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 FlaxWav2Vec2ForPreTraining(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 30,976 | 31,141 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,800 |
class FlaxWav2Vec2Model(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 31,144 | 31,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,801 |
class FlaxWav2Vec2PreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 31,303 | 31,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,802 |
class FlaxWhisperForAudioClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 31,472 | 31,644 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,803 |
class FlaxWhisperForConditionalGeneration(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 31,647 | 31,821 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,804 |
class FlaxWhisperModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 31,824 | 31,979 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,805 |
class FlaxWhisperPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 31,982 | 32,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,806 |
class FlaxXGLMForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 32,150 | 32,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,807 |
class FlaxXGLMModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 32,311 | 32,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,808 |
class FlaxXGLMPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 32,466 | 32,628 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,809 |
class FlaxXLMRobertaForCausalLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 32,631 | 32,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,810 |
class FlaxXLMRobertaForMaskedLM(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 32,798 | 32,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,811 |
class FlaxXLMRobertaForMultipleChoice(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 32,965 | 33,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,812 |
class FlaxXLMRobertaForQuestionAnswering(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 33,138 | 33,311 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,813 |
class FlaxXLMRobertaForSequenceClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 33,314 | 33,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,814 |
class FlaxXLMRobertaForTokenClassification(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 33,495 | 33,670 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,815 |
class FlaxXLMRobertaModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 33,673 | 33,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,816 |
class FlaxXLMRobertaPreTrainedModel(metaclass=DummyObject):
_backends = ["flax"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["flax"]) | class_definition | 33,834 | 34,002 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_flax_objects.py | null | 2,817 |
class NllbMoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NllbMoeModel`]. It is used to instantiate an
NLLB-MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a si... | class_definition | 755 | 11,167 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/configuration_nllb_moe.py | null | 2,818 |
class NllbMoeScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embeddin... | class_definition | 5,186 | 5,674 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,819 |
class NllbMoeSinusoidalPositionalEmbedding(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 = embedd... | class_definition | 5,772 | 9,374 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,820 |
class NllbMoeTop2Router(nn.Module):
"""
Router using tokens choose top-2 experts assignment.
This router uses the same mechanism as in NLLB-MoE from the fairseq repository. Items are sorted by router_probs
and then routed to their choice of expert until the expert's expert_capacity is reached. **There ... | class_definition | 9,377 | 17,762 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,821 |
class NllbMoeDenseActDense(nn.Module):
def __init__(self, config: NllbMoeConfig, ffn_dim: int):
super().__init__()
self.fc1 = nn.Linear(config.d_model, ffn_dim)
self.fc2 = nn.Linear(ffn_dim, config.d_model)
self.dropout = nn.Dropout(config.activation_dropout)
self.act = ACT2F... | class_definition | 17,765 | 18,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,822 |
class NllbMoeSparseMLP(nn.Module):
r"""
Implementation of the NLLB-MoE sparse MLP module.
"""
def __init__(self, config: NllbMoeConfig, ffn_dim: int, expert_class: nn.Module = NllbMoeDenseActDense):
super().__init__()
self.router = NllbMoeTop2Router(config)
self.moe_token_dropou... | class_definition | 18,683 | 22,090 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,823 |
class NllbMoeAttention(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 | 22,219 | 29,655 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,824 |
class NllbMoeEncoderLayer(nn.Module):
def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
super().__init__()
self.embed_dim = config.d_model
self.is_sparse = is_sparse
self.self_attn = NllbMoeAttention(
embed_dim=self.embed_dim,
num_heads=confi... | class_definition | 29,658 | 32,930 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,825 |
class NllbMoeDecoderLayer(nn.Module):
def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
super().__init__()
self.embed_dim = config.d_model
self.is_sparse = is_sparse
self.self_attn = NllbMoeAttention(
embed_dim=self.embed_dim,
num_heads=confi... | class_definition | 32,933 | 39,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,826 |
class NllbMoePreTrainedModel(PreTrainedModel):
config_class = NllbMoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["NllbMoeEncoderLayer", "NllbMoeDecoderLayer"]
def _init_weights(self, module):
"""Initialize the weights"""
std = self.... | class_definition | 39,077 | 39,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,827 |
class NllbMoeEncoder(NllbMoePreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`NllbMoeEncoderLayer`].
