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class MaskFormerSwinModelOutputWithPooling(ModelOutput):
"""
Class for MaskFormerSwinModel's outputs that also contains the spatial dimensions of the hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-st... | class_definition | 1,419 | 3,446 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,200 |
class MaskFormerSwinBaseModelOutput(ModelOutput):
"""
Class for SwinEncoder's outputs.
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 model.
hidden_states (`tup... | class_definition | 3,460 | 5,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,201 |
class MaskFormerSwinEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = MaskFormerSwinPatchEmbeddings(config)
num_patches = self.patch_embeddings.num_patches
self.patch_grid =... | class_definition | 7,376 | 10,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,202 |
class MaskFormerSwinPatchEmbeddings(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, c... | class_definition | 10,692 | 12,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,203 |
class MaskFormerSwinPatchMerging(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`):
Norm... | class_definition | 12,955 | 15,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,204 |
class MaskFormerSwinDropPath(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... | class_definition | 15,338 | 15,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,205 |
class MaskFormerSwinSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
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_heads})"
)
... | class_definition | 15,926 | 20,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,206 |
class MaskFormerSwinSelfOutput(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:
... | class_definition | 20,910 | 21,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,207 |
class MaskFormerSwinAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
self.self = MaskFormerSwinSelfAttention(config, dim, num_heads, window_size)
self.output = MaskFormerSwinSelfOutput(config, dim)
self.pruned_heads = set()
def pr... | class_definition | 21,453 | 23,161 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,208 |
class MaskFormerSwinIntermediate(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.int... | class_definition | 23,260 | 23,828 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,209 |
class MaskFormerSwinOutput(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... | class_definition | 23,921 | 24,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,210 |
class MaskFormerSwinLayer(nn.Module):
def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0):
super().__init__()
self.shift_size = shift_size
self.window_size = config.window_size
self.input_resolution = input_resolution
self.layernorm_... | class_definition | 24,353 | 29,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,211 |
class MaskFormerSwinStage(nn.Module):
# Copied from transformers.models.swin.modeling_swin.SwinStage.__init__ with Swin->MaskFormerSwin
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
super().__init__()
self.config = config
self.dim = dim
... | class_definition | 29,405 | 31,604 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,212 |
class MaskFormerSwinEncoder(nn.Module):
# Copied from transformers.models.swin.modeling_swin.SwinEncoder.__init__ with Swin->MaskFormerSwin
def __init__(self, config, grid_size):
super().__init__()
self.num_layers = len(config.depths)
self.config = config
dpr = [x.item() for x in... | class_definition | 31,607 | 34,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,213 |
class MaskFormerSwinPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MaskFormerSwinConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
support... | class_definition | 34,928 | 35,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,214 |
class MaskFormerSwinModel(MaskFormerSwinPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.num_layers = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
self.embedd... | class_definition | 35,913 | 39,384 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,215 |
class MaskFormerSwinBackbone(MaskFormerSwinPreTrainedModel, BackboneMixin):
"""
MaskFormerSwin backbone, designed especially for the MaskFormer framework.
This classes reshapes `hidden_states` from (`batch_size, sequence_length, hidden_size)` to (`batch_size,
num_channels, height, width)`). It also add... | class_definition | 39,387 | 42,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer_swin.py | null | 4,216 |
class TrackedStateDict:
def __init__(self, to_track: Dict):
"""This class "tracks" a python dictionary by keeping track of which item is accessed.
Args:
to_track (Dict): The dictionary we wish to track
"""
self.to_track = to_track
self._seen: Set[str] = set()
... | class_definition | 1,629 | 2,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | null | 4,217 |
class Args:
"""Fake command line arguments needed by maskformer/detectron implementation"""
config_file: str | class_definition | 2,874 | 2,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | null | 4,218 |
class OriginalMaskFormerConfigToOursConverter:
def __call__(self, original_config: object) -> MaskFormerConfig:
model = original_config.MODEL
mask_former = model.MASK_FORMER
swin = model.SWIN
dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0])
id2label = ... | class_definition | 3,232 | 5,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | null | 4,219 |
class OriginalMaskFormerConfigToImageProcessorConverter:
def __call__(self, original_config: object) -> MaskFormerImageProcessor:
model = original_config.MODEL
model_input = original_config.INPUT
dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0])
return MaskForm... | class_definition | 5,693 | 6,444 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | null | 4,220 |
class OriginalMaskFormerCheckpointToOursConverter:
def __init__(self, original_model: nn.Module, config: MaskFormerConfig):
self.original_model = original_model
self.config = config
def pop_all(self, renamed_keys: List[Tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict):
... | class_definition | 6,447 | 25,799 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | null | 4,221 |
class MaskFormerSwinConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MaskFormerSwinModel`]. It is used to instantiate
a Donut model according to the specified arguments, defining the model architecture. Instantiating a configuration
wi... | class_definition | 904 | 7,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/configuration_maskformer_swin.py | null | 4,222 |
class MaskFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a MaskFormer image processor. The image processor can be used to prepare image(s) and optional targets
for the model.
