 | 25,159 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,016 |
class SegGptDecoderHead(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv2d(
config.decoder_hidden_size,
config.decoder_hidden_size,
kernel_size=3,
padding=1,
)
self.layernorm = SegGptLayerNorm(
... | class_definition | 25,162 | 26,040 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,017 |
class SegGptDecoder(nn.Module):
def __init__(self, config):
super().__init__()
self.decoder_embed = nn.Linear(
config.hidden_size * len(config.intermediate_hidden_state_indices),
config.patch_size**2 * config.decoder_hidden_size,
bias=True,
)
self.... | class_definition | 26,043 | 27,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,018 |
class SegGptPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SegGptConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
supports_gradient_check... | class_definition | 27,392 | 29,642 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,019 |
class SegGptModel(SegGptPreTrainedModel):
def __init__(self, config: SegGptConfig):
super().__init__(config)
self.config = config
self.embeddings = SegGptEmbeddings(config)
self.encoder = SegGptEncoder(config)
# Initialize weights and apply final processing
self.pos... | class_definition | 32,536 | 37,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,020 |
class SegGptLoss(nn.Module):
def __init__(self, config):
super().__init__()
self.beta = config.beta
self.patch_size = config.patch_size
def forward(
self,
prompt_masks: torch.FloatTensor,
pred_masks: torch.FloatTensor,
labels: torch.FloatTensor,
b... | class_definition | 39,091 | 40,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,021 |
class SegGptForImageSegmentation(SegGptPreTrainedModel):
def __init__(self, config: SegGptConfig):
super().__init__(config)
self.config = config
self.model = SegGptModel(config)
self.decoder = SegGptDecoder(config)
# Initialize weights and apply final processing
sel... | class_definition | 40,898 | 45,783 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seggpt/modeling_seggpt.py | null | 10,022 |
class Swin2SRConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin
Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 796 | 6,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/configuration_swin2sr.py | null | 10,023 |
class Swin2SREncoderOutput(ModelOutput):
"""
Swin2SR encoder's outputs, 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 of the last layer of the mode... | class_definition | 1,580 | 2,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,024 |
class Swin2SRDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> to... | class_definition | 5,207 | 5,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,025 |
class Swin2SREmbeddings(nn.Module):
"""
Construct the patch and optional position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = Swin2SRPatchEmbeddings(config)
num_patches = self.patch_embeddings.num_patches
if config.use_absolut... | class_definition | 5,691 | 6,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,026 |
class Swin2SRPatchEmbeddings(nn.Module):
def __init__(self, config, normalize_patches=True):
super().__init__()
num_channels = config.embed_dim
image_size, patch_size = config.image_size, config.patch_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) e... | class_definition | 6,669 | 7,987 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,027 |
class Swin2SRPatchUnEmbeddings(nn.Module):
r"""Image to Patch Unembedding"""
def __init__(self, config):
super().__init__()
self.embed_dim = config.embed_dim
def forward(self, embeddings, x_size):
batch_size, height_width, num_channels = embeddings.shape
embeddings = embed... | class_definition | 7,990 | 8,425 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,028 |
class Swin2SRPatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalizati... | class_definition | 8,525 | 10,818 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,029 |
class Swin2SRSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=[0, 0]):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_... | class_definition | 10,919 | 17,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,030 |
class Swin2SRSelfOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hid... | class_definition | 17,992 | 18,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,031 |
class Swin2SRAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size, pretrained_window_size=0):
super().__init__()
self.self = Swin2SRSelfAttention(
config=config,
dim=dim,
num_heads=num_heads,
window_size=window_size,
... | class_definition | 18,529 | 20,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,032 |
class Swin2SRIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermedia... | class_definition | 20,627 | 21,188 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,033 |
class Swin2SROutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self... | class_definition | 21,274 | 21,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,034 |
class Swin2SRLayer(nn.Module):
def __init__(
self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0, pretrained_window_size=0
):
super().__init__()
self.input_resolution = input_resolution
window_size, shift_size = self._compute_window_shift(
... | class_definition | 21,789 | 27,735 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,035 |
class Swin2SRStage(nn.Module):
"""
This corresponds to the Residual Swin Transformer Block (RSTB) in the original implementation.
