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 GraniteMoeModel(GraniteMoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GraniteMoeDecoderLayer`]
Args:
config: GraniteMoeConfig
"""
def __init__(self, config: GraniteMoeConfig):
super().__init__(config)
se... | class_definition | 44,703 | 59,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 5,000 |
class GraniteMoeForCausalLM(GraniteMoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: GraniteMoeConfig):
super().__init__(config)
self.model = GraniteMoeModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Line... | class_definition | 59,103 | 65,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 5,001 |
class GraniteMoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteMoeModel`]. It is used to instantiate an GraniteMoe
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yi... | class_definition | 1,150 | 9,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/configuration_granitemoe.py | null | 5,002 |
class PvtConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | class_definition | 1,041 | 6,445 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/configuration_pvt.py | null | 5,003 |
class PvtOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
... | class_definition | 6,448 | 6,918 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/configuration_pvt.py | null | 5,004 |
class PvtImageProcessor(BaseImageProcessor):
r"""
Constructs a PVT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
size["width"])`. Can be overridden by the... | class_definition | 1,303 | 13,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/image_processing_pvt.py | null | 5,005 |
class PvtDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.... | class_definition | 2,942 | 3,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,006 |
class PvtPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(
self,
... | class_definition | 3,422 | 6,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,007 |
class PvtSelfOutput(nn.Module):
def __init__(self, config: PvtConfig, hidden_size: int):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidd... | class_definition | 6,906 | 7,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,008 |
class PvtEfficientSelfAttention(nn.Module):
"""Efficient self-attention mechanism with reduction of the sequence [PvT paper](https://arxiv.org/abs/2102.12122)."""
def __init__(
self, config: PvtConfig, hidden_size: int, num_attention_heads: int, sequences_reduction_ratio: float
):
super()._... | class_definition | 7,347 | 11,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,009 |
class PvtAttention(nn.Module):
def __init__(
self, config: PvtConfig, hidden_size: int, num_attention_heads: int, sequences_reduction_ratio: float
):
super().__init__()
self.self = PvtEfficientSelfAttention(
config,
hidden_size=hidden_size,
num_attenti... | class_definition | 11,232 | 13,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,010 |
class PvtFFN(nn.Module):
def __init__(
self,
config: PvtConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
):
super().__init__()
out_features = out_features if out_features is not None else in_features
... | class_definition | 13,019 | 14,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,011 |
class PvtLayer(nn.Module):
def __init__(
self,
config: PvtConfig,
hidden_size: int,
num_attention_heads: int,
drop_path: float,
sequences_reduction_ratio: float,
mlp_ratio: float,
):
super().__init__()
self.layer_norm_1 = nn.LayerNorm(hidde... | class_definition | 14,075 | 15,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,012 |
class PvtEncoder(nn.Module):
def __init__(self, config: PvtConfig):
super().__init__()
self.config = config
# stochastic depth decay rule
drop_path_decays = torch.linspace(0, config.drop_path_rate, sum(config.depths)).tolist()
# patch embeddings
embeddings = []
... | class_definition | 15,764 | 19,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,013 |
class PvtPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PvtConfig
base_model_prefix = "pvt"
main_input_name = "pixel_values"
_no_split_modules = []
def... | class_definition | 19,558 | 21,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,014 |
class PvtModel(PvtPreTrainedModel):
def __init__(self, config: PvtConfig):
super().__init__(config)
self.config = config
# hierarchical Transformer encoder
self.encoder = PvtEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def _p... | class_definition | 22,601 | 24,768 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,015 |
class PvtForImageClassification(PvtPreTrainedModel):
def __init__(self, config: PvtConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.pvt = PvtModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1], ... | class_definition | 24,997 | 28,414 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/pvt/modeling_pvt.py | null | 5,016 |
class HeliumRMSNorm(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):
input_dtype = hidden_states.dtype
hidden_states = hidden_states... | class_definition | 1,204 | 1,871 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,017 |
class HeliumRotaryEmbedding(LlamaRotaryEmbedding):
pass | class_definition | 1,874 | 1,933 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,018 |
class HeliumMLP(LlamaMLP):
pass | class_definition | 1,936 | 1,971 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,019 |
class HeliumAttention(GraniteAttention):
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.scaling = 1 / math.sqrt(self.head_dim) | class_definition | 3,873 | 4,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,020 |
class HeliumDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__()
self.mlp = HeliumMLP(config)
