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 YosoForSequenceClassification(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.yoso = YosoModel(config)
self.classifier = YosoClassificationHead(config)
# Initialize weights and apply final processing
... | class_definition | 39,353 | 43,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,600 |
class YosoForMultipleChoice(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.yoso = YosoModel(config)
self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weigh... | class_definition | 43,249 | 46,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,601 |
class YosoForTokenClassification(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.yoso = YosoModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size,... | class_definition | 47,175 | 50,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,602 |
class YosoForQuestionAnswering(YosoPreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.yoso = YosoModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# In... | class_definition | 50,576 | 54,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/modeling_yoso.py | null | 6,603 |
class YosoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`YosoModel`]. It is used to instantiate an YOSO
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 781 | 6,687 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yoso/configuration_yoso.py | null | 6,604 |
class TimmWrapperImageProcessor(BaseImageProcessor):
"""
Wrapper class for timm models to be used within transformers.
Args:
pretrained_cfg (`Dict[str, Any]`):
The configuration of the pretrained model used to resolve evaluation and
training transforms.
architecture ... | class_definition | 1,139 | 5,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timm_wrapper/image_processing_timm_wrapper.py | null | 6,605 |
class TimmWrapperModelOutput(ModelOutput):
"""
Output class for models TimmWrapperModel, containing the last hidden states, an optional pooled output,
and optional hidden states.
Args:
last_hidden_state (`torch.FloatTensor`):
The last hidden state of the model, output before applyin... | class_definition | 1,190 | 2,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py | null | 6,606 |
class TimmWrapperPreTrainedModel(PreTrainedModel):
main_input_name = "pixel_values"
config_class = TimmWrapperConfig
_no_split_modules = []
# used in Trainer to avoid passing `loss_kwargs` to model forward
accepts_loss_kwargs = False
def __init__(self, *args, **kwargs):
requires_backen... | class_definition | 3,410 | 5,489 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py | null | 6,607 |
class TimmWrapperModel(TimmWrapperPreTrainedModel):
"""
Wrapper class for timm models to be used in transformers.
"""
def __init__(self, config: TimmWrapperConfig):
super().__init__(config)
# using num_classes=0 to avoid creating classification head
self.timm_model = timm.create... | class_definition | 5,492 | 9,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py | null | 6,608 |
class TimmWrapperForImageClassification(TimmWrapperPreTrainedModel):
"""
Wrapper class for timm models to be used in transformers for image classification.
"""
def __init__(self, config: TimmWrapperConfig):
super().__init__(config)
if config.num_labels == 0:
raise ValueErro... | class_definition | 9,895 | 15,753 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py | null | 6,609 |
class TimmWrapperConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration for a timm backbone [`TimmWrapper`].
It is used to instantiate a timm model according to the specified arguments, defining the model.
Configuration objects inherit from [`PretrainedConfig`] and c... | class_definition | 931 | 4,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/timm_wrapper/configuration_timm_wrapper.py | null | 6,610 |
class Mamba2Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Mamba2Model`]. It is used to instantiate a MAMBA2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar ... | class_definition | 769 | 7,888 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/configuration_mamba2.py | null | 6,611 |
class Mamba2Cache:
"""
Arguments:
config: Mamba2Config
batch_size: int
dtype: torch.dtype
device: torch.device
Attributes:
dtype: (`torch.dtype`):
The default `dtype` used to initializing the cache.
conv_kernel_size: (`int`):
Model's c... | class_definition | 5,021 | 8,043 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,612 |
class MambaRMSNormGated(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states, gate=None):
input_dtype = hidden_states.dtype
hidden_s... | class_definition | 8,046 | 8,731 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,613 |
class Mamba2Mixer(nn.Module):
"""
Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
∆, B, C are input-dependent (this is a key difference between Mam... | class_definition | 8,734 | 30,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,614 |
class Mamba2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Mamba2RMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hid... | class_definition | 30,660 | 31,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,615 |
class Mamba2Block(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.residual_in_fp32 = config.residual_in_fp32
self.norm = Mamba2RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.mi... | class_definition | 31,301 | 32,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,616 |
class Mamba2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Mamba2Config
base_model_prefix = "backbone"
_no_split_modules = ["Mamba2Block"]
supports_gradient... | class_definition | 32,337 | 35,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,617 |
class Mamba2Output(ModelOutput):
"""
Class for the MAMBA2 model outputs.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
cache_params (`Mamba2Cache`):
... | class_definition | 35,120 | 36,391 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,618 |
class Mamba2CausalLMOutput(ModelOutput):
"""
Base class for causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.Flo... | class_definition | 36,499 | 38,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,619 |
class Mamba2Model(Mamba2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([Mamba2Block(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
self.gra... | class_definition | 41,417 | 46,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,620 |
class Mamba2ForCausalLM(Mamba2PreTrainedModel, GenerationMixin):
_tied_weights_keys = []
def __init__(self, config):
super().__init__(config)
self.backbone = Mamba2Model(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and ... | class_definition | 46,348 | 51,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba2/modeling_mamba2.py | null | 6,621 |
class SwitchTransformersConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SwitchTransformersModel`]. It is used to
instantiate a SwitchTransformers model according to the specified arguments, defining the model architecture.
