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 Data2VecTextSelfOutput(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 | 13,638 | 14,252 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,400 |
class Data2VecTextAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = DATA2VEC_TEXT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = Data... | class_definition | 14,451 | 16,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,401 |
class Data2VecTextIntermediate(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:
sel... | class_definition | 16,671 | 17,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,402 |
class Data2VecTextOutput(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 | 17,311 | 17,927 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,403 |
class Data2VecTextLayer(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 = Data2VecTextAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_atte... | class_definition | 18,017 | 21,964 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,404 |
class Data2VecTextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([Data2VecTextLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidde... | class_definition | 22,056 | 25,862 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,405 |
class Data2VecTextPooler(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 t... | class_definition | 25,929 | 26,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,406 |
class Data2VecTextPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Data2VecTextConfig
base_model_prefix = "data2vec_text"
supports_gradient_checkpointing = True
... | class_definition | 26,499 | 27,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,407 |
class Data2VecTextModel(Data2VecTextPreTrainedModel):
"""
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 Ashi... | class_definition | 31,821 | 41,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,408 |
class Data2VecTextForCausalLM(Data2VecTextPreTrainedModel, 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 `Data2VecTextLMHead... | class_definition | 41,270 | 47,949 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,409 |
class Data2VecTextForMaskedLM(Data2VecTextPreTrainedModel):
_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 `Data2VecTextForMaskedL... | class_definition | 48,066 | 51,850 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,410 |
class Data2VecTextLMHead(nn.Module):
"""Data2VecText 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)
... | class_definition | 51,953 | 53,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,411 |
class Data2VecTextForSequenceClassification(Data2VecTextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.data2vec_text = Data2VecTextModel(config, add_pooling_layer=False)
self.classifier = D... | class_definition | 53,263 | 57,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,412 |
class Data2VecTextForMultipleChoice(Data2VecTextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.data2vec_text = Data2VecTextModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# I... | class_definition | 57,376 | 61,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,413 |
class Data2VecTextForTokenClassification(Data2VecTextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.data2vec_text = Data2VecTextModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifie... | class_definition | 61,295 | 64,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,414 |
class Data2VecTextClassificationHead(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.class... | class_definition | 64,366 | 65,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,415 |
class Data2VecTextForQuestionAnswering(Data2VecTextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.data2vec_text = Data2VecTextModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, confi... | class_definition | 65,447 | 69,720 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | null | 5,416 |
class Data2VecAudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Data2VecAudioModel`]. It is used to instantiate
an Data2VecAudio model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the default... | class_definition | 796 | 16,320 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/configuration_data2vec_audio.py | null | 5,417 |
class Data2VecAudioConvLayer(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 | 7,295 | 8,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,418 |
class Data2VecAudioPadLayer(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[:, :, : -sel... | class_definition | 8,384 | 8,750 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,419 |
class Data2VecAudioPositionalConvLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.conv_pos_kernel_size,
padding=config.conv_pos_kernel_size // 2,
... | class_definition | 8,753 | 9,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,420 |
class Data2VecAudioPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList(
[Data2VecAudioPositionalConvLayer(config) for _ in range(config.num_conv_pos_embeddings)]
)
def forward(self, hidden_states):
hidden_sta... | class_definition | 9,781 | 10,302 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,421 |
class Data2VecAudioFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
self.conv_layers = nn.ModuleList(
[Data2VecAudioConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)]
)
... | class_definition | 10,305 | 11,710 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,422 |
class Data2VecAudioFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_pr... | class_definition | 11,829 | 12,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,423 |
class Data2VecAudioAttention(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 | 12,581 | 19,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,424 |
class Data2VecAudioFlashAttention2(Data2VecAudioAttention):
"""
Data2VecAudio flash attention module. This module inherits from `Data2VecAudioAttention` 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
... | class_definition | 20,090 | 26,580 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,425 |
class Data2VecAudioSdpaAttention(Data2VecAudioAttention):
# Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with Bart->Data2VecAudio
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optio... | class_definition | 26,583 | 32,504 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,426 |
class Data2VecAudioFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
... | class_definition | 32,790 | 33,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,427 |
class Data2VecAudioEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = DATA2VEC2AUDIO_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_... | class_definition | 33,905 | 35,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,428 |
class Data2VecAudioEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = Data2VecAudioPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropou... | class_definition | 35,389 | 39,240 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,429 |
class Data2VecAudioAdapter(nn.Module):
def __init__(self, config):
super().__init__()
# feature dim might need to be down-projected
if config.output_hidden_size != config.hidden_size:
self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
self.proj_laye... | class_definition | 39,349 | 40,562 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,430 |
class Data2VecAudioAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.output_hidden_size,
2 * config.output_hidden_size,