"""
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, pretrained_window_size=0):
super().__init__()
self.config = config
... | class_definition | 27,738 | 30,293 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,036 |
class Swin2SREncoder(nn.Module):
def __init__(self, config, grid_size):
super().__init__()
self.num_stages = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.stages = nn.ModuleList(
... | class_definition | 30,296 | 33,065 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,037 |
class Swin2SRPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Swin2SRConfig
base_model_prefix = "swin2sr"
main_input_name = "pixel_values"
supports_gradient_c... | class_definition | 33,068 | 33,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,038 |
class Swin2SRModel(Swin2SRPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
if config.num_channels == 3 and config.num_channels_out == 3:
rgb_mean = (0.4488, 0.4371, 0.4040)
self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)... | class_definition | 35,761 | 40,324 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,039 |
class Upsample(nn.Module):
"""Upsample module.
Args:
scale (`int`):
Scale factor. Supported scales: 2^n and 3.
num_features (`int`):
Channel number of intermediate features.
"""
def __init__(self, scale, num_features):
super().__init__()
self.sc... | class_definition | 40,327 | 41,706 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,040 |
class UpsampleOneStep(nn.Module):
"""UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
Used in lightweight SR to save parameters.
Args:
scale (int):
Scale factor. Supported scales: 2^n and 3.
in_channels (int):
Ch... | class_definition | 41,709 | 42,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,041 |
class PixelShuffleUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1, 1)
self.activation = nn.LeakyReLU(inplace=True)
self.upsample = Upsample(config.upscale, num_features)
... | class_definition | 42,475 | 43,093 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,042 |
class NearestConvUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
if config.upscale != 4:
raise ValueError("The nearest+conv upsampler only supports an upscale factor of 4 at the moment.")
self.conv_before_upsample = nn.Conv2d(config.embed_dim, ... | class_definition | 43,096 | 44,464 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,043 |
class PixelShuffleAuxUpsampler(nn.Module):
def __init__(self, config, num_features):
super().__init__()
self.upscale = config.upscale
self.conv_bicubic = nn.Conv2d(config.num_channels, num_features, 3, 1, 1)
self.conv_before_upsample = nn.Conv2d(config.embed_dim, num_features, 3, 1,... | class_definition | 44,467 | 45,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,044 |
class Swin2SRForImageSuperResolution(Swin2SRPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.swin2sr = Swin2SRModel(config)
self.upsampler = config.upsampler
self.upscale = config.upscale
# Upsampler
num_features = 64
if self.upsam... | class_definition | 46,010 | 50,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/modeling_swin2sr.py | null | 10,045 |
class Swin2SRImageProcessor(BaseImageProcessor):
r"""
Constructs a Swin2SR image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the... | class_definition | 1,216 | 9,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin2sr/image_processing_swin2sr.py | null | 10,046 |
class MBartTokenizer(PreTrainedTokenizer):
"""
Construct an MBART tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `... | class_definition | 1,275 | 14,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart.py | null | 10,047 |
class MBartConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MBartModel`]. It is used to instantiate an MBART
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 1,101 | 7,759 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py | null | 10,048 |
class MBartOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | class_definition | 7,852 | 18,161 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/configuration_mbart.py | null | 10,049 |
class FlaxMBartAttention(nn.Module):
config: MBartConfig
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_h... | class_definition | 12,993 | 20,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,050 |
class FlaxMBartEncoderLayer(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.conf... | class_definition | 20,391 | 22,675 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,051 |
class FlaxMBartEncoderLayerCollection(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMBartEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
... | class_definition | 22,784 | 24,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,052 |
class FlaxMBartDecoderLayer(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxMBartAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.conf... | class_definition | 24,741 | 28,291 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,053 |
class FlaxMBartDecoderLayerCollection(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxMBartDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
... | class_definition | 28,400 | 31,120 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,054 |
class FlaxMBartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
config: MBartConfig