Instantiating a configuration wi... | class_definition | 777 | 9,004 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/configuration_switch_transformers.py | null | 6,622 |
class SwitchTransformersTop1Router(nn.Module):
"""
Router using tokens choose top-1 experts assignment.
This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE
(https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by rout... | class_definition | 4,371 | 9,474 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,623 |
class SwitchTransformersLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the SwitchTransformers style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
se... | class_definition | 9,566 | 10,707 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,624 |
class SwitchTransformersDenseActDense(nn.Module):
def __init__(self, config: SwitchTransformersConfig):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropou... | class_definition | 10,862 | 11,753 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,625 |
class SwitchTransformersSparseMLP(nn.Module):
r"""
Implementation of the Switch Transformers Sparse MLP module.
"""
def __init__(self, config: SwitchTransformersConfig, expert_class: nn.Module = SwitchTransformersDenseActDense):
super().__init__()
# Step 1: Get the correct router accord... | class_definition | 11,756 | 14,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,626 |
class SwitchTransformersLayerFF(nn.Module):
r"""
Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module.
Parameters:
config : ([`SwitchTransformersConfig`]): Model configuration class with all the parameters of the model.
Initializing with ... | class_definition | 14,197 | 15,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,627 |
class SwitchTransformersAttention(nn.Module):
def __init__(
self,
config: SwitchTransformersConfig,
has_relative_attention_bias=False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bi... | class_definition | 15,986 | 27,250 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,628 |
class SwitchTransformersLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = SwitchTransformersAttention(
config, has_relative_attention_bias=has_relative_attention_bias, lay... | class_definition | 27,351 | 28,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,629 |
class SwitchTransformersLayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = SwitchTransformersAttention(
config, has_relative_attention_bias=False, layer_idx=layer_idx
)
self.layer_norm = S... | class_definition | 28,851 | 30,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,630 |
class SwitchTransformersBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, is_sparse=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.is_sparse = is_sparse
self.layer = nn.ModuleList()
self.layer... | class_definition | 30,337 | 34,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,631 |
class SwitchTransformersPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwitchTransformersConfig
base_model_prefix = "switch_transformers"
supports_gradient_chec... | class_definition | 34,668 | 40,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,632 |
class SwitchTransformersStack(SwitchTransformersPreTrainedModel):
def __init__(self, config, embed_tokens=None):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.wei... | class_definition | 40,172 | 58,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,633 |
class SwitchTransformersModel(SwitchTransformersPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: SwitchTransformersConfig):
super().__init__(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
... | class_definition | 69,623 | 77,360 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,634 |
class SwitchTransformersForConditionalGeneration(SwitchTransformersPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: SwitchTransformersConfig):
super().__init__(config)
self.model_d... | class_definition | 77,501 | 90,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,635 |
class SwitchTransformersEncoderModel(SwitchTransformersPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight"]
def __init__(self, config: SwitchTransformersConfig):
super().__init__(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = co... | class_definition | 90,941 | 94,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/switch_transformers/modeling_switch_transformers.py | null | 6,636 |
class AutoformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`AutoformerModel`]. It is used to instantiate an
Autoformer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will y... | class_definition | 822 | 12,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/configuration_autoformer.py | null | 6,637 |
class AutoFormerDecoderOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the... | class_definition | 1,592 | 4,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,638 |
class AutoformerModelOutput(ModelOutput):
"""
Autoformer model output that contains the additional trend output.
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 decoder ... | class_definition | 4,845 | 10,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,639 |
class AutoformerFeatureEmbedder(nn.Module):
"""
Embed a sequence of categorical features.
Args:
cardinalities (`list[int]`):
List of cardinalities of the categorical features.
embedding_dims (`list[int]`):
List of embedding dimensions of the categorical features.
