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 TFSwinPatchMerging(keras.layers.Layer):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`keras.layer.Layer`, *optional*, defaults to `keras.layers.LayerNorma... | class_definition | 18,107 | 21,246 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,600 |
class TFSwinDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: float = None, scale_by_keep: bool = True, **kwargs) -> None:
super(TFSwinDropPath, self).__init__(**kwargs)
self.drop_prob = dro... | class_definition | 21,249 | 21,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,601 |
class TFSwinSelfAttention(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
super().__init__(**kwargs)
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention ... | class_definition | 21,775 | 28,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,602 |
class TFSwinSelfOutput(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(dim, name="dense")
self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob, name="dropout")
self.... | class_definition | 28,405 | 29,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,603 |
class TFSwinAttention(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
super().__init__(**kwargs)
self.self = TFSwinSelfAttention(config, dim, num_heads, name="self")
self.self_output = TFSwinSelfOutput(config, dim, name="output")
... | class_definition | 29,410 | 30,968 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,604 |
class TFSwinIntermediate(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(int(config.mlp_ratio * dim), name="dense")
if isinstance(config.hidden_act, str):
self.intermediate_act_fn ... | class_definition | 30,971 | 31,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,605 |
class TFSwinOutput(keras.layers.Layer):
def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(dim, name="dense")
self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, "dropout")
self.config = config
... | class_definition | 31,885 | 32,755 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,606 |
class TFSwinLayer(keras.layers.Layer):
def __init__(
self,
config,
dim,
input_resolution: Tuple[int, int],
num_heads: int,
drop_path_rate: float = 0.0,
shift_size: int = 0,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.chunk_... | class_definition | 32,758 | 40,264 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,607 |
class TFSwinStage(keras.layers.Layer):
def __init__(
self,
config: SwinConfig,
dim: int,
input_resolution: Tuple[int, int],
depth: int,
num_heads: int,
drop_path: List[float],
downsample: Optional[Callable],
**kwargs,
) -> None:
sup... | class_definition | 40,267 | 43,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,608 |
class TFSwinEncoder(keras.layers.Layer):
def __init__(self, config: SwinConfig, grid_size: Tuple[int, int], **kwargs):
super().__init__(**kwargs)
self.num_layers = len(config.depths)
self.config = config
dpr = list((tf.linspace(0, 1, sum(config.depths)) * config.drop_path_rate).numpy... | class_definition | 43,097 | 46,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,609 |
class TFSwinPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwinConfig
base_model_prefix = "swin"
main_input_name = "pixel_values" | class_definition | 46,928 | 47,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,610 |
class AdaptiveAveragePooling1D(keras.layers.Layer):
"""
Args:
Average 1D Pooling with adaptive kernel size.
output_size: An integer or tuple/list of a single integer, specifying pooled_features.
The new size of output channels.
data_format: A string,
one of `channels_last` (defau... | class_definition | 49,540 | 52,347 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,611 |
class TFSwinMainLayer(keras.layers.Layer):
config_class = SwinConfig
def __init__(
self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
) -> None:
super().__init__(**kwargs)
self.config = config
self.num_layers = len(config.depths)... | class_definition | 52,370 | 56,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,612 |
class TFSwinModel(TFSwinPreTrainedModel):
def __init__(
self, config: SwinConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
) -> None:
super().__init__(config, **kwargs)
self.config = config
self.swin = TFSwinMainLayer(config, name="swin")
@add_s... | class_definition | 57,145 | 59,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,613 |
class TFSwinPixelShuffle(keras.layers.Layer):
"""TF layer implementation of torch.nn.PixelShuffle"""
def __init__(self, upscale_factor: int, **kwargs) -> None:
super().__init__(**kwargs)
if not isinstance(upscale_factor, int) or upscale_factor < 2:
raise ValueError(f"upscale_factor ... | class_definition | 59,457 | 60,904 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,614 |
class TFSwinDecoder(keras.layers.Layer):
def __init__(self, config: SwinConfig, **kwargs):
super().__init__(**kwargs)
self.conv2d = keras.layers.Conv2D(
filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, strides=1, name="0"
)
self.pixel_shuffle = TFSwi... | class_definition | 60,907 | 62,160 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,615 |
class TFSwinForMaskedImageModeling(TFSwinPreTrainedModel):
def __init__(self, config: SwinConfig):
super().__init__(config)
self.swin = TFSwinMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="swin")
self.decoder = TFSwinDecoder(config, name="decoder")
@add_start_do... | class_definition | 62,347 | 67,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,616 |
class TFSwinForImageClassification(TFSwinPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: SwinConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.swin = TFSwinMainLayer(config, name="swin")
# Classifier head
self.classifier =... | class_definition | 67,712 | 70,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,617 |
class MobileNetV1FeatureExtractor(MobileNetV1ImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use MobileNetV1ImageProcessor instead.",
... | class_definition | 831 | 1,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/feature_extraction_mobilenet_v1.py | null | 3,618 |
class MobileNetV1ImageProcessor(BaseImageProcessor):
r"""
Constructs a MobileNetV1 image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in... | class_definition | 1,360 | 15,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/image_processing_mobilenet_v1.py | null | 3,619 |