Args:
config:
NllbMoeConfig
embed_tokens (nn.Embedding):
output embedding
"""
def ... | class_definition | 47,628 | 56,318 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,828 |
class NllbMoeDecoder(NllbMoePreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`NllbMoeDecoderLayer`]
Args:
config:
NllbMoeConfig
embed_tokens (nn.Embedding):
output embedding
"""
def __init__(self, confi... | class_definition | 56,321 | 70,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,829 |
class NllbMoeModel(NllbMoePreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: NllbMoeConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(c... | class_definition | 70,642 | 76,800 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,830 |
class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: NllbMoeConfig):
super().__init__(config)
self.model = ... | class_definition | 76,940 | 84,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nllb_moe/modeling_nllb_moe.py | null | 2,831 |
class SEWDNoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 10,430 | 11,155 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,832 |
class SEWDLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 11,266 | 12,241 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,833 |
class SEWDGroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 12,352 | 13,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,834 |
class SEWDPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
... | class_definition | 13,341 | 15,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,835 |
class SEWDSamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self.num... | class_definition | 15,163 | 15,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,836 |
class SEWDUpsampling(nn.Module):
def __init__(self, config):
super().__init__()
self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor)
self.activation = ACT2FN[config.feat_extract_activation]
self.squeeze_factor = config.squeeze_factor
def fo... | class_definition | 15,607 | 16,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,837 |
class SEWDFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [SEWDGroupNormConvLayer(config, layer_id=0)] + [
SEWDNoLayerNormConvLayer(c... | class_definition | 16,659 | 18,347 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,838 |
class SEWDFeatureExtractor(SEWDFeatureEncoder):
def __init__(self, config):
super().__init__(config)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self.__class__.__bases__[0].__name... | class_definition | 18,350 | 18,722 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,839 |
class ContextPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)
self.dropout = StableDropout(config.pooler_dropout)
self.config = config
def forward(self, hidden_states):
# We "po... | class_definition | 18,725 | 19,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,840 |
class XSoftmax(torch.autograd.Function):
"""
Masked Softmax which is optimized for saving memory
Args:
input (`torch.tensor`): The input tensor that will apply softmax.
mask (`torch.IntTensor`):
The mask matrix where 0 indicate that element will be ignored in the softmax calcula... | class_definition | 19,472 | 21,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,841 |
class DropoutContext:
def __init__(self):
self.dropout = 0
self.mask = None
self.scale = 1
self.reuse_mask = True | class_definition | 21,568 | 21,717 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,842 |
class XDropout(torch.autograd.Function):
"""Optimized dropout function to save computation and memory by using mask operation instead of multiplication."""
@staticmethod
def forward(ctx, input, local_ctx):
mask, dropout = get_mask(input, local_ctx)
ctx.scale = 1.0 / (1 - dropout)
if... | class_definition | 21,720 | 23,317 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,843 |
class StableDropout(nn.Module):
"""
Optimized dropout module for stabilizing the training
Args:
drop_prob (float): the dropout probabilities
"""
def __init__(self, drop_prob):
super().__init__()
self.drop_prob = drop_prob
self.count = 0
self.context_stack = ... | class_definition | 23,320 | 24,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,844 |
class SEWDSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.activation_dropout)
def forward(s... | class_definition | 24,643 | 25,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,845 |
class DisentangledSelfAttention(nn.Module):
"""
Disentangled self-attention module
Parameters:
config (`DebertaV2Config`):
A model config class instance with the configuration to build a new model. The schema is similar to
*BertConfig*, for more details, please refer [`Deber... | class_definition | 25,200 | 35,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,846 |
class SEWDAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = DisentangledSelfAttention(config)
self.output = SEWDSelfOutput(config)
self.config = config
def forward(
self,
hidden_states,
attention_mask,
output_attenti... | class_definition | 35,333 | 36,354 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,847 |
class SEWDIntermediate(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.interm... | class_definition | 36,443 | 37,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,848 |
class SEWDOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)
self.dropout = nn.Dropout(config.activation_dropout)
self.con... | class_definition | 37,011 | 37,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,849 |
class SEWDLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = SEWDAttention(config)
self.intermediate = SEWDIntermediate(config)
self.output = SEWDOutput(config)
def forward(
self,
hidden_states,
attention_mask,
query... | class_definition | 37,599 | 38,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,850 |
class ConvLayer(nn.Module):
def __init__(self, config):
super().__init__()
kernel_size = getattr(config, "conv_kernel_size", 3)
groups = getattr(config, "conv_groups", 1)