... | class_definition | 10,430 | 11,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,640 |
class AutoformerStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
... | class_definition | 11,789 | 13,531 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,641 |
class AutoformerMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(confi... | class_definition | 13,708 | 16,111 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,642 |
class AutoformerNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim... | class_definition | 16,287 | 17,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,643 |
class AutoformerSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weig... | class_definition | 19,168 | 20,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,644 |
class AutoformerValueEmbedding(nn.Module):
def __init__(self, feature_size, d_model):
super().__init__()
self.value_projection = nn.Linear(in_features=feature_size, out_features=d_model, bias=False)
def forward(self, x):
return self.value_projection(x) | class_definition | 20,886 | 21,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,645 |
class AutoformerSeriesDecompositionLayer(nn.Module):
"""
Returns the trend and the seasonal parts of the time series. Calculated as:
x_trend = AvgPool(Padding(X)) and x_seasonal = X - x_trend
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.kernel_size ... | class_definition | 21,384 | 22,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,646 |
class AutoformerLayernorm(nn.Module):
"""
Special designed layer normalization for the seasonal part, calculated as: AutoformerLayernorm(x) = nn.LayerNorm(x)
- torch.mean(nn.LayerNorm(x))
"""
def __init__(self, config: AutoformerConfig):
super().__init__()
self.layernorm = nn.LayerN... | class_definition | 22,531 | 23,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,647 |
class AutoformerAttention(nn.Module):
"""
AutoCorrelation Mechanism with the following two phases:
(1) period-based dependencies discovery (2) time delay aggregation
This block replace the canonical self-attention mechanism.
"""
def __init__(
self,
embed_dim: int,
nu... | class_definition | 23,040 | 33,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,648 |
class AutoformerEncoderLayer(nn.Module):
def __init__(self, config: AutoformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = AutoformerAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=conf... | class_definition | 33,633 | 37,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,649 |
class AutoformerDecoderLayer(nn.Module):
def __init__(self, config: AutoformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = AutoformerAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=con... | class_definition | 37,143 | 44,447 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,650 |
class AutoformerPreTrainedModel(PreTrainedModel):
config_class = AutoformerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1... | class_definition | 44,450 | 45,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,651 |
class AutoformerEncoder(AutoformerPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`AutoformerEncoderLayer`].
Args:
config: AutoformerConfig
"""
def __init__(self, config: AutoformerConfig):
super().__init__... | class_definition | 55,011 | 61,452 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,652 |
class AutoformerDecoder(AutoformerPreTrainedModel):
"""
Transformer decoder consisting of `config.decoder_layers` layers. Each layer is a [`AutoformerDecoderLayer`]
Args:
config: AutoformerConfig
"""
def __init__(self, config: AutoformerConfig):
super().__init__(config)
sel... | class_definition | 61,455 | 73,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,653 |
class AutoformerModel(AutoformerPreTrainedModel):
def __init__(self, config: AutoformerConfig):
super().__init__(config)
if config.scaling == "mean" or config.scaling is True:
self.scaler = AutoformerMeanScaler(config)
elif config.scaling == "std":
self.scaler = Auto... | class_definition | 73,383 | 88,741 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,654 |
class AutoformerForPrediction(AutoformerPreTrainedModel):
def __init__(self, config: AutoformerConfig):
super().__init__(config)
self.model = AutoformerModel(config)
if config.distribution_output == "student_t":
self.distribution_output = StudentTOutput(dim=config.input_size)
... | class_definition | 88,890 | 108,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/autoformer/modeling_autoformer.py | null | 6,655 |
class CamembertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" CamemBERT tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#mo... | class_definition | 1,268 | 8,271 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/tokenization_camembert_fast.py | null | 6,656 |
class TFCamembertEmbeddings(keras.layers.Layer):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.padding_idx = 1
self.config = config
self.hidden_size = config.hid... | class_definition | 7,829 | 12,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,657 |
class TFCamembertPooler(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tan... | class_definition | 12,121 | 13,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,658 |
class TFCamembertSelfAttention(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of ... | class_definition | 13,200 | 20,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,659 |
class TFCamembertSelfOutput(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self... | class_definition | 20,129 | 21,466 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,660 |
class TFCamembertAttention(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFCamembertSelfAttention(config, name="self")
self.dense_output = TFCamembertSelfOutput(config, name="output")
def prune_heads(self, h... | class_definition | 21,562 | 23,414 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,661 |
class TFCamembertIntermediate(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 23,513 | 24,545 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,662 |
class TFCamembertOutput(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Lay... | class_definition | 24,638 | 25,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,663 |
class TFCamembertLayer(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFCamembertAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
... | class_definition | 26,069 | 30,828 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,664 |
class TFCamembertEncoder(keras.layers.Layer):
def __init__(self, config: CamembertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFCamembertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidd... | class_definition | 30,922 | 34,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,665 |
class TFCamembertMainLayer(keras.layers.Layer):
config_class = CamembertConfig
def __init__(self, config, add_pooling_layer=True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.num_hidden_layers = config.num_hidden_layers
... | class_definition | 34,146 | 44,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,666 |
class TFCamembertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CamembertConfig
base_model_prefix = "roberta" | class_definition | 44,633 | 44,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,667 |
class TFCamembertModel(TFCamembertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roberta = TFCamembertMainLayer(config, name="roberta")
@unpack_inputs
@add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format(... | class_definition | 45,187 | 49,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,668 |
class TFCamembertLMHead(keras.layers.Layer):
"""Camembert Head for masked language modeling."""