config.adapter_kernel_size,
stride=config.adapter_stride,
padding=1,... | class_definition | 40,676 | 41,188 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,431 |
class Data2VecAudioPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Data2VecAudioConfig
base_model_prefix = "data2vec_audio"
main_input_name = "input_values"
... | class_definition | 41,191 | 45,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,432 |
class Data2VecAudioModel(Data2VecAudioPreTrainedModel):
def __init__(self, config: Data2VecAudioConfig):
super().__init__(config)
self.config = config
self.feature_extractor = Data2VecAudioFeatureEncoder(config)
self.feature_projection = Data2VecAudioFeatureProjection(config)
... | class_definition | 48,480 | 54,203 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,433 |
class Data2VecAudioForCTC(Data2VecAudioPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.data2vec_audio = Data2VecAudioModel(config)
self.dropout = nn.Dropout(config.final_dropout)
if config.vocab_size is None:
raise ValueError(
... | class_definition | 54,386 | 59,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,434 |
class Data2VecAudioForSequenceClassification(Data2VecAudioPreTrainedModel):
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 Data2VecAudio... | class_definition | 59,928 | 65,117 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,435 |
class Data2VecAudioForAudioFrameClassification(Data2VecAudioPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if hasattr(config, "add_adapter") and config.add_adapter:
raise ValueError(
"Audio frame classification does not support the use of Data2Vec... | class_definition | 65,298 | 69,881 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,436 |
class AMSoftmaxLoss(nn.Module):
def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
super(AMSoftmaxLoss, self).__init__()
self.scale = scale
self.margin = margin
self.num_labels = num_labels
self.weight = nn.Parameter(torch.randn(input_dim, num_labels), require... | class_definition | 69,959 | 70,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,437 |
class TDNNLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
self.out_conv_dim = config.tdnn_dim[layer_id]
self.kernel_size = config.tdnn_kernel[layer_id]
... | class_definition | 70,909 | 72,339 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,438 |
class Data2VecAudioForXVector(Data2VecAudioPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.data2vec_audio = Data2VecAudioModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
... | class_definition | 72,528 | 78,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | null | 5,439 |
class DinatEncoderOutput(ModelOutput):
"""
Dinat encoder'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 | 2,183 | 4,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,440 |
class DinatModelOutput(ModelOutput):
"""
Dinat model's outputs that also contains a pooling of the last hidden states.
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 mo... | class_definition | 4,164 | 6,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,441 |
class DinatImageClassifierOutput(ModelOutput):
"""
Dinat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` o... | class_definition | 6,409 | 8,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,442 |
class DinatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = DinatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden... | class_definition | 8,550 | 9,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,443 |
class DinatPatchEmbeddings(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, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config)... | class_definition | 9,151 | 10,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,444 |
class DinatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn... | class_definition | 10,601 | 11,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,445 |
class DinatDropPath(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) -> torc... | class_definition | 12,637 | 13,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,446 |
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
... | class_definition | 13,119 | 16,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,447 |
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
... | class_definition | 16,187 | 16,637 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,448 |
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size, dilation):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size, dilation)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_head... | class_definition | 16,640 | 18,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,449 |
class DinatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate... | class_definition | 18,230 | 18,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,450 |
class DinatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.d... | class_definition | 18,792 | 19,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,451 |
class DinatLayer(nn.Module):
def __init__(self, config, dim, num_heads, dilation, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.dilation = dilation
self.window_size = self.kerne... | class_definition | 19,215 | 22,293 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,452 |
class DinatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, dilations, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
DinatLayer(
config=config,
... | class_definition | 22,296 | 23,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,453 |
class DinatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
... | class_definition | 23,819 | 26,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,454 |
class DinatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DinatConfig
base_model_prefix = "dinat"
main_input_name = "pixel_values"
def _init_weights(self, ... | class_definition | 26,935 | 27,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,455 |
class DinatModel(DinatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_level... | class_definition | 29,397 | 32,560 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,456 |
class DinatForImageClassification(DinatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.dinat = DinatModel(config)
# Classifier head
self.classifier = (
n... | class_definition | 32,793 | 36,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,457 |
class DinatBackbone(DinatPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
requires_backends(self, ["natten"])
self.embeddings = DinatEmbeddings(config)
self.encoder = DinatEncoder(config)
self.num_f... | class_definition | 36,401 | 40,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/modeling_dinat.py | null | 5,458 |
class DinatConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults wi... | class_definition | 919 | 7,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinat/configuration_dinat.py | null | 5,459 |
class ZambaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ZambaModel`]. It is used to instantiate a
Zamba model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar co... | class_definition | 819 | 11,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/configuration_zamba.py | null | 5,460 |
class ZambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
ZambaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 2,768 | 3,488 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,461 |
class ZambaHybridDynamicCache(DynamicCache):
"""
A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
(which has a constant shape regardless of seq_len).