self.conv_act = getattr(config, "conv_act", "tanh")
self.conv = nn.Conv1d(
config.hidden_size... | class_definition | 38,661 | 40,127 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,851 |
class SEWDTransformerEncoder(nn.Module):
"""Modified BertEncoder with relative position bias support"""
def __init__(self, config):
super().__init__()
self.layer = nn.ModuleList([SEWDLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention = getattr(config, "rel... | class_definition | 40,130 | 45,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,852 |
class SEWDEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = SEWDPositionalConvEmbedding(config)
self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor)
self.encoder = SEWDTransformerEncoder(config)
... | class_definition | 45,454 | 48,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,853 |
class SEWDPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SEWDConfig
base_model_prefix = "sew-d"
main_input_name = "input_values"
supports_gradient_checkpoin... | class_definition | 48,317 | 51,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,854 |
class SEWDModel(SEWDPreTrainedModel):
def __init__(self, config: SEWDConfig):
super().__init__(config)
self.config = config
self.feature_extractor = SEWDFeatureEncoder(config)
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.feature_layer_norm_eps)
self.project... | class_definition | 54,755 | 60,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,855 |
class SEWDForCTC(SEWDPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.sew_d = SEWDModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
... | class_definition | 60,545 | 67,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,856 |
class SEWDForSequenceClassification(SEWDPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Sequence classification does not support the use of SEWD adapters (config.add_adapt... | class_definition | 67,689 | 72,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/modeling_sew_d.py | null | 2,857 |
class SEWDConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SEWDModel`]. It is used to instantiate a SEW-D
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 831 | 16,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew_d/configuration_sew_d.py | null | 2,858 |
class TableTransformerDecoderOutput(BaseModelOutputWithCrossAttentions):
"""
Base class for outputs of the TABLE_TRANSFORMER decoder. This class adds one attribute to BaseModelOutputWithCrossAttentions,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each... | class_definition | 1,745 | 4,143 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,859 |
class TableTransformerModelOutput(Seq2SeqModelOutput):
"""
Base class for outputs of the TABLE_TRANSFORMER encoder-decoder model. This class adds one attribute to Seq2SeqModelOutput,
namely an optional stack of intermediate decoder activations, i.e. the output of each decoder layer, each of them
gone th... | class_definition | 4,278 | 7,793 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,860 |
class TableTransformerObjectDetectionOutput(ModelOutput):
"""
Output type of [`TableTransformerForObjectDetection`].
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-e... | class_definition | 7,953 | 12,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,861 |
class TableTransformerFrozenBatchNorm2d(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.
"""
de... | class_definition | 13,044 | 14,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,862 |
class TableTransformerConvEncoder(nn.Module):
"""
Convolutional backbone, using either the AutoBackbone API or one from the timm library.
nn.BatchNorm2d layers are replaced by TableTransformerFrozenBatchNorm2d as defined above.
"""
def __init__(self, config):
super().__init__()
s... | class_definition | 15,610 | 18,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,863 |
class TableTransformerConvModel(nn.Module):
"""
This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder.
"""
def __init__(self, conv_encoder, position_embedding):
super().__init__()
self.conv_encoder = conv_encoder
self.position_embe... | class_definition | 18,887 | 19,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,864 |
class TableTransformerSinePositionEmbedding(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, sca... | class_definition | 19,760 | 21,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,865 |
class TableTransformerLearnedPositionEmbedding(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.Emb... | class_definition | 21,589 | 22,542 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,866 |
class TableTransformerAttention(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 TABLE_TRANSFORMER paper).
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
... | class_definition | 23,294 | 29,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,867 |
class TableTransformerEncoderLayer(nn.Module):
# Copied from transformers.models.detr.modeling_detr.DetrEncoderLayer.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TableTransform... | class_definition | 29,197 | 32,356 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,868 |
class TableTransformerDecoderLayer(nn.Module):
# Copied from transformers.models.detr.modeling_detr.DetrDecoderLayer.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TableTransfor... | class_definition | 32,359 | 37,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,869 |
class TableTransformerPreTrainedModel(PreTrainedModel):
config_class = TableTransformerConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
_no_split_modules = [
r"TableTransformerConvEncoder",
r"TableTransformerEncoderLayer",
r"TableTransformerDecoderLayer",
]... | class_definition | 37,224 | 38,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,870 |
class TableTransformerEncoder(TableTransformerPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TableTransformerEncoderLayer`].