inner_dim: int
num_classes: int
pooler_dropout: float
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.inner_dim, dtype=self.... | class_definition | 31,225 | 32,276 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,055 |
class FlaxMBartEncoder(nn.Module):
config: MBartConfig
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.padding_idx = self.config... | class_definition | 32,279 | 35,158 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,056 |
class FlaxMBartDecoder(nn.Module):
config: MBartConfig
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.padding_idx = self.config... | class_definition | 35,161 | 38,422 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,057 |
class FlaxMBartModule(nn.Module):
config: MBartConfig
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_s... | class_definition | 38,515 | 40,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,058 |
class FlaxMBartPreTrainedModel(FlaxPreTrainedModel):
config_class = MBartConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: MBartConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float3... | class_definition | 40,959 | 55,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,059 |
class FlaxMBartModel(FlaxMBartPreTrainedModel):
config: MBartConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxMBartModule | class_definition | 55,618 | 55,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,060 |
class FlaxMBartForConditionalGenerationModule(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
... | class_definition | 56,017 | 58,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,061 |
class FlaxMBartForConditionalGeneration(FlaxMBartPreTrainedModel):
module_class = FlaxMBartForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MBartC... | class_definition | 58,743 | 66,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,062 |
class FlaxMBartForSequenceClassificationModule(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
num_labels: Optional[int] = None
def setup(self):
self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
self.classification_head = FlaxMBartClassificationHead(
... | class_definition | 68,449 | 71,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,063 |
class FlaxMBartForSequenceClassification(FlaxMBartPreTrainedModel):
module_class = FlaxMBartForSequenceClassificationModule
dtype = jnp.float32 | class_definition | 71,816 | 71,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,064 |
class FlaxMBartForQuestionAnsweringModule(nn.Module):
config: MBartConfig
dtype: jnp.dtype = jnp.float32
num_labels = 2
def setup(self):
self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
self.qa_outputs = nn.Dense(
self.num_labels, dtype=self.dtype, kernel_i... | class_definition | 72,241 | 74,507 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,065 |
class FlaxMBartForQuestionAnswering(FlaxMBartPreTrainedModel):
module_class = FlaxMBartForQuestionAnsweringModule
dtype = jnp.float32 | class_definition | 74,795 | 74,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | null | 10,066 |
class MBartTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" MBART tokenizer (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 [`PreTrainedTokenizerFast`] w... | class_definition | 1,469 | 10,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py | null | 10,067 |
class MBartLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and a... | class_definition | 3,089 | 3,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,068 |
class MBartScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_... | class_definition | 4,092 | 4,578 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,069 |
class MBartAttention(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,
co... | class_definition | 4,665 | 12,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,070 |
class MBartFlashAttention2(MBartAttention):
"""
MBart flash attention module. This module inherits from `MBartAttention` 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 | 12,150 | 18,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,071 |
class MBartSdpaAttention(MBartAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[to... | class_definition | 18,683 | 24,464 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,072 |
class MBartEncoderLayer(nn.Module):
def __init__(self, config: MBartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
... | class_definition | 24,607 | 27,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,073 |
class MBartDecoderLayer(nn.Module):
def __init__(self, config: MBartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MBART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 27,745 | 33,621 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,074 |
class MBartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_di... | class_definition | 33,717 | 34,504 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,075 |
class MBartPreTrainedModel(PreTrainedModel):
config_class = MBartConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MBartDecoderLayer", "MBartAttention"]
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
... | class_definition | 34,507 | 35,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,076 |
class MBartEncoder(MBartPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`MBartEncoderLayer`].