This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cach... | class_definition | 4,209 | 9,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,462 |
class ZambaAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
Adapted from transformers.models.mistral.modeling_mistral.MistralAttention:
The input di... | class_definition | 10,345 | 14,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,463 |
class ZambaMambaMixer(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... | class_definition | 14,293 | 28,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,464 |
class ZambaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | class_definition | 29,086 | 29,754 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,465 |
class ZambaAttentionDecoderLayer(nn.Module):
def __init__(self, config: ZambaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.self_attn = ZambaAttention(config, layer_idx)
self.feed_forward = ZambaMLP(config)
self.input_layernorm = ZambaRMSNorm(config.attention_hidd... | class_definition | 29,757 | 33,335 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,466 |
class ZambaMambaDecoderLayer(nn.Module):
def __init__(self, config: ZambaConfig, layer_idx: int):
super().__init__()
self.mamba = ZambaMambaMixer(config=config, layer_idx=layer_idx)
self.input_layernorm = ZambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layer_idx = laye... | class_definition | 33,338 | 36,430 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,467 |
class ZambaHybridLayer(nn.Module):
def __init__(self, shared_transf: ZambaAttentionDecoderLayer, linear: nn.Linear, mamba: ZambaMambaDecoderLayer):
super().__init__()
self.shared_transf = shared_transf
self.linear = linear
self.mamba_decoder = mamba
def forward(
self,
... | class_definition | 36,433 | 39,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,468 |
class ZambaPreTrainedModel(PreTrainedModel):
config_class = ZambaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["ZambaAttentionDecoderLayer", "ZambaMambaDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = False
... | class_definition | 40,818 | 43,747 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,469 |
class ZambaModel(ZambaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ZambaDecoderLayer`]
Args:
config: ZambaConfig
"""
def __init__(self, config: ZambaConfig):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 48,258 | 57,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,470 |
class ZambaForCausalLM(ZambaPreTrainedModel, GenerationMixin):
def __init__(self, config: ZambaConfig):
super().__init__(config)
self.model = ZambaModel(config)
self._tied_weights_keys = ["lm_head.weight", *self.model._tied_weights_keys]
self.vocab_size = config.vocab_size
se... | class_definition | 57,978 | 65,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,471 |
class ZambaForSequenceClassification(ZambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = ZambaModel(config)
self._tied_weights_keys = self.model._tied_weights_keys
self.score = nn.Linear(config.hidden_si... | class_definition | 66,139 | 71,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zamba/modeling_zamba.py | null | 5,472 |
class MambaMixer(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 Mamb... | class_definition | 2,131 | 15,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,473 |
class MambaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
MambaRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidde... | class_definition | 15,980 | 16,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,474 |
class MambaBlock(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 = MambaRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
self.mixe... | class_definition | 16,716 | 17,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,475 |
class MambaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MambaConfig
base_model_prefix = "backbone"
_no_split_modules = ["MambaBlock", "MambaMixer"]
suppor... | class_definition | 17,752 | 20,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,476 |
class MambaOutput(ModelOutput):
"""
Class for the MAMBA 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 (`MambaCache`):
... | class_definition | 20,833 | 22,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,477 |
class MambaCausalLMOutput(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.Floa... | class_definition | 22,114 | 23,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,478 |
class MambaModel(MambaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([MambaBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
self.gradie... | class_definition | 26,731 | 31,429 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,479 |
class MambaForCausalLM(MambaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.backbone = MambaModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize... | class_definition | 31,632 | 37,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/modeling_mamba.py | null | 5,480 |
class MambaConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar con... | class_definition | 768 | 7,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mamba/configuration_mamba.py | null | 5,481 |
class StableLmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~StableLmModel`].
