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 ClapTextModel(ClapPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by Ashish Vaswani, ... | class_definition | 80,676 | 89,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,400 |
class ClapModel(ClapPreTrainedModel):
config_class = ClapConfig
def __init__(self, config: ClapConfig):
super().__init__(config)
if not isinstance(config.text_config, ClapTextConfig):
raise TypeError(
"config.text_config is expected to be of type ClapTextConfig but ... | class_definition | 89,319 | 98,955 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,401 |
class ClapTextModelWithProjection(ClapPreTrainedModel):
config_class = ClapTextConfig
def __init__(self, config: ClapTextConfig):
super().__init__(config)
self.text_model = ClapTextModel(config)
self.text_projection = ClapProjectionLayer(config)
# Initialize weights and apply fi... | class_definition | 99,123 | 101,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,402 |
class ClapAudioModelWithProjection(ClapPreTrainedModel):
config_class = ClapAudioConfig
main_input_name = "input_features"
def __init__(self, config: ClapAudioConfig):
super().__init__(config)
self.audio_model = ClapAudioModel(config)
self.audio_projection = ClapProjectionLayer(conf... | class_definition | 101,917 | 104,850 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,403 |
class ClapProcessor(ProcessorMixin):
r"""
Constructs a CLAP processor which wraps a CLAP feature extractor and a RoBerta tokenizer into a single processor.
[`ClapProcessor`] offers all the functionalities of [`ClapFeatureExtractor`] and [`RobertaTokenizerFast`]. See the
[`~ClapProcessor.__call__`] and ... | class_definition | 752 | 5,677 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/processing_clap.py | null | 7,404 |
class ClapTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simil... | class_definition | 781 | 6,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/configuration_clap.py | null | 7,405 |
class ClapAudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yie... | class_definition | 6,797 | 14,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/configuration_clap.py | null | 7,406 |
class ClapConfig(PretrainedConfig):
r"""
[`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate
a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a
configuration with the defaults ... | class_definition | 14,391 | 18,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/configuration_clap.py | null | 7,407 |
class MobileViTConvLayer(nn.Module):
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias: bool = False,
dilation: int = 1,
use_normalization: bool = ... | class_definition | 2,524 | 4,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,408 |
class MobileViTInvertedResidual(nn.Module):
"""
Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381
"""
def __init__(
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1
) -> None:
super().__init__()
expanded... | class_definition | 4,615 | 6,130 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,409 |
class MobileViTMobileNetLayer(nn.Module):
def __init__(
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int = 1, num_stages: int = 1
) -> None:
super().__init__()
self.layer = nn.ModuleList()
for i in range(num_stages):
layer = MobileViTIn... | class_definition | 6,133 | 6,888 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,410 |
class MobileViTSelfAttention(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
super().__init__()
if hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {hidden_size,} is not a multiple of the number of atte... | class_definition | 6,891 | 9,337 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,411 |
class MobileViTSelfOutput(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
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.... | class_definition | 9,340 | 9,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,412 |
class MobileViTAttention(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int) -> None:
super().__init__()
self.attention = MobileViTSelfAttention(config, hidden_size)
self.output = MobileViTSelfOutput(config, hidden_size)
self.pruned_heads = set()
def prune_... | class_definition | 9,801 | 11,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,413 |
class MobileViTIntermediate(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
super().__init__()
self.dense = nn.Linear(hidden_size, intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2F... | class_definition | 11,252 | 11,875 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,414 |
class MobileViTOutput(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
super().__init__()
self.dense = nn.Linear(intermediate_size, hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_state... | class_definition | 11,878 | 12,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,415 |
class MobileViTTransformerLayer(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int) -> None:
super().__init__()
self.attention = MobileViTAttention(config, hidden_size)
self.intermediate = MobileViTIntermediate(config, hidden_size, intermediate_s... | class_definition | 12,446 | 13,430 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,416 |
class MobileViTTransformer(nn.Module):
def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int) -> None:
super().__init__()
self.layer = nn.ModuleList()
for _ in range(num_stages):
transformer_layer = MobileViTTransformerLayer(
config,
... | class_definition | 13,433 | 14,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,417 |
class MobileViTLayer(nn.Module):
"""
MobileViT block: https://arxiv.org/abs/2110.02178
"""
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
stride: int,
hidden_size: int,
num_stages: int,
dilation: int = 1,... | class_definition | 14,119 | 20,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,418 |
class MobileViTEncoder(nn.Module):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList()