class MobileNetV1ConvLayer(nn.Module):
def __init__(
self,
config: MobileNetV1Config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: Optional[int] = 1,
groups: Optional[int] = 1,
bias: bool = False,
use_normalization: Optional[b... | class_definition | 6,894 | 9,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py | null | 3,620 |
class MobileNetV1PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileNetV1Config
load_tf_weights = load_tf_weights_in_mobilenet_v1
base_model_prefix = "mobilen... | class_definition | 9,136 | 10,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py | null | 3,621 |
class MobileNetV1Model(MobileNetV1PreTrainedModel):
def __init__(self, config: MobileNetV1Config, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
depth = 32
out_channels = max(int(depth * config.depth_multiplier), config.min_depth)
self.conv_s... | class_definition | 11,444 | 14,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py | null | 3,622 |
class MobileNetV1ForImageClassification(MobileNetV1PreTrainedModel):
def __init__(self, config: MobileNetV1Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilenet_v1 = MobileNetV1Model(config)
last_hidden_size = self.mobilenet_v1.layer[-1].convo... | class_definition | 15,142 | 18,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py | null | 3,623 |
class MobileNetV1Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MobileNetV1Model`]. It is used to instantiate a
MobileNetV1 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 914 | 4,263 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py | null | 3,624 |
class MobileNetV1OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})])
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.ta... | class_definition | 4,266 | 4,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v1/configuration_mobilenet_v1.py | null | 3,625 |
class TFConvNextDropPath(keras.layers.Layer):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
References:
(1) github.com:rwightman/pytorch-image-models
"""
def __init__(self, drop_path: float, **kwargs):
super().__init__(**kwargs)
self... | class_definition | 1,492 | 2,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,626 |
class TFConvNextEmbeddings(keras.layers.Layer):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config: ConvNextConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = ... | class_definition | 2,212 | 4,328 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,627 |
class TFConvNextLayer(keras.layers.Layer):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linea... | class_definition | 4,331 | 8,391 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,628 |
class TFConvNextStage(keras.layers.Layer):
"""ConvNext stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config (`ConvNextV2Config`):
Model configuration class.
in_channels (`int`):
Number of input channels.
out_channels (`int`... | class_definition | 8,394 | 11,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,629 |
class TFConvNextEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.stages = []
drop_path_rates = tf.linspace(0.0, config.drop_path_rate, sum(config.depths))
drop_path_rates = tf.split(drop_path_rates, config.depths)
drop_path_ra... | class_definition | 11,594 | 13,328 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,630 |
class TFConvNextMainLayer(keras.layers.Layer):
config_class = ConvNextConfig
def __init__(self, config: ConvNextConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFConvNextEmbeddings(config, name="embeddings")
s... | class_definition | 13,351 | 16,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,631 |
class TFConvNextPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextConfig
base_model_prefix = "convnext"
main_input_name = "pixel_values" | class_definition | 16,470 | 16,773 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,632 |
class TFConvNextModel(TFConvNextPreTrainedModel):
def __init__(self, config, *inputs, add_pooling_layer=True, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.convnext = TFConvNextMainLayer(config, add_pooling_layer=add_pooling_layer, name="convnext")
@unpack_inputs
@add_start_do... | class_definition | 20,352 | 22,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,633 |
class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: ConvNextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.convnext = TFConvNextMainLayer(config, name="c... | class_definition | 23,111 | 27,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | null | 3,634 |
class ConvNextImageProcessor(BaseImageProcessor):
r"""
Constructs a ConvNeXT 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_resize` ... | class_definition | 1,438 | 15,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/image_processing_convnext.py | null | 3,635 |
class ConvNextFeatureExtractor(ConvNextImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ConvNextImageProcessor instead.",
FutureWa... | class_definition | 821 | 1,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/feature_extraction_convnext.py | null | 3,636 |
class ConvNextConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
ConvNeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the d... | class_definition | 1,036 | 5,725 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py | null | 3,637 |
class ConvNextOnnxConfig(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 | 5,728 | 6,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/configuration_convnext.py | null | 3,638 |
class ConvNextDropPath(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) -> t... | class_definition | 2,974 | 3,456 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,639 |
class ConvNextLayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
width, channels) while channels_first corresponds to inputs with shap... | class_definition | 3,459 | 4,935 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,640 |
class ConvNextEmbeddings(nn.Module):
"""This class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = nn.Conv2d(
config.num_channels, c... | class_definition | 4,938 | 5,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,641 |
class ConvNextLayer(nn.Module):
"""This corresponds to the `Block` class in the original implementation.
