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
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|---|---|---|---|---|---|---|---|
class ViTHybridForImageClassification(ViTHybridPreTrainedModel):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.vit = ViTHybridModel(config, add_pooling_layer=False)
# Classifier head
self.classifier = ... | class_definition | 28,858 | 32,562 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,400 |
class MultiScaleDeformableAttentionFunction(Function):
@staticmethod
def forward(
context,
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
im2col_step,
):
context.im2col_step = im2col_step
ou... | class_definition | 2,569 | 4,013 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,401 |
class DetaDecoderOutput(ModelOutput):
"""
Base class for outputs of the DetaDecoder. This class adds two attributes to
BaseModelOutputWithCrossAttentions, namely:
- a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer)
- a stacked tensor of intermediate refer... | class_definition | 4,524 | 7,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,402 |
class DetaModelOutput(ModelOutput):
"""
Base class for outputs of the Deformable DETR encoder-decoder model.
Args:
init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
Initial reference points sent through the Transformer decoder.
last_hidden_sta... | class_definition | 7,251 | 12,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,403 |
class DetaObjectDetectionOutput(ModelOutput):
"""
Output type of [`DetaForObjectDetection`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class predic... | class_definition | 12,417 | 19,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,404 |
class DetaFrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than
torchvision.models.resnet[18,34,50,101] produce nans.
"""
def __init__(s... | class_definition | 19,488 | 21,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,405 |
class DetaBackboneWithPositionalEncodings(nn.Module):
"""
Backbone model with positional embeddings.
nn.BatchNorm2d layers are replaced by DetaFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
backbone = load_backbone(config)
with torch... | class_definition | 21,821 | 23,573 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,406 |
class DetaSinePositionEmbedding(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, embedding_dim=64, temperature=10000, normalize=False, scale=None):
... | class_definition | 23,576 | 25,317 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,407 |
class DetaLearnedPositionEmbedding(nn.Module):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, embedding_dim=256):
super().__init__()
self.row_embeddings = nn.Embedding(50, embedding_dim)
self.column_embeddings = nn.Embedding(50, e... | class_definition | 25,320 | 26,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,408 |
class DetaMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: DetaConfig, num_heads: int, n_points: int):
super().__init__()
kernel_loaded = MultiScaleDeformableAttention is not None
if is_... | class_definition | 28,853 | 35,428 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,409 |
class DetaMultiheadAttention(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 Deformable DETR paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
... | class_definition | 35,431 | 40,726 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,410 |
class DetaEncoderLayer(nn.Module):
def __init__(self, config: DetaConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = DetaMultiscaleDeformableAttention(
config,
num_heads=config.encoder_attention_heads,
n_points=config.encoder_n_po... | class_definition | 40,729 | 44,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,411 |
class DetaDecoderLayer(nn.Module):
def __init__(self, config: DetaConfig):
super().__init__()
self.embed_dim = config.d_model
# self-attention
self.self_attn = DetaMultiheadAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 44,472 | 49,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,412 |
class DetaPreTrainedModel(PreTrainedModel):
config_class = DetaConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
_no_split_modules = [r"DetaBackboneWithPositionalEncodings", r"DetaEncoderLayer", r"DetaDecoderLayer"]
supports_gradient_checkpointing = True
def _init_weights(self... | class_definition | 49,200 | 50,766 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,413 |
class DetaEncoder(DetaPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a
[`DetaEncoderLayer`].
The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers.
Args:
config: De... | class_definition | 54,081 | 60,378 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,414 |
class DetaDecoder(DetaPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`DetaDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some tweaks for Deformable DETR:
- `position_emb... | class_definition | 60,381 | 69,080 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,415 |
class DetaModel(DetaPreTrainedModel):
def __init__(self, config: DetaConfig):
super().__init__(config)
if config.two_stage:
requires_backends(self, ["torchvision"])
# Create backbone with positional encoding
self.backbone = DetaBackboneWithPositionalEncodings(config)
... | class_definition | 69,301 | 89,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,416 |
class DetaForObjectDetection(DetaPreTrainedModel):
# When using clones, all layers > 0 will be clones, but layer 0 *is* required
_tied_weights_keys = [r"bbox_embed\.\d+", r"class_embed\.\d+"]
# We can't initialize the model on meta device as some weights are modified during the initialization
_no_split_... | class_definition | 89,601 | 101,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,417 |
class DetaLoss(nn.Module):
"""
This class computes the losses for `DetaForObjectDetection`. The process happens in two steps: 1) we compute
hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched
ground-truth / prediction (supervised class and bo... | class_definition | 103,575 | 112,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,418 |
class DetaMLPPredictionHead(nn.Module):
"""
Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates,
height and width of a bounding box w.r.t. an image.
Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py
"""
def... | class_definition | 112,316 | 113,089 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,419 |
class DetaHungarianMatcher(nn.Module):
"""
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matchi... | class_definition | 113,092 | 117,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,420 |
class DetaMatcher:
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will
have exactly zero or one matches; each ground-truth element may be matched to zero or more predicted elements.
The matching is determined by the MxN match_quality_matr... | class_definition | 120,130 | 126,373 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,421 |
class DetaStage2Assigner(nn.Module):
def __init__(self, num_queries, max_k=4):
super().__init__()
self.positive_fraction = 0.25
self.bg_label = 400 # number > 91 to filter out later
self.batch_size_per_image = num_queries
self.proposal_matcher = DetaMatcher(thresholds=[0.6],... | class_definition | 129,352 | 132,915 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,422 |
class DetaStage1Assigner(nn.Module):
def __init__(self, t_low=0.3, t_high=0.7, max_k=4):
super().__init__()
self.positive_fraction = 0.5
self.batch_size_per_image = 256
self.k = max_k
self.t_low = t_low
self.t_high = t_high
self.anchor_matcher = DetaMatcher(
... | class_definition | 133,078 | 135,662 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/modeling_deta.py | null | 10,423 |
class DetaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DetaModel`]. It is used to instantiate a DETA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 832 | 13,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/configuration_deta.py | null | 10,424 |
class DetaImageProcessor(BaseImageProcessor):
r"""
Constructs a Deformable DETR image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults ... | class_definition | 17,424 | 54,890 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/deta/image_processing_deta.py | null | 10,425 |
class RetriBertTokenizer(PreTrainedTokenizer):
r"""
Constructs a RetriBERT tokenizer.
[`RetriBertTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting
and wordpiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main... | class_definition | 1,512 | 12,007 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/tokenization_retribert.py | null | 10,426 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 12,010 | 18,758 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/tokenization_retribert.py | null | 10,427 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 18,761 | 20,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/tokenization_retribert.py | null | 10,428 |
class RetriBertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" RetriBERT tokenizer (backed by HuggingFace's *tokenizers* library).
[`RetriBertTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
This tokenizer ... | class_definition | 1,014 | 7,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/tokenization_retribert_fast.py | null | 10,429 |
class RetriBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RetriBertModel`]. It is used to instantiate a
RetriBertModel model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will y... | class_definition | 823 | 5,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/configuration_retribert.py | null | 10,430 |
class RetriBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RetriBertConfig
load_tf_weights = None
base_model_prefix = "retribert"
def _init_weights(self... | class_definition | 1,094 | 2,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py | null | 10,431 |
class RetriBertModel(RetriBertPreTrainedModel):
def __init__(self, config: RetriBertConfig) -> None:
super().__init__(config)
self.projection_dim = config.projection_dim
self.bert_query = BertModel(config)
self.bert_doc = None if config.share_encoders else BertModel(config)
... | class_definition | 3,048 | 9,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py | null | 10,432 |
class Speech2Text2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Speech2Text2ForCausalLM`]. It is used to
instantiate an Speech2Text2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defa... | class_definition | 790 | 6,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py | null | 10,433 |
class Speech2Text2Tokenizer(PreTrainedTokenizer):
"""
Constructs a Speech2Text2Tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`)... | class_definition | 1,439 | 8,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py | null | 10,434 |
class Speech2Text2SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = e... | class_definition | 1,402 | 4,818 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,435 |
class Speech2Text2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | class_definition | 4,821 | 12,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,436 |
class Speech2Text2DecoderLayer(nn.Module):
def __init__(self, config: Speech2Text2Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Speech2Text2Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropo... | class_definition | 12,230 | 18,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,437 |
class Speech2Text2PreTrainedModel(PreTrainedModel):
config_class = Speech2Text2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.d... | class_definition | 18,023 | 18,669 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,438 |
class Speech2Text2Decoder(Speech2Text2PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Speech2Text2DecoderLayer`]
Args:
config: Speech2Text2Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: S... | class_definition | 19,566 | 31,681 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,439 |
class Speech2Text2DecoderWrapper(Speech2Text2PreTrainedModel):
"""
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__... | class_definition | 31,839 | 32,304 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,440 |
class Speech2Text2ForCausalLM(Speech2Text2PreTrainedModel):
_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 = Speech2Text2Dec... | class_definition | 32,529 | 43,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py | null | 10,441 |
class Speech2Text2Processor(ProcessorMixin):
r"""
Constructs a Speech2Text2 processor which wraps a Speech2Text2 feature extractor and a Speech2Text2 tokenizer into
a single processor.