Args:
config: MBartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MBartConf... | class_definition | 44,458 | 53,392 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,077 |
class MBartDecoder(MBartPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MBartDecoderLayer`]
Args:
config: MBartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: MBartConfig, embed_tokens: Op... | class_definition | 53,395 | 68,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,078 |
class MBartModel(MBartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: MBartConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(config.... | class_definition | 68,209 | 73,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,079 |
class MBartForConditionalGeneration(MBartPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: MB... | class_definition | 73,878 | 80,102 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,080 |
class MBartForSequenceClassification(MBartPreTrainedModel):
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight"]
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = MBartModel(config)
self.classifi... | class_definition | 80,303 | 85,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,081 |
class MBartForQuestionAnswering(MBartPreTrainedModel):
_tied_weights_keys = ["model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = MB... | class_definition | 86,180 | 91,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,082 |
class MBartDecoderWrapper(MBartPreTrainedModel):
"""
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 | 91,714 | 92,158 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,083 |
class MBartForCausalLM(MBartPreTrainedModel, 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 = MBartDecoder... | class_definition | 92,294 | 101,644 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_mbart.py | null | 10,084 |
class TFMBartLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
# MBart is set up so that if padding_idx is specified then offset the embedding id... | class_definition | 4,063 | 5,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,085 |
class TFMBartAttention(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 | 5,284 | 12,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,086 |
class TFMBartEncoderLayer(keras.layers.Layer):
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMBartAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="... | class_definition | 12,862 | 16,532 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,087 |
class TFMBartDecoderLayer(keras.layers.Layer):
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMBartAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 16,535 | 23,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,088 |
class TFMBartPreTrainedModel(TFPreTrainedModel):
config_class = MBartConfig
base_model_prefix = "model" | class_definition | 23,326 | 23,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,089 |
class TFMBartEncoder(keras.layers.Layer):
config_class = MBartConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFMBartEncoderLayer`].
Args:
config: MBartConfig
"""
def __init__(self, config: MBartConfig, embed_tokens: Opt... | class_definition | 33,293 | 41,706 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,090 |
class TFMBartDecoder(keras.layers.Layer):
config_class = MBartConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMBartDecoderLayer`]
Args:
config: MBartConfig
embed_tokens: output embedding
"""
def __init__(self, config: MBartConfig... | class_definition | 41,729 | 54,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,091 |
class TFMBartMainLayer(keras.layers.Layer):
config_class = MBartConfig
def __init__(self, config: MBartConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model... | class_definition | 54,558 | 60,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,092 |
class TFMBartModel(TFMBartPreTrainedModel):
def __init__(self, config: MBartConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMBartMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
r... | class_definition | 60,377 | 64,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,093 |
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 | 64,331 | 65,137 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,094 |
class TFMBartForConditionalGeneration(TFMBartPreTrainedModel, 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, *... | class_definition | 65,318 | 74,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | null | 10,095 |
class GraphormerDataCollator:
def __init__(self, spatial_pos_max=20, on_the_fly_processing=False):
if not is_cython_available():
raise ImportError("Graphormer preprocessing needs Cython (pyximport)")
self.spatial_pos_max = spatial_pos_max
self.on_the_fly_processing = on_the_fly_... | class_definition | 2,699 | 6,086 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/collating_graphormer.py | null | 10,096 |
class GraphormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~GraphormerModel`]. It is used to instantiate an
Graphormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will y... | class_definition | 816 | 10,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/configuration_graphormer.py | null | 10,097 |
class LayerDropModuleList(nn.ModuleList):
"""
From:
https://github.com/facebookresearch/fairseq/blob/dd0079bde7f678b0cd0715cbd0ae68d661b7226d/fairseq/modules/layer_drop.py
A LayerDrop implementation based on [`torch.nn.ModuleList`]. LayerDrop as described in
https://arxiv.org/abs/1909.11556.
We... | class_definition | 5,115 | 6,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,098 |
class GraphormerGraphNodeFeature(nn.Module):
"""
Compute node features for each node in the graph.
"""
def __init__(self, config: GraphormerConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.num_atoms = config.num_atoms
self.atom_encoder = nn.E... | class_definition | 6,488 | 7,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/graphormer/modeling_graphormer.py | null | 10,099 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.