This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
r... | class_definition | 12,543 | 58,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/image_processing_maskformer.py | null | 4,223 |
class DetrDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the DETR decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone through... | class_definition | 2,046 | 4,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,224 |
class MaskFormerPixelLevelModuleOutput(ModelOutput):
"""
MaskFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the
`encoder` and `decoder`. By default, the `encoder` is a MaskFormerSwin Transformer and the `decoder` is a Feature
Pyramid Network (FPN).
... | class_definition | 4,433 | 6,466 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,225 |
class MaskFormerPixelDecoderOutput(ModelOutput):
"""
MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state
and (optionally) the hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, wi... | class_definition | 6,480 | 7,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,226 |
class MaskFormerModelOutput(ModelOutput):
"""
Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits.
Args:
encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Last hidden states (... | class_definition | 7,965 | 11,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,227 |
class MaskFormerForInstanceSegmentationOutput(ModelOutput):
"""
Class for outputs of [`MaskFormerForInstanceSegmentation`].
This output can be directly passed to [`~MaskFormerImageProcessor.post_process_semantic_segmentation`] or or
[`~MaskFormerImageProcessor.post_process_instance_segmentation`] or
... | class_definition | 11,516 | 16,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,228 |
class DetrAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper.
Here, we add position embeddings to the queries and keys (as explained in the DETR paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0... | class_definition | 22,232 | 28,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,229 |
class DetrDecoderLayer(nn.Module):
def __init__(self, config: DetrConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetrAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_drop... | class_definition | 28,180 | 32,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,230 |
class DetrDecoder(nn.Module):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetrDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some small tweaks for DETR:
- object_queries and query_pos... | class_definition | 32,887 | 40,297 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,231 |
class MaskFormerHungarianMatcher(nn.Module):
"""This class computes an assignment between the labels and the predictions of the network.
For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more
predictions than labels. In this case, we do a 1-to-1 matching... | class_definition | 40,342 | 45,375 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,232 |
class MaskFormerLoss(nn.Module):
def __init__(
self,
num_labels: int,
matcher: MaskFormerHungarianMatcher,
weight_dict: Dict[str, float],
eos_coef: float,
):
"""
The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two ste... | class_definition | 45,428 | 56,093 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,233 |
class MaskFormerFPNConvLayer(nn.Module):
def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1):
"""
A basic module that executes conv - norm - in sequence used in MaskFormer.
Args:
in_features (`int`):
The number of input... | class_definition | 56,096 | 57,548 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,234 |
class MaskFormerFPNLayer(nn.Module):
def __init__(self, in_features: int, lateral_features: int):
"""
A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous
and backbone layer. Due to the spatial mismatch, the tensor coming from the pr... | class_definition | 57,551 | 58,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,235 |
class MaskFormerFPNModel(nn.Module):
def __init__(self, in_features: int, lateral_widths: List[int], feature_size: int = 256):
"""
Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it
creates a list of feature maps with the same featur... | class_definition | 58,643 | 59,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,236 |
class MaskFormerPixelDecoder(nn.Module):
def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs):
r"""
Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
Segmentation](https://arxiv.org/abs/2107.06278). It first runs... | class_definition | 59,980 | 61,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,237 |
class MaskFormerSinePositionEmbedding(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one used by the Attention is all you
need paper, generalized to work on images.
"""
def __init__(
self, num_pos_feats: int = 64, temperature: int = 10000, nor... | class_definition | 61,738 | 63,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,238 |
class PredictionBlock(nn.Module):
def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None:
super().__init__()
self.layers = [nn.Linear(in_dim, out_dim), activation]
# Maintain submodule indexing as if part of a Sequential block
for i, layer in enumerate(self.laye... | class_definition | 63,554 | 64,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,239 |
class MaskformerMLPPredictionHead(nn.Module):
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
"""
A classic Multi Layer Perceptron (MLP).