[`Speech2Text2Processor`] offers all the functionalities of [`AutoFeatureExtractor`] and [`Speech2Text2Tokenizer`].
... | class_definition | 759 | 4,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py | null | 10,442 |
class EfficientFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`EfficientFormerModel`]. It is used to
instantiate an EfficientFormer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the ... | class_definition | 819 | 7,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py | null | 10,443 |
class EfficientFormerPatchEmbeddings(nn.Module):
"""
This class performs downsampling between two stages. For the input tensor with the shape [batch_size, num_channels,
height, width] it produces output tensor with the shape [batch_size, num_channels, height/stride, width/stride]
"""
def __init__(s... | class_definition | 1,682 | 2,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,444 |
class EfficientFormerSelfAttention(nn.Module):
def __init__(self, dim: int, key_dim: int, num_heads: int, attention_ratio: int, resolution: int):
super().__init__()
self.num_heads = num_heads
self.key_dim = key_dim
self.attention_ratio = attention_ratio
self.scale = key_dim*... | class_definition | 2,983 | 6,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,445 |
class EfficientFormerConvStem(nn.Module):
def __init__(self, config: EfficientFormerConfig, out_channels: int):
super().__init__()
self.convolution1 = nn.Conv2d(config.num_channels, out_channels // 2, kernel_size=3, stride=2, padding=1)
self.batchnorm_before = nn.BatchNorm2d(out_channels //... | class_definition | 6,109 | 7,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,446 |
class EfficientFormerPooling(nn.Module):
def __init__(self, pool_size: int):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
output = self.pool(hidden_states... | class_definition | 7,020 | 7,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,447 |
class EfficientFormerDenseMlp(nn.Module):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
):
super().__init__()
out_features = out_features or in_features
... | class_definition | 7,382 | 8,367 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,448 |
class EfficientFormerConvMlp(nn.Module):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
drop: float = 0.0,
):
super().__init__()
out_features = out_fea... | class_definition | 8,370 | 9,661 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,449 |
class EfficientFormerDropPath(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.Tenso... | class_definition | 10,756 | 11,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,450 |
class EfficientFormerFlat(nn.Module):
def __init__(self):
super().__init__()
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
hidden_states = hidden_states.flatten(2).transpose(1, 2)
return hidden_states | class_definition | 11,248 | 11,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,451 |
class EfficientFormerMeta3D(nn.Module):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0):
super().__init__()
self.token_mixer = EfficientFormerSelfAttention(
dim=config.dim,
key_dim=config.key_dim,
num_heads=config.num_attention... | class_definition | 11,509 | 13,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,452 |
class EfficientFormerMeta3DLayers(nn.Module):
def __init__(self, config: EfficientFormerConfig):
super().__init__()
drop_paths = [
config.drop_path_rate * (block_idx + sum(config.depths[:-1]))
for block_idx in range(config.num_meta3d_blocks)
]
self.blocks = nn... | class_definition | 13,653 | 14,777 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,453 |
class EfficientFormerMeta4D(nn.Module):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0):
super().__init__()
pool_size = config.pool_size if config.pool_size is not None else 3
self.token_mixer = EfficientFormerPooling(pool_size=pool_size)
mlp_hidde... | class_definition | 14,780 | 16,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,454 |
class EfficientFormerMeta4DLayers(nn.Module):
def __init__(self, config: EfficientFormerConfig, stage_idx: int):
super().__init__()
num_layers = (
config.depths[stage_idx] if stage_idx != -1 else config.depths[stage_idx] - config.num_meta3d_blocks
)
drop_paths = [
... | class_definition | 16,352 | 17,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,455 |
class EfficientFormerIntermediateStage(nn.Module):
def __init__(self, config: EfficientFormerConfig, index: int):
super().__init__()
self.meta4D_layers = EfficientFormerMeta4DLayers(config, index)
def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor]:
hidden_states = sel... | class_definition | 17,214 | 17,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,456 |
class EfficientFormerLastStage(nn.Module):
def __init__(self, config: EfficientFormerConfig):
super().__init__()
self.meta4D_layers = EfficientFormerMeta4DLayers(config, -1)
self.flat = EfficientFormerFlat()
self.meta3D_layers = EfficientFormerMeta3DLayers(config)
def forward(se... | class_definition | 17,596 | 18,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,457 |
class EfficientFormerEncoder(nn.Module):
def __init__(self, config: EfficientFormerConfig):
super().__init__()
self.config = config
num_intermediate_stages = len(config.depths) - 1
downsamples = [
config.downsamples[i] or config.hidden_sizes[i] != config.hidden_sizes[i + ... | class_definition | 18,222 | 20,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,458 |
class EfficientFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EfficientFormerConfig
base_model_prefix = "efficientformer"
main_input_name = "pixel_values"... | class_definition | 20,398 | 21,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,459 |
class EfficientFormerModel(EfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.config = config
_no_split_modules = ["EfficientFormerMeta4D"]
self.patch_embed = EfficientFormerConvStem(config, config.hidden_sizes[0])
... | class_definition | 22,816 | 25,051 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,460 |
class EfficientFormerForImageClassification(EfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = EfficientFormerModel(config)
# Classifier head
self.classifier... | class_definition | 25,304 | 28,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,461 |
class EfficientFormerForImageClassificationWithTeacherOutput(ModelOutput):
"""
Output type of [`EfficientFormerForImageClassificationWithTeacher`].