It is used to instantiate an StableLM model according to the specified arguments, defining the model
architecture. Instantiating a configuration with the defaults will yield a... | class_definition | 860 | 10,805 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/configuration_stablelm.py | null | 5,482 |
class StableLmRotaryEmbedding(nn.Module):
def __init__(self, config: StableLmConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_typ... | class_definition | 2,214 | 5,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,483 |
class StableLmMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
s... | class_definition | 7,379 | 8,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,484 |
class StableLmLayerNormPerHead(nn.Module):
def __init__(self, dim, num_heads, eps=1e-5, bias=False):
super().__init__()
self.dim = dim
self.num_heads = num_heads
self.norms = nn.ModuleList([nn.LayerNorm(dim, eps=eps, bias=bias) for _ in range(self.num_heads)])
def forward(self, ... | class_definition | 8,053 | 8,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,485 |
class StableLmAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: StableLmConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | class_definition | 9,480 | 15,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,486 |
class StableLmSdpaAttention(StableLmAttention):
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
... | class_definition | 15,457 | 20,793 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,487 |
class StableLmFlashAttention2(StableLmAttention):
"""
StableLM flash attention module. This module inherits from `StableLmAttention` 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 ... | class_definition | 20,796 | 25,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,488 |
class StableLmDecoderLayer(nn.Module):
def __init__(self, config: StableLmConfig, layer_idx: int):
super().__init__()
self.use_parallel_residual = config.use_parallel_residual
self.hidden_size = config.hidden_size
self.self_attn = ATTENTION_CLASSES[config._attn_implementation](config... | class_definition | 25,645 | 30,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,489 |
class StableLmPreTrainedModel(PreTrainedModel):
config_class = StableLmConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["StableLmDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_cache_class = ... | class_definition | 31,156 | 32,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,490 |
class StableLmModel(StableLmPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`StableLmDecoderLayer`]
Args:
config: StableLmConfig
"""
def __init__(self, config: StableLmConfig):
super().__init__(config)
self.padding... | class_definition | 36,876 | 49,342 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,491 |
class StableLmForCausalLM(StableLmPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->STABLELM,Llama->StableLm
def __init__(self, config):
super().__init__(config)
self.model ... | class_definition | 49,474 | 55,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,492 |
class StableLmForSequenceClassification(StableLmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = StableLmModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | class_definition | 56,454 | 60,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,493 |
class StableLmForTokenClassification(StableLmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = StableLmModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = conf... | class_definition | 60,652 | 63,876 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/stablelm/modeling_stablelm.py | null | 5,494 |
class SpeechEncoderDecoderConfig(PretrainedConfig):
r"""
[`SpeechEncoderDecoderConfig`] is the configuration class to store the configuration of a
[`SpeechEncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified
arguments, defining the encoder and decoder conf... | class_definition | 843 | 4,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_encoder_decoder/configuration_speech_encoder_decoder.py | null | 5,495 |
class FlaxSpeechEncoderDecoderModule(nn.Module):
config: SpeechEncoderDecoderConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
encoder_config = self.config.encoder
decoder_config = self.config.decoder
# Copied from `modeling_hybrid_clip.py` with modifications.
from ...... | class_definition | 12,071 | 17,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_encoder_decoder/modeling_flax_speech_encoder_decoder.py | null | 5,496 |
class FlaxSpeechEncoderDecoderModel(FlaxPreTrainedModel):
r"""
[`FlaxSpeechEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one
as decoder ... | class_definition | 17,083 | 44,641 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_encoder_decoder/modeling_flax_speech_encoder_decoder.py | null | 5,497 |
class SpeechEncoderDecoderModel(PreTrainedModel, GenerationMixin):
r"""
[`SpeechEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with
one of the base model classes of the library as encoder and another one as decoder when created with the
:meth*~tran... | class_definition | 11,274 | 32,082 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py | null | 5,498 |
class ZoeDepthImageProcessor(BaseImageProcessor):
r"""
Constructs a ZoeDepth image processor.
Args:
do_pad (`bool`, *optional*, defaults to `True`):
Whether to apply pad the input.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by t... | class_definition | 2,964 | 28,036 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/image_processing_zoedepth.py | null | 5,499 |
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