self.input_layernorm = HeliumRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = H... | class_definition | 4,175 | 4,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,021 |
class HeliumPreTrainedModel(LlamaPreTrainedModel):
pass | class_definition | 4,555 | 4,614 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,022 |
class HeliumModel(HeliumPreTrainedModel, LlamaModel):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[HeliumDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = HeliumRMSNorm(confi... | class_definition | 4,617 | 5,158 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,023 |
class HeliumForCausalLM(GemmaForCausalLM):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.post_init() | class_definition | 5,161 | 5,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,024 |
class HeliumForSequenceClassification(GemmaForSequenceClassification):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.post_init() | class_definition | 5,351 | 5,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,025 |
class HeliumForTokenClassification(GemmaForTokenClassification):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.post_init() | class_definition | 5,569 | 5,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modular_helium.py | null | 5,026 |
class HeliumConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HeliumModel`]. It is used to instantiate an Helium
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simila... | class_definition | 695 | 6,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/configuration_helium.py | null | 5,027 |
class HeliumRMSNorm(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):
input_dtype = hidden_states.dtype
hidden_states = hidden_states... | class_definition | 2,503 | 3,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,028 |
class HeliumRotaryEmbedding(nn.Module):
def __init__(self, config: HeliumConfig, 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_type", ... | class_definition | 3,173 | 6,370 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,029 |
class HeliumMLP(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=config.mlp_bias)
... | class_definition | 6,373 | 7,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,030 |
class HeliumAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "he... | class_definition | 10,525 | 14,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,031 |
class HeliumDecoderLayer(nn.Module):
def __init__(self, config: HeliumConfig, layer_idx: Optional[int] = None):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = HeliumAttention(config=config, layer_idx=layer_idx)
self.mlp = HeliumMLP(config)
self.in... | class_definition | 14,061 | 16,154 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,032 |
class HeliumPreTrainedModel(PreTrainedModel):
config_class = HeliumConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["HeliumDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_s... | class_definition | 17,180 | 18,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,033 |
class HeliumModel(HeliumPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HeliumDecoderLayer`]
Args:
config: HeliumConfig
"""
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.padding_idx = con... | class_definition | 22,913 | 34,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,034 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 34,143 | 34,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,035 |
class HeliumForCausalLM(HeliumPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.model = HeliumModel(config)
self.vocab_size = config.vocab_size
... | class_definition | 34,208 | 39,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,036 |
class HeliumForSequenceClassification(HeliumPreTrainedModel):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = HeliumModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Ini... | class_definition | 40,070 | 43,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,037 |
class HeliumForTokenClassification(HeliumPreTrainedModel):
def __init__(self, config: HeliumConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = HeliumModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropou... | class_definition | 44,149 | 47,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/helium/modeling_helium.py | null | 5,038 |
class MLukeTokenizer(PreTrainedTokenizer):
"""
Adapted from [`XLMRobertaTokenizer`] and [`LukeTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this supe... | class_definition | 6,040 | 82,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mluke/tokenization_mluke.py | null | 5,039 |
class SuperPointImageProcessor(BaseImageProcessor):
r"""
Constructs a SuperPoint image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden
by `do_resi... | class_definition | 3,275 | 15,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/image_processing_superpoint.py | null | 5,040 |
class SuperPointKeypointDescriptionOutput(ModelOutput):
"""
Base class for outputs of image point description models. Due to the nature of keypoint detection, the number of
keypoints is not fixed and can vary from image to image, which makes batching non-trivial. In the batch of images,
the maximum numb... | class_definition | 2,758 | 4,837 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,041 |
class SuperPointConvBlock(nn.Module):
def __init__(
self, config: SuperPointConfig, in_channels: int, out_channels: int, add_pooling: bool = False
) -> None:
super().__init__()
self.conv_a = nn.Conv2d(
in_channels,
out_channels,
kernel_size=3,
... | class_definition | 4,840 | 5,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,042 |
class SuperPointEncoder(nn.Module):
"""
SuperPoint encoder module. It is made of 4 convolutional layers with ReLU activation and max pooling, reducing the
dimensionality of the image.