Args:
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Prediction scores as the average of the cls_logits and disti... | class_definition | 28,757 | 30,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,462 |
class EfficientFormerForImageClassificationWithTeacher(EfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = EfficientFormerModel(config)
# Classifier head
self... | class_definition | 31,194 | 33,579 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py | null | 10,463 |
class EfficientFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a EfficientFormer 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"])`. ... | class_definition | 1,345 | 15,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/image_processing_efficientformer.py | null | 10,464 |
class TFEfficientFormerPatchEmbeddings(keras.layers.Layer):
"""
This class performs downsampling between two stages. For the input tensor with the shape [batch_size, num_channels,
height, width] it produces output tensor with the shape [batch_size, num_channels, height/stride, width/stride]
"""
def... | class_definition | 1,780 | 3,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,465 |
class TFEfficientFormerSelfAttention(keras.layers.Layer):
def __init__(
self,
dim: int,
key_dim: int,
num_heads: int,
attention_ratio: int,
resolution: int,
config: EfficientFormerConfig,
**kwargs,
):
super().__init__(**kwargs)
sel... | class_definition | 3,965 | 8,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,466 |
class TFEfficientFormerConvStem(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, out_channels: int, **kwargs):
super().__init__(**kwargs)
self.padding = keras.layers.ZeroPadding2D(padding=1)
self.convolution1 = keras.layers.Conv2D(
filters=out_channels // 2... | class_definition | 8,223 | 11,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,467 |
class TFEfficientFormerPooling(keras.layers.Layer):
def __init__(self, pool_size: int, **kwargs):
super().__init__(**kwargs)
self.pool = keras.layers.AveragePooling2D(pool_size=pool_size, strides=1, padding="same")
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
output = self.poo... | class_definition | 11,003 | 11,401 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,468 |
class TFEfficientFormerDenseMlp(keras.layers.Layer):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
**kwargs,
):
super().__init__(**kwargs)
out_feature... | class_definition | 11,404 | 13,287 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,469 |
class TFEfficientFormerConvMlp(keras.layers.Layer):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
drop: float = 0.0,
**kwargs,
):
super().__init__(**k... | class_definition | 13,290 | 16,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,470 |
class TFEfficientFormerDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
References:
(1) github.com:rwightman/pytorch-image-models
"""
def __init__(self, drop_path: float, **kwargs):
super().__init__(**kwargs)
... | class_definition | 16,442 | 17,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,471 |
class TFEfficientFormerFlat(keras.layers.Layer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def call(self, hidden_states: tf.Tensor) -> Tuple[tf.Tensor]:
batch_size, _, _, in_channels = shape_list(hidden_states)
hidden_states = tf.reshape(hidden_states, shape=[batch_size, ... | class_definition | 17,169 | 17,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,472 |
class TFEfficientFormerMeta3D(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0, **kwargs):
super().__init__(**kwargs)
self.token_mixer = TFEfficientFormerSelfAttention(
dim=config.dim,
key_dim=config.key_dim,
... | class_definition | 17,538 | 21,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,473 |
class TFEfficientFormerMeta3DLayers(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, **kwargs):
super().__init__(**kwargs)
drop_paths = [
config.drop_path_rate * (block_idx + sum(config.depths[:-1]))
for block_idx in range(config.num_meta3d_blocks)
... | class_definition | 21,823 | 23,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,474 |
class TFEfficientFormerMeta4D(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0, **kwargs):
super().__init__(**kwargs)
pool_size = config.pool_size if config.pool_size is not None else 3
self.token_mixer = TFEfficientFormerPooling(pool_si... | class_definition | 23,421 | 26,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,475 |
class TFEfficientFormerMeta4DLayers(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, stage_idx: int, **kwargs):
super().__init__(**kwargs)
num_layers = (
config.depths[stage_idx] if stage_idx != -1 else config.depths[stage_idx] - config.num_meta3d_blocks
)
... | class_definition | 26,392 | 27,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,476 |
class TFEfficientFormerIntermediateStage(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, index: int, **kwargs):
super().__init__(**kwargs)
self.meta4D_layers = TFEfficientFormerMeta4DLayers(config=config, stage_idx=index, name="meta4D_layers")
def call(self, hidden_states... | class_definition | 27,639 | 28,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,477 |
class TFEfficientFormerLastStage(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, **kwargs):
super().__init__(**kwargs)
self.meta4D_layers = TFEfficientFormerMeta4DLayers(config=config, stage_idx=-1, name="meta4D_layers")
self.flat = TFEfficientFormerFlat(name="flat")
... | class_definition | 28,412 | 29,867 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,478 |