There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Li... | class_definition | 5,963 | 7,842 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,642 |
class ConvNextStage(nn.Module):
"""ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks.
Args:
config ([`ConvNextConfig`]): Model configuration class.
in_channels (`int`): Number of input channels.
out_channels (`int`): Number of output channels.
... | class_definition | 7,845 | 9,239 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,643 |
class ConvNextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.stages = nn.ModuleList()
drop_path_rates = [
x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)
]
prev_chs = config.hidden_size... | class_definition | 9,242 | 10,890 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,644 |
class ConvNextPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextConfig
base_model_prefix = "convnext"
main_input_name = "pixel_values"
_no_split_module... | class_definition | 10,893 | 11,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,645 |
class ConvNextModel(ConvNextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = ConvNextEmbeddings(config)
self.encoder = ConvNextEncoder(config)
# final layernorm layer
self.layernorm = nn.LayerNorm(config.h... | class_definition | 13,206 | 15,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,646 |
class ConvNextForImageClassification(ConvNextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.convnext = ConvNextModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1],... | class_definition | 15,496 | 18,753 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,647 |
class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.embeddings = ConvNextEmbeddings(config)
self.encoder = ConvNextEncoder(config)
self.num_features = [config.hidden_sizes[0]... | class_definition | 18,904 | 21,822 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py | null | 3,648 |
class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
... | class_definition | 1,008 | 8,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py | null | 3,649 |
class GPTNeoXConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an
GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 857 | 10,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py | null | 3,650 |
class GPTNeoXPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTNeoXConfig
base_model_prefix = "gpt_neox"
supports_gradient_checkpointing = True
_no_split_mo... | class_definition | 2,104 | 3,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,651 |
class GPTNeoXAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.config = config
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
if self.hidden_size % self.num_attention_heads != 0:
... | class_definition | 8,612 | 17,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,652 |
class GPTNeoXRotaryEmbedding(nn.Module):
def __init__(self, config: GPTNeoXConfig, 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 | 17,597 | 20,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,653 |
class GPTNeoXMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size)
self.act = ACT2FN[config.hidden_act]
def forward(... | class_definition | 22,600 | 23,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,654 |
class GPTNeoXLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.use_parallel_residual = config.use_parallel_residual
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hi... | class_definition | 23,137 | 25,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,655 |
class GPTNeoXModel(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
self.emb_dropout = nn.Dropout(config.hidden_dropout)
self.layers = nn.ModuleList([GPTNeoX... | class_definition | 30,613 | 44,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,656 |
class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["embed_out.weight"]
def __init__(self, config):
super().__init__(config)
self.gpt_neox = GPTNeoXModel(config)
self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
... | class_definition | 44,358 | 49,091 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,657 |
class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | class_definition | 49,891 | 55,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,658 |
class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.dropout = nn.Dropout(config.classifier_dropout)
self.classifier = nn.Linear(config.... | class_definition | 55,118 | 58,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,659 |
class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final pr... | class_definition | 58,547 | 62,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py | null | 3,660 |
class PoolFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of [`PoolFormerModel`]. It is used to instantiate a
PoolFormer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | class_definition | 929 | 5,168 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py | null | 3,661 |
class PoolFormerOnnxConfig(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 | 5,171 | 5,574 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/configuration_poolformer.py | null | 3,662 |
class PoolFormerDropPath(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) ->... | class_definition | 2,802 | 3,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,663 |
class PoolFormerEmbeddings(nn.Module):
"""
Construct Patch Embeddings.
