text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class TransfoXLModel(TransfoXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.n_token = config.vocab_size
self.d_embed = config.d_embed
self.d_model = config.d_model
self.n_head = config.n_head
self.d_head = config.d_head
self.wo... | class_definition | 32,702 | 42,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,200 |
class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
_tied_weights_keys = [r"crit\.out_projs\.\d+", r"crit\.out_layers\.\d+\.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = TransfoXLModel(config)
self.sample_softmax = config.sample_softmax
self.... | class_definition | 42,531 | 50,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,201 |
class TransfoXLForSequenceClassification(TransfoXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = TransfoXLModel(config)
self.score = nn.Linear(config.d_embed, self.num_labels, bias=False)
# Initial... | class_definition | 50,998 | 55,891 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py | null | 10,202 |
class TFPositionalEmbedding(keras.layers.Layer):
def __init__(self, demb, **kwargs):
super().__init__(**kwargs)
self.inv_freq = 1 / (10000 ** (tf.range(0, demb, 2.0) / demb))
def call(self, pos_seq, bsz=None):
self.inv_freq = tf.cast(self.inv_freq, dtype=pos_seq.dtype)
sinusoid... | class_definition | 1,591 | 2,183 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,203 |
class TFPositionwiseFF(keras.layers.Layer):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5, init_std=0.02, **kwargs):
super().__init__(**kwargs)
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.layer_1 = keras... | class_definition | 2,186 | 3,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,204 |
class TFRelPartialLearnableMultiHeadAttn(keras.layers.Layer):
def __init__(
self,
n_head,
d_model,
d_head,
dropout,
dropatt=0.0,
pre_lnorm=False,
r_r_bias=None,
r_w_bias=None,
layer_norm_epsilon=1e-5,
init_std=0.02,
outp... | class_definition | 3,873 | 9,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,205 |
class TFRelPartialLearnableDecoderLayer(keras.layers.Layer):
def __init__(
self,
n_head,
d_model,
d_head,
d_inner,
dropout,
dropatt=0.0,
pre_lnorm=False,
r_w_bias=None,
r_r_bias=None,
layer_norm_epsilon=1e-5,
init_std=0.... | class_definition | 9,355 | 10,824 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,206 |
class TFTransfoEmbeddings(keras.layers.Layer):
def __init__(self, vocab_size, emb_size, init_std, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.emb_size = emb_size
self.init_std = init_std
def build(self, input_shape):
self.weight = self.add_we... | class_definition | 10,827 | 11,412 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,207 |
class TFAdaptiveEmbedding(keras.layers.Layer):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, init_std=0.02, sample_softmax=False, **kwargs):
super().__init__(**kwargs)
self.n_token = n_token
self.d_embed = d_embed
self.init_std = init_std
self.cutoffs = c... | class_definition | 11,415 | 14,143 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,208 |
class TFTransfoXLMainLayer(keras.layers.Layer):
config_class = TransfoXLConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
... | class_definition | 14,166 | 23,589 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,209 |
class TFTransfoXLPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TransfoXLConfig
base_model_prefix = "transformer" | class_definition | 23,592 | 23,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,210 |
class TFTransfoXLModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output o... | class_definition | 23,877 | 25,620 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,211 |
class TFTransfoXLLMHeadModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
losses (`tf.Tensor` of shape *(batch_size, sequence_length-1)*, *optional*, returned when `labels` is provided):
Language ... | class_definition | 25,634 | 27,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,212 |
class TFTransfoXLSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
lo... | class_definition | 27,602 | 29,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,213 |
class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFTransfoXLMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(TRANSFO_XL_INPUTS_DOCSTRIN... | class_definition | 34,781 | 36,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,214 |
class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = TFTransfoXLMainLayer(config, name="transformer")
self.sample_softmax = config.sample_softmax
assert self.sample_softmax <= 0, (
"Sampling from... | class_definition | 36,317 | 40,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,215 |
class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = keras.layers.Dense(
config.num_labels,... | class_definition | 41,428 | 45,904 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl.py | null | 10,216 |
class TransfoXLConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`TransfoXLModel`] or a [`TFTransfoXLModel`]. It is
used to instantiate a Transformer-XL model according to the specified arguments, defining the model architecture.