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.dense = keras.layers.Dense(
... | class_definition | 49,162 | 51,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,669 |
class TFCamembertForMaskedLM(TFCamembertPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]
def __init__(self, config, *in... | class_definition | 51,837 | 55,522 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,670 |
class TFCamembertClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.in... | class_definition | 55,615 | 57,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,671 |
class TFCamembertForSequenceClassification(TFCamembertPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
def __init__(self, config, *inp... | class_definition | 57,544 | 61,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,672 |
class TFCamembertForTokenClassification(TFCamembertPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
_keys_to_ignore_on_load_missing = [r"d... | class_definition | 61,377 | 65,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,673 |
class TFCamembertForMultipleChoice(TFCamembertPreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"lm_head"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __i... | class_definition | 65,538 | 69,764 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,674 |
class TFCamembertForQuestionAnswering(TFCamembertPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
def __init__(self, config, *inputs, **kwa... | class_definition | 70,197 | 74,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,675 |
class TFCamembertForCausalLM(TFCamembertPreTrainedModel, TFCausalLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]
def __init__(self, config: Cam... | class_definition | 74,907 | 81,529 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_tf_camembert.py | null | 6,676 |
class CamembertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(... | class_definition | 2,986 | 7,168 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,677 |
class CamembertSelfAttention(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_siz... | class_definition | 7,275 | 14,627 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,678 |
class CamembertSdpaSelfAttention(CamembertSelfAttention):
def __init__(self, config, position_embedding_type=None):
super().__init__(config, position_embedding_type=position_embedding_type)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = version.parse(ge... | class_definition | 14,738 | 20,371 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,679 |
class CamembertSelfOutput(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)
... | class_definition | 20,475 | 21,086 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,680 |
class CamembertAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = CAMEMBERT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = CamembertSe... | class_definition | 21,326 | 23,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,681 |
class CamembertIntermediate(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.i... | class_definition | 23,566 | 24,136 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,682 |
class CamembertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 24,233 | 24,846 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,683 |
class CamembertLayer(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 = CamembertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention ... | class_definition | 24,945 | 28,877 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,684 |
class CamembertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([CamembertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_stat... | class_definition | 28,978 | 32,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,685 |
class CamembertPooler(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 ... | class_definition | 32,845 | 33,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,686 |
class CamembertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CamembertConfig
base_model_prefix = "roberta"
supports_gradient_checkpointing = True
_supports... | class_definition | 33,412 | 34,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,687 |
class CamembertClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifi... | class_definition | 37,410 | 38,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,688 |
class CamembertLMHead(nn.Module):
"""Camembert Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.... | class_definition | 38,285 | 39,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,689 |
class CamembertModel(CamembertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by Ashish Vas... | class_definition | 39,517 | 50,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,690 |
class CamembertForMaskedLM(CamembertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `CamembertForMaskedLM` make s... | class_definition | 50,715 | 54,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,691 |
class CamembertForSequenceClassification(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta = CamembertModel(config, add_pooling_layer=False)
self.classifier = CamembertClassif... | class_definition | 54,985 | 58,997 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,692 |
class CamembertForMultipleChoice(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta = CamembertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weigh... | class_definition | 59,366 | 63,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,693 |
class CamembertForTokenClassification(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = CamembertModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if co... | class_definition | 63,473 | 66,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,694 |
class CamembertForQuestionAnswering(CamembertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = CamembertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
... | class_definition | 67,064 | 71,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,695 |
class CamembertForCausalLM(CamembertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `CamembertLMHeadModel` as... | class_definition | 71,715 | 78,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/modeling_camembert.py | null | 6,696 |
class CamembertTokenizer(PreTrainedTokenizer):
"""
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Construct a CamemBERT tokenizer. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. ... | class_definition | 1,067 | 13,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/tokenization_camembert.py | null | 6,697 |
class CamembertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`CamembertModel`] or a [`TFCamembertModel`]. It is
used to instantiate a Camembert model according to the specified arguments, defining the model architecture.
Instantiating a configuration with... | class_definition | 958 | 6,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/configuration_camembert.py | null | 6,698 |
class CamembertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 6,898 | 7,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/camembert/configuration_camembert.py | null | 6,699 |
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