The encoder updates the flattened feature map through multiple self-attention layers.
Small tweak f... | class_definition | 41,748 | 46,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,871 |
class TableTransformerDecoder(TableTransformerPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TableTransformerDecoderLayer`].
The decoder updates the query embeddings through multiple self-attention and cross-attention layers.
Some small tweaks... | class_definition | 46,739 | 54,731 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,872 |
class TableTransformerModel(TableTransformerPreTrainedModel):
# Copied from transformers.models.detr.modeling_detr.DetrModel.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__(config)
# Create backbone + positional encoding
backbone... | class_definition | 54,978 | 62,528 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,873 |
class TableTransformerForObjectDetection(TableTransformerPreTrainedModel):
# Copied from transformers.models.detr.modeling_detr.DetrForObjectDetection.__init__ with Detr->TableTransformer
def __init__(self, config: TableTransformerConfig):
super().__init__(config)
# DETR encoder-decoder model
... | class_definition | 62,773 | 68,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,874 |
class TableTransformerMLPPredictionHead(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/table_transformer/blob/master/models/... | class_definition | 69,106 | 69,917 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/modeling_table_transformer.py | null | 2,875 |
class TableTransformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TableTransformerModel`]. It is used to
instantiate a Table Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with th... | class_definition | 1,018 | 12,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py | null | 2,876 |
class TableTransformerOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
... | class_definition | 12,774 | 13,303 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/table_transformer/configuration_table_transformer.py | null | 2,877 |
class BarkSemanticGenerationConfig(GenerationConfig):
model_type = "semantic"
def __init__(
self,
eos_token_id=10_000,
renormalize_logits=True,
max_new_tokens=768,
output_scores=False,
return_dict_in_generate=False,
output_hidden_states=False,
out... | class_definition | 864 | 5,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py | null | 2,878 |
class BarkCoarseGenerationConfig(GenerationConfig):
model_type = "coarse_acoustics"
def __init__(
self,
renormalize_logits=True,
output_scores=False,
return_dict_in_generate=False,
output_hidden_states=False,
output_attentions=False,
temperature=1.0,
... | class_definition | 5,491 | 9,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py | null | 2,879 |
class BarkFineGenerationConfig(GenerationConfig):
model_type = "fine_acoustics"
def __init__(
self,
temperature=1.0,
max_fine_history_length=512,
max_fine_input_length=1024,
n_fine_codebooks=8,
**kwargs,
):
"""Class that holds a generation configurati... | class_definition | 9,570 | 11,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py | null | 2,880 |
class BarkGenerationConfig(GenerationConfig):
model_type = "bark"
is_composition = True
# TODO (joao): nested from_dict
def __init__(
self,
semantic_config: Dict = None,
coarse_acoustics_config: Dict = None,
fine_acoustics_config: Dict = None,
sample_rate=24_000... | class_definition | 11,218 | 14,946 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/generation_configuration_bark.py | null | 2,881 |
class BarkProcessor(ProcessorMixin):
r"""
Constructs a Bark processor which wraps a text tokenizer and optional Bark voice presets into a single processor.
Args:
tokenizer ([`PreTrainedTokenizer`]):
An instance of [`PreTrainedTokenizer`].
speaker_embeddings (`Dict[Dict[str]]`, *... | class_definition | 1,003 | 13,309 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/processing_bark.py | null | 2,882 |
class BarkSubModelConfig(PretrainedConfig):
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
"vocab_size": "input_vocab_size",
"window_size": "block_size",
}
def __init__(
... | class_definition | 3,506 | 4,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py | null | 2,883 |
class BarkSemanticConfig(BarkSubModelConfig):
model_type = "semantic"
base_config_key = "semantic_config" | class_definition | 5,206 | 5,319 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py | null | 2,884 |
class BarkCoarseConfig(BarkSubModelConfig):
model_type = "coarse_acoustics"
base_config_key = "coarse_acoustics_config" | class_definition | 5,889 | 6,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py | null | 2,885 |
class BarkFineConfig(BarkSubModelConfig):
model_type = "fine_acoustics"
base_config_key = "fine_acoustics_config"
def __init__(self, tie_word_embeddings=True, n_codes_total=8, n_codes_given=1, **kwargs):
self.n_codes_total = n_codes_total
self.n_codes_given = n_codes_given
super().... | class_definition | 6,920 | 7,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py | null | 2,886 |
class BarkConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark
model according to the specified sub-models configurations, defining the model architecture.