Instantiating a configuration... | class_definition | 897 | 7,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/configuration_transfo_xl.py | null | 10,217 |
class TFAdaptiveSoftmaxMask(keras.layers.Layer):
def __init__(self, vocab_size, d_embed, d_proj, cutoffs, div_val=1, keep_order=False, **kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [vo... | class_definition | 900 | 7,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_tf_transfo_xl_utilities.py | null | 10,218 |
class ProjectedAdaptiveLogSoftmax(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, keep_order=False):
super().__init__()
self.n_token = n_token
self.d_embed = d_embed
self.d_proj = d_proj
self.cutoffs = cutoffs + [n_token]
self.cutoff_end... | class_definition | 1,000 | 10,858 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl_utilities.py | null | 10,219 |
class TransfoXLTokenizer(PreTrainedTokenizer):
"""
Construct a Transformer-XL tokenizer adapted from Vocab class in [the original
code](https://github.com/kimiyoung/transformer-xl). The Transformer-XL tokenizer is a word-level tokenizer (no
sub-word tokenization).
This tokenizer inherits from [`Pre... | class_definition | 2,980 | 19,917 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py | null | 10,220 |
class LMOrderedIterator:
def __init__(self, data, bsz, bptt, device="cpu", ext_len=None):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.device = devic... | class_definition | 19,920 | 21,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py | null | 10,221 |
class LMShuffledIterator:
def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
"""
data -- list[LongTensor] -- there is no order among the LongTensors
"""
self.data = data
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ex... | class_definition | 21,941 | 24,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py | null | 10,222 |
class LMMultiFileIterator(LMShuffledIterator):
def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
self.paths = paths
self.vocab = vocab
self.bsz = bsz
self.bptt = bptt
self.ext_len = ext_len if ext_len is not None else 0
self.dev... | class_definition | 24,619 | 25,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py | null | 10,223 |
class TransfoXLCorpus:
@classmethod
@torch_only_method
def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
"""
Instantiate a pre-processed corpus.
"""
vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **... | class_definition | 25,520 | 30,304 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/tokenization_transfo_xl.py | null | 10,224 |
class ErnieMEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_tok... | class_definition | 1,760 | 3,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,225 |
class ErnieMSelfAttention(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 | 3,520 | 10,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,226 |
class ErnieMAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self_attn = ErnieMSelfAttention(config, position_embedding_type=position_embedding_type)
self.out_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.pruned_h... | class_definition | 10,887 | 13,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,227 |
class ErnieMEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
# to mimic paddlenlp implementation
dropout = 0.1 if config.hidden_dropout_prob is None else config.hidden_dropout_prob
act_dropout = config.hidden_dropout_prob if config.act_dropout is None else conf... | class_definition | 13,048 | 15,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,228 |
class ErnieMEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList([ErnieMEncoderLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
input_embeds: torch.Tensor,
attention_mask: O... | class_definition | 15,652 | 17,576 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,229 |
class ErnieMPooler(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 hid... | class_definition | 17,579 | 18,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,230 |
class ErnieMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ErnieMConfig
base_model_prefix = "ernie_m"
def _init_weights(self, module):
"""Initialize th... | class_definition | 18,143 | 19,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,231 |
class ErnieMModel(ErnieMPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super(ErnieMModel, self).__init__(config)
self.initializer_range = config.initializer_range
self.embeddings = ErnieMEmbeddings(config)
self.encoder = ErnieMEncoder(config)
self.poole... | class_definition | 22,505 | 27,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,232 |
class ErnieMForSequenceClassification(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.ernie_m = ErnieMModel(config)
classifier_dropout = (
config.classifier_dropout if conf... | class_definition | 27,649 | 31,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,233 |
class ErnieMForMultipleChoice(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.ernie_m = ErnieMModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
... | class_definition | 31,982 | 35,422 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,234 |
class ErnieMForTokenClassification(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.clas... | class_definition | 35,646 | 38,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,235 |
class ErnieMForQuestionAnswering(ErnieMPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie_m = ErnieMModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
#... | class_definition | 38,843 | 43,011 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,236 |
class ErnieMForInformationExtraction(ErnieMPreTrainedModel):
def __init__(self, config):
super(ErnieMForInformationExtraction, self).__init__(config)
self.ernie_m = ErnieMModel(config)
self.linear_start = nn.Linear(config.hidden_size, 1)
self.linear_end = nn.Linear(config.hidden_size... | class_definition | 43,277 | 47,027 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/modeling_ernie_m.py | null | 10,237 |
class ErnieMTokenizer(PreTrainedTokenizer):
r"""
Constructs a Ernie-M tokenizer. It uses the `sentencepiece` tools to cut the words to sub-words.