"""
def __init__(self, config: SuperPointConfig) -> None:
super().__init__()
# SuperPoint uses 1 c... | class_definition | 5,810 | 7,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,043 |
class SuperPointInterestPointDecoder(nn.Module):
"""
The SuperPointInterestPointDecoder uses the output of the SuperPointEncoder to compute the keypoint with scores.
The scores are first computed by a convolutional layer, then a softmax is applied to get a probability distribution
over the 65 possible k... | class_definition | 7,593 | 10,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,044 |
class SuperPointDescriptorDecoder(nn.Module):
"""
The SuperPointDescriptorDecoder uses the outputs of both the SuperPointEncoder and the
SuperPointInterestPointDecoder to compute the descriptors at the keypoints locations.
The descriptors are first computed by a convolutional layer, then normalized to ... | class_definition | 10,826 | 13,573 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,045 |
class SuperPointPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SuperPointConfig
base_model_prefix = "superpoint"
main_input_name = "pixel_values"
supports_g... | class_definition | 13,576 | 15,315 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,046 |
class SuperPointForKeypointDetection(SuperPointPreTrainedModel):
"""
SuperPoint model. It consists of a SuperPointEncoder, a SuperPointInterestPointDecoder and a
SuperPointDescriptorDecoder. SuperPoint was proposed in `SuperPoint: Self-Supervised Interest Point Detection and
Description <https://arxiv.o... | class_definition | 16,664 | 21,577 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/modeling_superpoint.py | null | 5,047 |
class SuperPointConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SuperPointForKeypointDetection`]. It is used to instantiate a
SuperPoint model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the de... | class_definition | 754 | 4,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/superpoint/configuration_superpoint.py | null | 5,048 |
class AxialPositionEmbeddings(nn.Module):
"""
Constructs axial position embeddings. Useful for very long input sequences to save memory and time.
"""
def __init__(self, config):
super().__init__()
self.axial_pos_shape = config.axial_pos_shape
self.axial_pos_embds_dim = config.ax... | class_definition | 4,397 | 8,686 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,049 |
class PositionEmbeddings(nn.Module):
"""Constructs conventional position embeddings of shape `[max_pos_embeddings, hidden_size]`."""
def __init__(self, config):
super().__init__()
self.dropout = config.hidden_dropout_prob
self.embedding = nn.Embedding(config.max_position_embeddings, con... | class_definition | 8,689 | 9,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,050 |
class ReformerEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.max_position_embeddings = config.max_position_embeddings
self.dropout = config.hidden_dropout_prob
self.word_em... | class_definition | 9,273 | 11,124 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,051 |
class EfficientAttentionMixin:
"""
A few utilities for nn.Modules in Reformer, to be used as a mixin.
"""
def _look_adjacent(self, vectors, num_chunks_before, num_chunks_after):
"""
Used to implement attention between consecutive chunks.
Args:
vectors: array of shap... | class_definition | 11,127 | 13,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,052 |
class LSHSelfAttention(nn.Module, EfficientAttentionMixin):
def __init__(self, config):
super().__init__()
self.config = config
self.chunk_length = config.lsh_attn_chunk_length
self.num_hashes = config.num_hashes
self.num_buckets = config.num_buckets
self.num_chunks_... | class_definition | 13,495 | 43,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,053 |
class ReverseSort(Function):
"""
After chunked attention is applied which sorted clusters, original ordering has to be restored. Since customized
backward function is used for Reformer, the gradients of the output vectors have to be explicitly sorted here.