Args:
input_dim (`int`):
The input dimensions.
hidden_dim (`int`):
... | class_definition | 64,111 | 65,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,240 |
class MaskFormerPixelLevelModule(nn.Module):
def __init__(self, config: MaskFormerConfig):
"""
Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
Segmentation](https://arxiv.org/abs/2107.06278). It runs the input image through a backbone and a pixel
... | class_definition | 65,805 | 68,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,241 |
class MaskFormerTransformerModule(nn.Module):
"""
The MaskFormer's transformer module.
"""
def __init__(self, in_features: int, config: MaskFormerConfig):
super().__init__()
hidden_size = config.decoder_config.hidden_size
should_project = in_features != hidden_size
self.... | class_definition | 68,211 | 70,431 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,242 |
class MaskFormerPreTrainedModel(PreTrainedModel):
config_class = MaskFormerConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module: nn.Module):
xavier_std = self.config.init_xavier_std
std = self.config.init_std
if isinstance(module, Ma... | class_definition | 72,227 | 74,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,243 |
class MaskFormerModel(MaskFormerPreTrainedModel):
def __init__(self, config: MaskFormerConfig):
super().__init__(config)
self.pixel_level_module = MaskFormerPixelLevelModule(config)
self.transformer_module = MaskFormerTransformerModule(
in_features=self.pixel_level_module.encoder... | class_definition | 74,556 | 78,685 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,244 |
class MaskFormerForInstanceSegmentation(MaskFormerPreTrainedModel):
def __init__(self, config: MaskFormerConfig):
super().__init__(config)
self.model = MaskFormerModel(config)
hidden_size = config.decoder_config.hidden_size
# + 1 because we add the "null" class
self.class_pre... | class_definition | 78,688 | 90,773 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/modeling_maskformer.py | null | 4,245 |
class MaskFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MaskFormerModel`]. It is used to instantiate a
MaskFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yie... | class_definition | 1,009 | 10,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/maskformer/configuration_maskformer.py | null | 4,246 |
class XPathEmbeddings(nn.Module):
"""Construct the embeddings from xpath tags and subscripts.
We drop tree-id in this version, as its info can be covered by xpath.
"""
def __init__(self, config):
super(XPathEmbeddings, self).__init__()
self.max_depth = config.max_depth
self.xp... | class_definition | 1,622 | 3,573 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,247 |
class MarkupLMEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super(MarkupLMEmbeddings, self).__init__()
self.config = config
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, paddi... | class_definition | 4,343 | 8,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,248 |
class MarkupLMSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
d... | class_definition | 8,335 | 8,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,249 |
class MarkupLMIntermediate(nn.Module):
def __init__(self, config):
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:
self.in... | class_definition | 9,018 | 9,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,250 |
class MarkupLMOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 9,674 | 10,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,251 |
class MarkupLMPooler(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 taking the h... | class_definition | 10,353 | 10,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,252 |
class MarkupLMPredictionHeadTransform(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.... | class_definition | 11,020 | 11,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,253 |
class MarkupLMLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = MarkupLMPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.... | class_definition | 11,821 | 12,661 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,254 |
class MarkupLMOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = MarkupLMLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scor... | class_definition | 12,753 | 13,075 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,255 |
class MarkupLMSelfAttention(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.hidden_size... | class_definition | 13,169 | 20,519 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,256 |
class MarkupLMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = MARKUPLM_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = MarkupLMSelfO... | class_definition | 20,700 | 22,834 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,257 |
class MarkupLMLayer(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 = MarkupLMAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = ... | class_definition | 22,920 | 26,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,258 |
class MarkupLMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([MarkupLMLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states... | class_definition | 26,935 | 30,733 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,259 |
class MarkupLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MarkupLMConfig
base_model_prefix = "markuplm"
# Copied from transformers.models.bert.modeling_bert... | class_definition | 30,736 | 32,200 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,260 |
class MarkupLMModel(MarkupLMPreTrainedModel):
# Copied from transformers.models.clap.modeling_clap.ClapTextModel.__init__ with ClapText->MarkupLM
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MarkupLMEmbeddings(config... | class_definition | 35,995 | 42,099 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,261 |
class MarkupLMForQuestionAnswering(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with bert->markuplm, Bert->MarkupLM
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.markuplm ... | class_definition | 42,394 | 47,821 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,262 |
class MarkupLMForTokenClassification(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with bert->markuplm, Bert->MarkupLM
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.marku... | class_definition | 47,937 | 51,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,263 |
class MarkupLMForSequenceClassification(MarkupLMPreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with bert->markuplm, Bert->MarkupLM
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.... | class_definition | 52,181 | 57,126 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/modeling_markuplm.py | null | 4,264 |
class MarkupLMTokenizer(PreTrainedTokenizer):
r"""
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE). [`MarkupLMTokenizer`] can be used to
turn HTML strings into to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_tags_seq` and
`xpath_tags_seq`. This tokeniz... | class_definition | 6,591 | 70,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | null | 4,265 |
class MarkupLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MarkupLMModel`]. It is used to instantiate a
MarkupLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | class_definition | 788 | 7,310 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/configuration_markuplm.py | null | 4,266 |
class MarkupLMProcessor(ProcessorMixin):
r"""
Constructs a MarkupLM processor which combines a MarkupLM feature extractor and a MarkupLM tokenizer into a single
processor.