Args:
sentencepiece_model_file (`str`):
The file path of sentencepiece model.
vocab_file (`str`, *optional*):
The file pa... | class_definition | 1,317 | 16,168 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/tokenization_ernie_m.py | null | 10,238 |
class ErnieMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ErnieMModel`]. It is used to instantiate a
Ernie-M model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 1,008 | 5,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/ernie_m/configuration_ernie_m.py | null | 10,239 |
class VanDropPath(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) -> torch.... | class_definition | 2,813 | 3,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,240 |
class VanOverlappingPatchEmbedder(nn.Module):
"""
Downsamples the input using a patchify operation with a `stride` of 4 by default making adjacent windows overlap by
half of the area. From [PVTv2: Improved Baselines with Pyramid Vision
Transformer](https://arxiv.org/abs/2106.13797).
"""
def __i... | class_definition | 3,293 | 4,124 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,241 |
class VanMlpLayer(nn.Module):
"""
MLP with depth-wise convolution, from [PVTv2: Improved Baselines with Pyramid Vision
Transformer](https://arxiv.org/abs/2106.13797).
"""
def __init__(
self,
in_channels: int,
hidden_size: int,
out_channels: int,
hidden_act: s... | class_definition | 4,127 | 5,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,242 |
class VanLargeKernelAttention(nn.Module):
"""
Basic Large Kernel Attention (LKA).
"""
def __init__(self, hidden_size: int):
super().__init__()
self.depth_wise = nn.Conv2d(hidden_size, hidden_size, kernel_size=5, padding=2, groups=hidden_size)
self.depth_wise_dilated = nn.Conv2d(... | class_definition | 5,341 | 6,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,243 |
class VanLargeKernelAttentionLayer(nn.Module):
"""
Computes attention using Large Kernel Attention (LKA) and attends the input.
"""
def __init__(self, hidden_size: int):
super().__init__()
self.attention = VanLargeKernelAttention(hidden_size)
def forward(self, hidden_state: torch.T... | class_definition | 6,109 | 6,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,244 |
class VanSpatialAttentionLayer(nn.Module):
"""
Van spatial attention layer composed by projection (via conv) -> act -> Large Kernel Attention (LKA) attention ->
projection (via conv) + residual connection.
"""
def __init__(self, hidden_size: int, hidden_act: str = "gelu"):
super().__init__(... | class_definition | 6,572 | 7,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,245 |
class VanLayerScaling(nn.Module):
"""
Scales the inputs by a learnable parameter initialized by `initial_value`.
"""
def __init__(self, hidden_size: int, initial_value: float = 1e-2):
super().__init__()
self.weight = nn.Parameter(initial_value * torch.ones((hidden_size)), requires_grad=... | class_definition | 7,660 | 8,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,246 |
class VanLayer(nn.Module):
"""
Van layer composed by normalization layers, large kernel attention (LKA) and a multi layer perceptron (MLP).