"""
@staticmethod
def forward(ctx, out... | class_definition | 43,652 | 45,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,054 |
class LocalSelfAttention(nn.Module, EfficientAttentionMixin):
def __init__(self, config):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.chunk_length = config.local_attn_chunk_length
self.num_chunks_before = config.local_num_chunks_before
self.... | class_definition | 45,087 | 54,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,055 |
class ReformerSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
all_head_size = config.num_attention_heads * config.attention_head_size
self.dropout = config.hidden_dropout_prob
self.dense = nn.Linear(all_head_size, config.hidden_size, bias=False)
def forwar... | class_definition | 54,505 | 55,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,056 |
class ReformerAttention(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.layer_id = layer_id
self.attn_layers = config.attn_layers
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
if len(set(self.attn_layers)) == 1 and... | class_definition | 55,031 | 58,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,057 |
class ReformerFeedForwardDense(nn.Module):
def __init__(self, config):
super().__init__()
self.dropout = config.hidden_dropout_prob
if isinstance(config.hidden_act, str):
self.act_fn = ACT2FN[config.hidden_act]
else:
self.act_fn = config.hidden_act
s... | class_definition | 58,683 | 59,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,058 |
class ReformerFeedForwardOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dropout = config.hidden_dropout_prob
self.dense = nn.Linear(config.feed_forward_size, config.hidden_size)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
... | class_definition | 59,343 | 59,792 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,059 |
class ChunkReformerFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dense = ReformerFee... | class_definition | 59,795 | 60,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,060 |
class ReformerLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.attention = ReformerAttention(config, layer_id)
# dropout requires to have the same
# seed for forward and backward pass
self.attention_seed = None
self.feed_forward_seed =... | class_definition | 60,615 | 66,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,061 |
class _ReversibleFunction(Function):
"""
To prevent PyTorch from performing the usual backpropagation, a customized backward function is implemented here.
This way it is made sure that no memory expensive activations are saved during the forward pass. This function is
heavily inspired by https://github.... | class_definition | 66,935 | 71,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,062 |
class ReformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.dropout = config.hidden_dropout_prob
self.layers = nn.ModuleList([ReformerLayer(config, i) for i in range(config.num_hidden_layers)])
# Reformer is using Rev Nets, thus last layer outputs are conca... | class_definition | 71,041 | 73,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,063 |
class ReformerOnlyLMHead(nn.Module):
def __init__(self, config):
super().__init__()
# Reformer is using Rev Nets, thus last layer outputs are concatenated and
# Layer Norm is done over 2 * hidden_size
self.seq_len_dim = 1
self.chunk_size_lm_head = config.chunk_size_lm_head
... | class_definition | 73,122 | 74,281 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,064 |
class ReformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ReformerConfig
base_model_prefix = "reformer"
@property
def dummy_inputs(self):
input_... | class_definition | 74,284 | 75,806 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,065 |
class ReformerModelOutput(ModelOutput):
"""
Output type of [`ReformerModel`].
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
`num_predict` corresponds to `target_mapp... | class_definition | 75,820 | 78,056 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,066 |
class ReformerModelWithLMHeadOutput(ModelOutput):
"""
Output type of [`ReformerModelWithLMHead`].
Args:
loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided)
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of s... | class_definition | 78,070 | 80,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,067 |
class ReformerModel(ReformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
assert (
self.config.num_hidden_layers > 0
), "`config.attn_layers` is empty. Select at least one attn layer form ['lsh', 'local']"
self.embedd... | class_definition | 85,441 | 94,455 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,068 |
class ReformerModelWithLMHead(ReformerPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
assert config.is_decoder, "If you want to use `ReformerModelWithLMHead` make sure that `is_decode... | class_definition | 94,568 | 99,804 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,069 |
class ReformerForMaskedLM(ReformerPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
assert not config.is_decoder, (
"If you want to use `ReformerForMaskedLM` make sure `config.is_decoder=Fal... | class_definition | 99,917 | 104,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,070 |
class ReformerForSequenceClassification(ReformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.reformer = ReformerModel(config)
self.classifier = ReformerClassificationHead(config)
... | class_definition | 105,099 | 110,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,071 |
class ReformerClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(2 * config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.class... | class_definition | 110,003 | 110,935 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,072 |
class ReformerForQuestionAnswering(ReformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.reformer = ReformerModel(config)
# 2 * config.hidden_size because we use reversible residual layers
self.qa_outputs = nn... | class_definition | 111,236 | 115,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/modeling_reformer.py | null | 5,073 |
class ReformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ReformerModel`]. It is used to instantiate a
Reformer model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | class_definition | 849 | 13,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/configuration_reformer.py | null | 5,074 |
class ReformerTokenizer(PreTrainedTokenizer):
"""
Construct a Reformer tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece) .