class TFEfficientFormerEncoder(keras.layers.Layer):
def __init__(self, config: EfficientFormerConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
num_intermediate_stages = len(config.depths) - 1
downsamples = [
config.downsamples[i] or config.hidden_sizes[... | class_definition | 29,870 | 32,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,479 |
class TFEfficientFormerMainLayer(keras.layers.Layer):
config_class = EfficientFormerConfig
def __init__(self, config: EfficientFormerConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.patch_embed = TFEfficientFormerConvStem(config, config.hidden_sizes[0], n... | class_definition | 32,862 | 36,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,480 |
class TFEfficientFormerPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EfficientFormerConfig
base_model_prefix = "efficientformer"
main_input_name = "pixel_val... | class_definition | 36,558 | 36,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,481 |
class TFEfficientFormerModel(TFEfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.efficientformer = TFEfficientFormerMainLayer(config, name="efficientformer")
@unpack_inputs
@add_start_docstrings_to_... | class_definition | 38,537 | 40,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,482 |
class TFEfficientFormerForImageClassification(TFEfficientFormerPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = TFEfficientFormerMainLayer(config, name="effi... | class_definition | 40,221 | 43,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,483 |
class TFEfficientFormerForImageClassificationWithTeacherOutput(ModelOutput):
"""
Args:
Output type of [`EfficientFormerForImageClassificationWithTeacher`].
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Prediction scores as the average of the cls_logits and distillation... | class_definition | 43,292 | 45,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,484 |
class TFEfficientFormerForImageClassificationWithTeacher(TFEfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = TFEfficientFormerMainLayer(config, name="efficientformer... | class_definition | 45,608 | 49,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py | null | 10,485 |
class ScaNNSearcher:
"""Note that ScaNNSearcher cannot currently be used within the model. In future versions, it might however be included."""
def __init__(
self,
db,
num_neighbors,
dimensions_per_block=2,
num_leaves=1000,
num_leaves_to_search=100,
train... | class_definition | 1,336 | 2,375 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py | null | 10,486 |
class RealmRetriever:
"""The retriever of REALM outputting the retrieved evidence block and whether the block has answers as well as answer
positions."
Parameters:
block_records (`np.ndarray`):
A numpy array which cantains evidence texts.
tokenizer ([`RealmTokeni... | class_definition | 2,378 | 6,371 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py | null | 10,487 |
class RealmTokenizer(PreTrainedTokenizer):
r"""
Construct a REALM tokenizer.
[`RealmTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting and
wordpiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Use... | class_definition | 1,601 | 15,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py | null | 10,488 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 15,036 | 21,222 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py | null | 10,489 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 21,225 | 23,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py | null | 10,490 |
class RealmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of
1. [`RealmEmbedder`]
2. [`RealmScorer`]
3. [`RealmKnowledgeAugEncoder`]
4. [`RealmRetriever`]
5. [`RealmReader`]
6. [`RealmForOpenQA`]
It is used to instantiate an REALM model ac... | class_definition | 786 | 7,556 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py | null | 10,491 |
class RealmTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" REALM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
[`RealmTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
This to... | class_definition | 1,100 | 10,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py | null | 10,492 |
class RealmEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embedd... | class_definition | 6,786 | 9,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,493 |
class RealmSelfAttention(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 | 9,963 | 17,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,494 |
class RealmSelfOutput(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)
def ... | class_definition | 17,310 | 17,917 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,495 |
class RealmAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = REALM_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = RealmSelfOutput(con... | class_definition | 17,990 | 20,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,496 |
class RealmIntermediate(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.inter... | class_definition | 20,118 | 20,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,497 |
class RealmOutput(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)
de... | class_definition | 20,687 | 21,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,498 |
class RealmLayer(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 = RealmAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config... | class_definition | 21,299 | 25,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,499 |
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