"""
def __init__(
self,
config: VanConfig,
hidden_size: int,
mlp_ratio: int = 4,
drop_path_rate: float = 0.5,
):
... | class_definition | 8,201 | 9,955 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,247 |
class VanStage(nn.Module):
"""
VanStage, consisting of multiple layers.
"""
def __init__(
self,
config: VanConfig,
in_channels: int,
hidden_size: int,
patch_size: int,
stride: int,
depth: int,
mlp_ratio: int = 4,
drop_path_rate: fl... | class_definition | 9,958 | 11,384 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,248 |
class VanEncoder(nn.Module):
"""
VanEncoder, consisting of multiple stages.
"""
def __init__(self, config: VanConfig):
super().__init__()
self.stages = nn.ModuleList([])
patch_sizes = config.patch_sizes
strides = config.strides
hidden_sizes = config.hidden_sizes
... | class_definition | 11,387 | 13,398 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,249 |
class VanPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VanConfig
base_model_prefix = "van"
main_input_name = "pixel_values"
supports_gradient_checkpointing... | class_definition | 13,401 | 14,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,250 |
class VanModel(VanPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = VanEncoder(config)
# final layernorm layer
self.layernorm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps)
# Initialize weigh... | class_definition | 15,932 | 17,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,251 |
class VanForImageClassification(VanPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.van = VanModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
... | class_definition | 17,956 | 21,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/modeling_van.py | null | 10,252 |
class VanConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VanModel`]. It is used to instantiate a VAN model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | class_definition | 782 | 4,656 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/configuration_van.py | null | 10,253 |
class Tracker:
module: nn.Module
traced: List[nn.Module] = field(default_factory=list)
handles: list = field(default_factory=list)
def _forward_hook(self, m, inputs: Tensor, outputs: Tensor):
has_not_submodules = len(list(m.modules())) == 1 or isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchN... | class_definition | 1,379 | 2,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/convert_van_to_pytorch.py | null | 10,254 |
class ModuleTransfer:
src: nn.Module
dest: nn.Module
verbose: int = 0
src_skip: List = field(default_factory=list)
dest_skip: List = field(default_factory=list)
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest` by performing a forward pass usin... | class_definition | 2,292 | 3,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/van/convert_van_to_pytorch.py | null | 10,255 |
class TvltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TvltModel`]. It is used to instantiate a TVLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 797 | 8,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/configuration_tvlt.py | null | 10,256 |
class TvltProcessor(ProcessorMixin):
r"""
Constructs a TVLT processor which wraps a TVLT image processor and TVLT feature extractor into a single processor.
[`TvltProcessor`] offers all the functionalities of [`TvltImageProcessor`] and [`TvltFeatureExtractor`]. See the
docstring of [`~TvltProcessor.__c... | class_definition | 690 | 3,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/processing_tvlt.py | null | 10,257 |
class TvltImageProcessor(BaseImageProcessor):
r"""
Constructs a TVLT image processor.
This processor can be used to prepare either videos or images for the model by converting images to 1-frame videos.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the ... | class_definition | 2,050 | 20,089 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/image_processing_tvlt.py | null | 10,258 |
class TvltModelOutput(ModelOutput):
"""
Class for TvltModel's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model... | class_definition | 1,505 | 4,268 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,259 |
class TvltDecoderOutput(ModelOutput):
"""
Class for TvltDecoder's outputs, with potential hidden states and attentions.
Args:
logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(torch.FloatTenso... | class_definition | 4,282 | 5,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,260 |
class TvltForPreTrainingOutput(ModelOutput):
"""
Class for TvltForPreTraining's outputs, with potential hidden states and attentions.