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information ... | class_definition | 996 | 6,725 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/tokenization_reformer.py | null | 5,075 |
class ReformerTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Reformer tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTok... | class_definition | 1,150 | 4,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/reformer/tokenization_reformer_fast.py | null | 5,076 |
class SEWConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SEWModel`]. It is used to instantiate a SEW model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | class_definition | 829 | 14,180 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/configuration_sew.py | null | 5,077 |
class SEWNoLayerNormConvLayer(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 | 7,414 | 8,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,078 |
class SEWLayerNormConvLayer(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,
s... | class_definition | 8,248 | 9,222 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,079 |
class SEWGroupNormConvLayer(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,
s... | class_definition | 9,332 | 10,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,080 |
class SEWPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
... | class_definition | 10,227 | 11,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,081 |
class SEWSamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -self.num_... | class_definition | 12,047 | 12,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,082 |
class SEWUpsampling(nn.Module):
def __init__(self, config):
super().__init__()
self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor)
self.activation = ACT2FN[config.feat_extract_activation]
self.squeeze_factor = config.squeeze_factor
def for... | class_definition | 12,410 | 13,354 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,083 |
class SEWFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [SEWGroupNormConvLayer(config, layer_id=0)] + [
SEWNoLayerNormConvLayer(conf... | class_definition | 13,460 | 15,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,084 |
class SEWFeatureExtractor(SEWFeatureEncoder):
def __init__(self, config):
super().__init__(config)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self.__class__.__bases__[0].__name__... | class_definition | 15,147 | 15,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,085 |
class SEWAttention(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,
conf... | class_definition | 15,602 | 22,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,086 |
class SEWFlashAttention2(SEWAttention):
"""
SEW flash attention module. This module inherits from `SEWAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with padding to... | class_definition | 23,081 | 29,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,087 |
class SEWSdpaAttention(SEWAttention):
# Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with Bart->SEW
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = Non... | class_definition | 29,514 | 35,385 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,088 |
class SEWFeedForward(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):
self... | class_definition | 35,620 | 36,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,089 |
class SEWEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = SEW_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
... | class_definition | 36,704 | 38,048 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,090 |
class SEWEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = SEWPositionalConvEmbedding(config)
self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor)
self.layer_norm = nn.LayerNorm(config.hidden_size, ... | class_definition | 38,051 | 43,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,091 |
class SEWPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SEWConfig
base_model_prefix = "sew"
main_input_name = "input_values"
supports_gradient_checkpointing... | class_definition | 43,240 | 46,643 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,092 |
class SEWModel(SEWPreTrainedModel):
def __init__(self, config: SEWConfig):
super().__init__(config)
self.config = config
self.feature_extractor = SEWFeatureEncoder(config)
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.project_features = ... | class_definition | 49,369 | 54,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,093 |
class SEWForCTC(SEWPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.sew = SEWModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:
... | class_definition | 55,138 | 61,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,094 |
class SEWForSequenceClassification(SEWPreTrainedModel):
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 SEW adapters (config.add_adapter=... | class_definition | 62,261 | 67,320 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/sew/modeling_sew.py | null | 5,095 |
class OneFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a OneFormer image processor. The image processor can be used to prepare image(s), task input(s) and
optional text inputs and targets for the model.
This image processor inherits from [`BaseImageProcessor`] which contains most of the mai... | class_definition | 13,273 | 61,225 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/image_processing_oneformer.py | null | 5,096 |
class TrackedStateDict:
def __init__(self, to_track: Dict):
"""This class "tracks" a python dictionary by keeping track of which item is accessed.
Args:
to_track (Dict): The dictionary we wish to track
"""
self.to_track = to_track
self._seen: Set[str] = set()
... | class_definition | 1,925 | 2,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/convert_to_hf_oneformer.py | null | 5,097 |
class Args:
"""Fake command line arguments needed by oneformer/detectron2 implementation"""
config_file: str | class_definition | 3,138 | 3,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/convert_to_hf_oneformer.py | null | 5,098 |
class OriginalOneFormerConfigToOursConverter:
def __call__(self, original_config: object, is_swin: bool) -> OneFormerConfig:
model = original_config.MODEL
dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST_PANOPTIC[0])
id2label = dict(enumerate(dataset_catalog.stuff_classes... | class_definition | 3,572 | 7,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/oneformer/convert_to_hf_oneformer.py | null | 5,099 |
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