[`MarkupLMProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`MarkupLMFeatureEx... | class_definition | 856 | 6,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/processing_markuplm.py | null | 4,267 |
class MarkupLMFeatureExtractor(FeatureExtractionMixin):
r"""
Constructs a MarkupLM feature extractor. This can be used to get a list of nodes and corresponding xpaths from HTML
strings.
This feature extractor inherits from [`~feature_extraction_utils.PreTrainedFeatureExtractor`] which contains most
... | class_definition | 924 | 6,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/feature_extraction_markuplm.py | null | 4,268 |
class MarkupLMTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a MarkupLM tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
[`MarkupLMTokenizerFast`] can be used to turn HTML strings into to token-level `input_ids`, `attention_mask`,
`token_type_ids`, `xpath_tags_seq` and `xpath_tags_seq`.... | class_definition | 2,829 | 43,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/markuplm/tokenization_markuplm_fast.py | null | 4,269 |
class WordpieceTokenizer:
def __init__(self, vocab, unk_token="<unk>", max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, token):
chars = list(token)
if len(chars) > sel... | class_definition | 1,387 | 2,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/tokenization_cpmant.py | null | 4,270 |
class CpmAntTokenizer(PreTrainedTokenizer):
"""
Construct a CPMAnt tokenizer. Based on byte-level Byte-Pair-Encoding.
Args:
vocab_file (`str`):
Path to the vocabulary file.
bod_token (`str`, *optional*, defaults to `"<d>"`):
The beginning of document token.
e... | class_definition | 2,354 | 9,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/tokenization_cpmant.py | null | 4,271 |
class CpmAntConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CpmAntModel`]. It is used to instantiate an
CPMAnt model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 804 | 5,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/configuration_cpmant.py | null | 4,272 |
class CpmAntLayerNorm(nn.Module):
"""
We use Root Mean Square (RMS) Layer Normalization, please see https://arxiv.org/abs/1910.07467 for details."
"""
def __init__(self, config: CpmAntConfig):
super().__init__()
self.eps = config.eps
self.dim_norm = config.hidden_size
s... | class_definition | 1,352 | 2,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,273 |
class CpmAntAttention(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.dim_model = config.hidden_size
self.num_heads = config.num_attention_heads
self.dim_head = config.dim_head
self.project_q = nn.Linear(self.dim_model, self.num_heads * self.dim... | class_definition | 2,288 | 6,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,274 |
class CpmAntSelfAttentionBlock(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.layernorm_before_attention = CpmAntLayerNorm(config)
self.self_attention = CpmAntAttention(config)
if config.dropout_p:
self.dropout = torch.nn.Dropout(config.drop... | class_definition | 6,869 | 9,093 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,275 |
class CpmAntDenseGatedACT(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.w_0 = nn.Linear(config.hidden_size, config.dim_ff, bias=False)
self.w_1 = nn.Linear(config.hidden_size, config.dim_ff, bias=False)
self.act = torch.nn.GELU()
def forward(self,... | class_definition | 9,096 | 9,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,276 |
class CpmAntFeedForward(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.w_in = CpmAntDenseGatedACT(config)
if config.dropout_p is not None:
self.dropout = torch.nn.Dropout(config.dropout_p)
else:
self.dropout = None
self.... | class_definition | 9,836 | 10,614 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,277 |
class CpmAntFFNBlock(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.layernorm_before_ffn = CpmAntLayerNorm(config)
self.ffn = CpmAntFeedForward(config)
if config.dropout_p:
self.dropout = torch.nn.Dropout(config.dropout_p)
else:
... | class_definition | 10,617 | 11,477 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,278 |
class CpmAntTransformerBlock(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.self_att = CpmAntSelfAttentionBlock(config)
self.ffn = CpmAntFFNBlock(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
... | class_definition | 11,480 | 13,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,279 |
class CpmAntEncoder(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.num_layers = config.num_hidden_layers
self.layers = nn.ModuleList([CpmAntTransformerBlock(config) for ith in range(self.num_layers)])
self.output_layernorm = CpmAntLayerNorm(config)
... | class_definition | 13,451 | 16,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,280 |
class CpmAntIntermediate(nn.Module):
def __init__(self, config):
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:
self.inte... | class_definition | 16,455 | 17,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,281 |
class CpmAntSegmentPositionEmbedding(nn.Module):
def __init__(self, config: CpmAntConfig):
super().__init__()
self.num_heads = config.num_attention_heads