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
matching_logits (`torch.FloatTensor` of shape `(batch_size, 1)`):
... | class_definition | 5,612 | 7,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,261 |
class TvltPixelEmbeddings(nn.Module):
"""Construct the patch and position embeddings."""
def __init__(self, config):
super().__init__()
self.patch_embeddings = TvltPixelPatchEmbeddings(config)
self.num_patches_per_image = self.patch_embeddings.num_patches_per_image
self.type_e... | class_definition | 9,767 | 10,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,262 |
class TvltAudioEmbeddings(nn.Module):
"""Construct the patch and position embeddings."""
def __init__(self, config):
super().__init__()
self.patch_embeddings = TvltAudioPatchEmbeddings(config)
self.num_patches = self.patch_embeddings.num_patches
self.type_embed_a = nn.Paramete... | class_definition | 10,881 | 12,152 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,263 |
class TvltPixelPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config... | class_definition | 12,155 | 14,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,264 |
class TvltAudioPatchEmbeddings(nn.Module):
"""
This class turns `audio_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config... | class_definition | 14,224 | 16,264 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,265 |
class TvltSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a multiple of the number ... | class_definition | 16,267 | 19,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,266 |
class TvltSelfOutput(nn.Module):
"""
The residual connection is defined in TvltLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: TvltConfig) -> None:
super().__init__()
self.dense = nn.Linear(conf... | class_definition | 19,167 | 19,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,267 |
class TvltAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = TvltSelfAttention(config)
self.output = TvltSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, in... | class_definition | 19,816 | 21,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,268 |
class TvltIntermediate(nn.Module):
def __init__(self, config: TvltConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | class_definition | 21,347 | 21,933 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,269 |
class TvltOutput(nn.Module):
def __init__(self, config: TvltConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.T... | class_definition | 21,936 | 22,465 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,270 |
class TvltLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = TvltAttention(config)
self... | class_definition | 22,468 | 24,056 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,271 |
class TvltEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([TvltLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 24,059 | 25,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,272 |
class TvltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TvltConfig
base_model_prefix = "tvlt"
main_input_name = "pixel_values"
supports_gradient_checkpoint... | class_definition | 25,950 | 26,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,273 |
class TvltModel(TvltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.pixel_embeddings = TvltPixelEmbeddings(config)
self.audio_embeddings = TvltAudioEmbeddings(config)
self.encoder = TvltEncoder(config)
self.cls_embedd... | class_definition | 29,978 | 36,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,274 |
class TvltDecoder(nn.Module):
def __init__(self, config):
super().__init__()
decoder_config = deepcopy(config)
decoder_config.hidden_size = config.decoder_hidden_size
decoder_config.num_hidden_layers = config.decoder_num_hidden_layers
decoder_config.num_attention_heads = con... | class_definition | 36,496 | 38,732 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,275 |
class TvltForPreTraining(TvltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.task_matching = config.task_matching
self.task_mae = config.task_mae
if not (self.task_matching or self.task_mae):
raise ValueError("Must... | class_definition | 38,878 | 51,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,276 |
class TvltPooler(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):
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(fi... | class_definition | 51,928 | 52,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,277 |
class TvltMatchingHead(nn.Module):
def __init__(self, config):
super().__init__()
self.pooler = TvltPooler(config)
self.fc = nn.Linear(config.hidden_size, 1)
def forward(self, hidden_states):
hidden_states = self.fc(self.pooler(hidden_states))
return hidden_states | class_definition | 52,352 | 52,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,278 |
class TvltMAEHead(nn.Module):
def __init__(self, config, output_dim=None):
super().__init__()
self.config = config
self.decoder = nn.Linear(config.decoder_hidden_size, output_dim)
def forward(self, hidden_states):
hidden_states = self.decoder(hidden_states)
return hidden... | class_definition | 52,668 | 52,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,279 |
class TvltForAudioVisualClassification(TvltPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.tvlt = TvltModel(config)
# Classifier head
self.classifier = nn.Sequential(
nn.Linear(config.hidden_size, config.hidden_size * 2),
nn.Layer... | class_definition | 53,290 | 56,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/modeling_tvlt.py | null | 10,280 |
class TvltFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a TVLT audio feature extractor. This feature extractor can be used to prepare audios for the model.