"""
def __init__(self, hidden_size, num_channels, patch_size, stride, padding, norm_layer=None):
super().__init__()
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_... | class_definition | 3,289 | 4,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,664 |
class PoolFormerGroupNorm(nn.GroupNorm):
"""
Group Normalization with 1 group. Input: tensor in shape [B, C, H, W]
"""
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs) | class_definition | 4,165 | 4,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,665 |
class PoolFormerPooling(nn.Module):
def __init__(self, pool_size):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, hidden_states):
return self.pool(hidden_states) - hidden_states | class_definition | 4,399 | 4,694 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,666 |
class PoolFormerOutput(nn.Module):
def __init__(self, config, dropout_prob, hidden_size, intermediate_size):
super().__init__()
self.conv1 = nn.Conv2d(hidden_size, intermediate_size, 1)
self.conv2 = nn.Conv2d(intermediate_size, hidden_size, 1)
self.drop = PoolFormerDropPath(dropout_p... | class_definition | 4,697 | 5,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,667 |
class PoolFormerLayer(nn.Module):
"""This corresponds to the 'PoolFormerBlock' class in the original implementation."""
def __init__(self, config, num_channels, pool_size, hidden_size, intermediate_size, drop_path):
super().__init__()
self.pooling = PoolFormerPooling(pool_size)
self.out... | class_definition | 5,499 | 7,765 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,668 |
class PoolFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
# patch embeddings
embeddings = []
for... | class_definition | 7,768 | 10,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,669 |
class PoolFormerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = PoolFormerConfig
base_model_prefix = "poolformer"
main_input_name = "pixel_values"
_no_split_... | class_definition | 10,403 | 11,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,670 |
class PoolFormerModel(PoolFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = PoolFormerEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
... | class_definition | 12,268 | 13,999 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,671 |
class PoolFormerFinalPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
def forward(self, hidden_states):
output = self.dense(hidden_states)
return output | class_definition | 14,002 | 14,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,672 |
class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.poolformer = PoolFormerModel(config)
# Final norm
self.norm = PoolFormerGroupNorm(config.hidden_sizes[-1])
... | class_definition | 14,426 | 17,779 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/modeling_poolformer.py | null | 3,673 |
class PoolFormerImageProcessor(BaseImageProcessor):
r"""
Constructs a PoolFormer image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in t... | class_definition | 1,421 | 17,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/image_processing_poolformer.py | null | 3,674 |
class PoolFormerFeatureExtractor(PoolFormerImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PoolFormerImageProcessor instead.",
... | class_definition | 827 | 1,213 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/poolformer/feature_extraction_poolformer.py | null | 3,675 |
class MimiConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`MimiModel`]. It is used to instantiate a
Mimi model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar conf... | class_definition | 855 | 11,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/configuration_mimi.py | null | 3,676 |
class MimiOutput(ModelOutput):
"""
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*)... | class_definition | 1,643 | 3,631 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,677 |
class MimiEncoderOutput(ModelOutput):
"""
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, num_quantizers, codes_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
encoder_past_key_values (`Cache`, *optional*):
Pre-computed hidden-s... | class_definition | 3,645 | 4,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,678 |
class MimiDecoderOutput(ModelOutput):
"""
Args:
audio_values (`torch.FloatTensor` of shape `(batch_size, segment_length)`, *optional*):
Decoded audio values, obtained using the decoder part of Mimi.
decoder_past_key_values (`Cache`, *optional*):
Pre-computed hidden-state... | class_definition | 4,690 | 5,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,679 |
class MimiConv1d(nn.Module):
"""Conv1d with asymmetric or causal padding and normalization."""
def __init__(
self,
config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
groups: int = 1,
pad_mode... | class_definition | 5,722 | 9,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,680 |
class MimiConvTranspose1d(nn.Module):
"""ConvTranspose1d with asymmetric or causal padding and normalization."""
def __init__(
self,
config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias=True,
):... | class_definition | 9,858 | 12,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,681 |
class MimiResnetBlock(nn.Module):
"""
Residual block from SEANet model as used by Mimi.