self.num_buckets = config.position_bias_num_buckets
self.max_distance = config.position_bias_max_distance
self.num_segmen... | class_definition | 17,025 | 21,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,282 |
class CpmAntOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
d... | class_definition | 21,140 | 21,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,283 |
class CpmAntPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CpmAntConfig
base_model_prefix = "cpmant"
def _init_weights(self, module):
"""Initialize the... | class_definition | 21,753 | 22,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,284 |
class CpmAntModel(CpmAntPreTrainedModel):
def __init__(self, config: CpmAntConfig):
super().__init__(config)
self.encoder = CpmAntEncoder(config)
self.segment_embedding = nn.Embedding(config.segment_types, config.hidden_size)
self.input_embedding = nn.Embedding(
config.vo... | class_definition | 24,991 | 31,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,285 |
class CpmAntForCausalLM(CpmAntPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: CpmAntConfig):
super().__init__(config)
self.cpmant = CpmAntModel(config)
# lm_head.weight is tied to cpmant.input_embedding.weight
self.lm_head =... | class_definition | 31,471 | 37,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cpmant/modeling_cpmant.py | null | 4,286 |
class TFLEDLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(self, input_... | class_definition | 4,036 | 4,686 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,287 |
class TFLEDEncoderSelfAttention(keras.layers.Layer):
def __init__(self, config, layer_id, **kwargs):
super().__init__(**kwargs)
self.config = config
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}... | class_definition | 4,815 | 41,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,288 |
class TFLEDEncoderAttention(keras.layers.Layer):
def __init__(self, config, layer_id, **kwargs):
super().__init__(**kwargs)
self.longformer_self_attn = TFLEDEncoderSelfAttention(config, layer_id=layer_id, name="longformer_self_attn")
self.output_dense = keras.layers.Dense(config.d_model, use... | class_definition | 41,336 | 42,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,289 |
class TFLEDDecoderAttention(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,
):
s... | class_definition | 42,820 | 50,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,290 |
class TFLEDEncoderLayer(keras.layers.Layer):
def __init__(self, config: LEDConfig, layer_id: int, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFLEDEncoderAttention(config, layer_id, name="self_attn")
self.self_attn_layer_norm = keras.layers.... | class_definition | 50,227 | 53,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,291 |
class TFLEDDecoderLayer(keras.layers.Layer):
def __init__(self, config: LEDConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFLEDDecoderAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 53,994 | 60,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,292 |
class TFLEDPreTrainedModel(TFPreTrainedModel):
config_class = LEDConfig
base_model_prefix = "led"
@property
def input_signature(self):
sig = super().input_signature
sig["global_attention_mask"] = tf.TensorSpec((None, None), tf.int32, name="global_attention_mask")
return sig | class_definition | 60,792 | 61,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,293 |
class TFLEDEncoderBaseModelOutput(ModelOutput):
"""
Base class for Longformer's outputs, with potential hidden states, local and global attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the... | class_definition | 61,249 | 64,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,294 |
class TFLEDSeq2SeqModelOutput(ModelOutput):
"""
Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidd... | class_definition | 64,417 | 69,024 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,295 |
class TFLEDSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
logits (`tf.Tensor` of shape `(batch_size, sequence_length,... | class_definition | 69,038 | 73,582 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,296 |
class TFLEDEncoder(keras.layers.Layer):
config_class = LEDConfig
"""
Transformer encoder consisting of *config.encoder_layers* self-attention layers. Each layer is a
[`TFLEDEncoderLayer`].
Args:
config: LEDConfig
"""
def __init__(self, config: LEDConfig, embed_tokens: Optional[kera... | class_definition | 80,384 | 92,528 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,297 |
class TFLEDDecoder(keras.layers.Layer):
config_class = LEDConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFLEDDecoderLayer`]
Args:
config: LEDConfig
embed_tokens: output embedding
"""
def __init__(self, config: LEDConfig, embed_to... | class_definition | 92,551 | 103,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,298 |
class TFLEDMainLayer(keras.layers.Layer):
config_class = LEDConfig
def __init__(self, config: LEDConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_model,
... | class_definition | 103,538 | 108,618 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/led/modeling_tf_led.py | null | 4,299 |
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