This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
should refer to this s... | class_definition | 998 | 10,557 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tvlt/feature_extraction_tvlt.py | null | 10,281 |
class MCTCTProcessor(ProcessorMixin):
r"""
Constructs a MCTCT processor which wraps a MCTCT feature extractor and a MCTCT tokenizer into a single processor.
[`MCTCTProcessor`] offers all the functionalities of [`MCTCTFeatureExtractor`] and [`AutoTokenizer`]. See the
[`~MCTCTProcessor.__call__`] and [`~... | class_definition | 775 | 5,930 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/processing_mctct.py | null | 10,282 |
class MCTCTFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a M-CTC-T feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information re... | class_definition | 1,083 | 13,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/feature_extraction_mctct.py | null | 10,283 |
class MCTCTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MCTCTModel`]. It is used to instantiate an
M-CTC-T model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar... | class_definition | 786 | 9,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/configuration_mctct.py | null | 10,284 |
class MCTCTConv1dSubsampler(nn.Module):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config):
super().__init__()
self.config = con... | class_definition | 1,786 | 4,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,285 |
class MCTCTEmbeddings(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 | 4,429 | 7,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,286 |
class MCTCTSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numbe... | class_definition | 7,230 | 11,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,287 |
class MCTCTLayerNorm(nn.Module):
def __init__(self):
super().__init__()
self.singleton_weight = nn.Parameter(torch.ones(1))
self.singleton_bias = nn.Parameter(torch.zeros(1))
def forward(self, hidden_states):
return (hidden_states * self.singleton_weight) + self.singleton_bias | class_definition | 11,978 | 12,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,288 |
class MCTCTSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Drop... | class_definition | 12,299 | 12,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,289 |
class MCTCTAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MCTCTSelfAttention(config)
self.output = MCTCTSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, inde... | class_definition | 12,906 | 14,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,290 |
class MCTCTIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | class_definition | 14,470 | 15,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,291 |
class MCTCTOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_p... | class_definition | 15,021 | 15,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,292 |
class MCTCTLayer(nn.Module):
def __init__(self, config: MCTCTConfig):
super().__init__()
self.seq_len_dim = 1
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.intermediate = MCTCTIntermediate(config)
self.attention = MCTCTAttention(config)
self.is_... | class_definition | 15,601 | 16,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,293 |
class MCTCTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MCTCTConfig
base_model_prefix = "mctct"
main_input_name = "input_features"
supports_gradient_check... | class_definition | 16,895 | 19,937 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,294 |
class MCTCTEncoder(MCTCTPreTrainedModel):
def __init__(self, config: MCTCTConfig):
super().__init__(config)
self.hidden_dropout_prob = config.hidden_dropout_prob
self.layer_norm = MCTCTLayerNorm()
self.conv = MCTCTConv1dSubsampler(config)
self.layers = nn.ModuleList([MCTCTLa... | class_definition | 22,154 | 26,267 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,295 |
class MCTCTModel(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = MCTCTEncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DO... | class_definition | 26,427 | 28,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,296 |
class MCTCTForCTC(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mctct = MCTCTModel(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
... | class_definition | 28,638 | 32,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mctct/modeling_mctct.py | null | 10,297 |
class NezhaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`NezhaModel`]. It is used to instantiate an Nezha
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar ... | class_definition | 36 | 4,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/configuration_nezha.py | null | 10,298 |
class NezhaRelativePositionsEncoding(nn.Module):
"""Implement the Functional Relative Position Encoding"""
def __init__(self, length, depth, max_relative_position=127):
super().__init__()
vocab_size = max_relative_position * 2 + 1
range_vec = torch.arange(length)
range_mat = ran... | class_definition | 4,722 | 6,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,299 |
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