"""
def __init__(self, config: MimiConfig, dim: int, dilations: List[int]):
super().__init__()
kernel_sizes = (config.residual_kernel_size, 1)
if len(kernel_sizes) != len(dilations):
... | class_definition | 12,133 | 13,331 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,682 |
class MimiEncoder(nn.Module):
"""SEANet encoder as used by Mimi."""
def __init__(self, config: MimiConfig):
super().__init__()
model = [MimiConv1d(config, config.audio_channels, config.num_filters, config.kernel_size)]
scaling = 1
# Downsample to raw audio scale
for rat... | class_definition | 13,334 | 14,576 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,683 |
class MimiLayerScale(nn.Module):
"""Layer scale from [Touvron et al 2021] (https://arxiv.org/pdf/2103.17239.pdf).
This rescales diagonally the residual outputs close to 0, with a learnt scale.
"""
def __init__(self, config):
super().__init__()
channels = config.hidden_size
initi... | class_definition | 14,579 | 15,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,684 |
class MimiRotaryEmbedding(nn.Module):
def __init__(self, config: MimiConfig, 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", conf... | class_definition | 15,211 | 18,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,685 |
class MimiMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.fc2 = nn.Linear(config.intermediate_size, config.hi... | class_definition | 20,275 | 20,942 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,686 |
class MimiAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: MimiConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
logge... | class_definition | 21,751 | 26,537 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,687 |
class MimiFlashAttention2(MimiAttention):
"""
Mimi flash attention module. This module inherits from `MimiAttention` 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 paddin... | class_definition | 26,671 | 32,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,688 |
class MimiSdpaAttention(MimiAttention):
"""
Mimi attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`MimiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from Mim... | class_definition | 32,389 | 36,809 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,689 |
class MimiTransformerLayer(nn.Module):
def __init__(self, config: MimiConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MIMI_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = MimiMLP(config... | class_definition | 36,948 | 40,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,690 |
class MimiTransformerModel(nn.Module):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MimiTransformerLayer`]
Args:
config: MimiConfig
"""
def __init__(self, config: MimiConfig):
super().__init__()
self.layers = nn.ModuleList(
... | class_definition | 40,402 | 57,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,691 |
class MimiDecoder(nn.Module):
"""SEANet decoder as used by Mimi."""
def __init__(self, config: MimiConfig):
super().__init__()
scaling = int(2 ** len(config.upsampling_ratios))
model = [MimiConv1d(config, config.hidden_size, scaling * config.num_filters, config.kernel_size)]
# ... | class_definition | 57,142 | 58,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,692 |
class MimiEuclideanCodebook(nn.Module):
"""Codebook with Euclidean distance."""
def __init__(self, config: MimiConfig, epsilon: float = 1e-5):
super().__init__()
embed = torch.zeros(config.codebook_size, config.codebook_dim)
self.codebook_size = config.codebook_size
self.regis... | class_definition | 58,483 | 60,264 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,693 |
class MimiVectorQuantization(nn.Module):
"""
Vector quantization implementation. Currently supports only euclidean distance.
"""
def __init__(self, config: MimiConfig):
super().__init__()
self.codebook = MimiEuclideanCodebook(config)
def encode(self, hidden_states):
hidden_... | class_definition | 60,371 | 60,963 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,694 |
class MimiResidualVectorQuantizer(nn.Module):
"""Residual Vector Quantizer."""
def __init__(self, config: MimiConfig, num_quantizers: int = None):
super().__init__()
self.codebook_size = config.codebook_size
self.frame_rate = config.frame_rate
self.num_quantizers = num_quantizer... | class_definition | 60,966 | 63,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,695 |
class MimiSplitResidualVectorQuantizer(nn.Module):
"""Split Residual Vector Quantizer."""
def __init__(self, config: MimiConfig):
super().__init__()
self.codebook_size = config.codebook_size
self.frame_rate = config.frame_rate
self.max_num_quantizers = config.num_quantizers
... | class_definition | 63,372 | 66,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,696 |
class MimiPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MimiConfig
base_model_prefix = "mimi"
main_input_name = "input_values"
supports_gradient_checkpoint... | class_definition | 66,188 | 68,066 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,697 |
class MimiModel(MimiPreTrainedModel):
def __init__(self, config: MimiConfig):
super().__init__(config)
self.config = config
self.encoder = MimiEncoder(config)
self.encoder_transformer = MimiTransformerModel(config)
self.downsample = None
self.upsample = None
... | class_definition | 71,249 | 83,223 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mimi/modeling_mimi.py | null | 3,698 |
class WavLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`WavLMModel`]. It is used to instantiate an WavLM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 861 | 18,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wavlm/configuration_wavlm.py | null | 3,699 |
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