Instantiating a configuration with the defaults will yield... | class_definition | 7,302 | 11,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/configuration_bark.py | null | 2,887 |
class BarkSelfAttention(nn.Module):
# adapted from GPTNeoSelfAttention and Bark code
# BarkSelfAttention can have two attention type, i.e full attention or causal attention
def __init__(self, config, is_causal=False):
super().__init__()
# regularization
self.dropout = config.dropou... | class_definition | 1,983 | 6,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,888 |
class BarkSelfFlashAttention2(BarkSelfAttention):
"""
Bark flash attention module. This module inherits from `BarkSelfAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal... | class_definition | 6,999 | 10,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,889 |
class BarkLayerNorm(nn.Module):
"""LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False."""
def __init__(self, hidden_size, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size)) if ... | class_definition | 11,001 | 11,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,890 |
class BarkMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.in_proj = nn.Linear(config.hidden_size, 4 * config.hidden_size, bias=config.bias)
self.out_proj = nn.Linear(4 * config.hidden_size, config.hidden_size, bias=config.bias)
self.dropout = nn.Dropout(config.dro... | class_definition | 11,457 | 12,086 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,891 |
class BarkBlock(nn.Module):
def __init__(self, config, is_causal=False):
super().__init__()
if is_causal:
# if causal, uses handmade LayerNorm, so that the layerNorm bias is optional
# this handmade layerNorm is used to stick with Bark choice of leaving optional bias in
... | class_definition | 12,089 | 14,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,892 |
class BarkPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BarkConfig
supports_gradient_checkpointing = False
_supports_flash_attn_2 = True
def _init_weights... | class_definition | 14,077 | 16,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,893 |
class BarkCausalModel(BarkPreTrainedModel, GenerationMixin):
config_class = BarkSubModelConfig
def __init__(self, config):
super().__init__(config)
self.config = config
# initialize as an autoregressive GPT-like model
self.input_embeds_layer = nn.Embedding(config.input_vocab_si... | class_definition | 24,166 | 35,008 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,894 |
class BarkSemanticModel(BarkCausalModel):
base_model_prefix = "semantic"
config_class = BarkSemanticConfig
def generate(
self,
input_ids: torch.Tensor,
semantic_generation_config: BarkSemanticGenerationConfig = None,
history_prompt: Optional[Dict[str, torch.Tensor]] = None,
... | class_definition | 35,281 | 40,053 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,895 |
class BarkCoarseModel(BarkCausalModel):
base_model_prefix = "coarse_acoustics"
config_class = BarkCoarseConfig
def preprocess_histories(
self,
max_coarse_history: int,
semantic_to_coarse_ratio: int,
batch_size: int,
semantic_generation_config: int,
codebook_s... | class_definition | 40,338 | 49,954 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,896 |
class BarkFineModel(BarkPreTrainedModel):
base_model_prefix = "fine_acoustics"
config_class = BarkFineConfig
main_input_name = "codebook_idx"
def __init__(self, config):
# non-causal gpt-like model with one embedding layer and one lm_head for each codebook of Encodec
super().__init__(co... | class_definition | 50,219 | 68,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,897 |
class BarkModel(BarkPreTrainedModel):
config_class = BarkConfig
def __init__(self, config):
super().__init__(config)
self.semantic = BarkSemanticModel(config.semantic_config)
self.coarse_acoustics = BarkCoarseModel(config.coarse_acoustics_config)
self.fine_acoustics = BarkFineM... | class_definition | 69,967 | 82,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bark/modeling_bark.py | null | 2,898 |
class TFConvNextV2DropPath(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)
se... | class_definition | 1,949 | 2,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnextv2/modeling_tf_convnextv2.py | null | 2,899 |
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