self.gradient_checkpointing = False
# segmentation architectures like DeepLab and PSPNet modify the strides
#... | class_definition | 20,310 | 23,487 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,419 |
class MobileViTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileViTConfig
base_model_prefix = "mobilevit"
main_input_name = "pixel_values"
supports_grad... | class_definition | 23,490 | 24,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,420 |
class MobileViTModel(MobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig, expand_output: bool = True):
super().__init__(config)
self.config = config
self.expand_output = expand_output
self.conv_stem = MobileViTConvLayer(
config,
in_channels=... | class_definition | 25,911 | 29,232 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,421 |
class MobileViTForImageClassification(MobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilevit = MobileViTModel(config)
# Classifier head
self.dropout = nn.Dropout(config.clas... | class_definition | 29,440 | 32,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,422 |
class MobileViTASPPPooling(nn.Module):
def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int) -> None:
super().__init__()
self.global_pool = nn.AdaptiveAvgPool2d(output_size=1)
self.conv_1x1 = MobileViTConvLayer(
config,
in_channels=in_chan... | class_definition | 32,832 | 33,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,423 |
class MobileViTASPP(nn.Module):
"""
ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587
"""
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
in_channels = config.neck_hidden_sizes[-2]
out_channels = co... | class_definition | 33,661 | 35,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,424 |
class MobileViTDeepLabV3(nn.Module):
"""
DeepLabv3 architecture: https://arxiv.org/abs/1706.05587
"""
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
self.aspp = MobileViTASPP(config)
self.dropout = nn.Dropout2d(config.classifier_dropout_prob)
s... | class_definition | 35,496 | 36,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,425 |
class MobileViTForSemanticSegmentation(MobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilevit = MobileViTModel(config, expand_output=False)
self.segmentation_head = MobileViTDeepLabV... | class_definition | 36,484 | 40,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | null | 7,426 |
class MobileViTImageProcessor(BaseImageProcessor):
r"""
Constructs a MobileViT 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 the
`do_resize` pa... | class_definition | 1,486 | 21,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/image_processing_mobilevit.py | null | 7,427 |
class TFMobileViTConvLayer(keras.layers.Layer):
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias: bool = False,
dilation: int = 1,
use_normalizati... | class_definition | 2,531 | 5,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,428 |
class TFMobileViTInvertedResidual(keras.layers.Layer):
"""
Inverted residual block (MobileNetv2): https://arxiv.org/abs/1801.04381
"""
def __init__(
self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int = 1, **kwargs
) -> None:
super().__init... | class_definition | 5,407 | 7,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,429 |
class TFMobileViTMobileNetLayer(keras.layers.Layer):
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
stride: int = 1,
num_stages: int = 1,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.layers = []
... | class_definition | 7,681 | 8,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,430 |
class TFMobileViTSelfAttention(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
super().__init__(**kwargs)
if hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {hidden_size,} is not a m... | class_definition | 8,885 | 12,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,431 |
class TFMobileViTSelfOutput(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(hidden_size, name="dense")
self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
self... | class_definition | 12,125 | 12,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,432 |
class TFMobileViTAttention(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
super().__init__(**kwargs)
self.attention = TFMobileViTSelfAttention(config, hidden_size, name="attention")
self.dense_output = TFMobileViTSelfOutput(config, hidde... | class_definition | 12,983 | 14,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,433 |
class TFMobileViTIntermediate(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(intermediate_size, name="dense")
if isinstance(config.hidden_act, str):
... | class_definition | 14,086 | 15,064 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,434 |
class TFMobileViTOutput(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
super().__init__(**kwargs)
self.dense = keras.layers.Dense(hidden_size, name="dense")
self.dropout = keras.layers.Dropout(config.hidden_dropou... | class_definition | 15,067 | 16,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,435 |
class TFMobileViTTransformerLayer(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
super().__init__(**kwargs)
self.attention = TFMobileViTAttention(config, hidden_size, name="attention")
self.intermediate = TFMobile... | class_definition | 16,041 | 18,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,436 |
class TFMobileViTTransformer(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int, **kwargs) -> None:
super().__init__(**kwargs)
self.layers = []
for i in range(num_stages):
transformer_layer = TFMobileViTTransformerLayer(
... | class_definition | 18,292 | 19,372 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,437 |
class TFMobileViTLayer(keras.layers.Layer):
"""
MobileViT block: https://arxiv.org/abs/2110.02178
"""
def __init__(
self,
config: MobileViTConfig,
in_channels: int,
out_channels: int,
stride: int,
hidden_size: int,
num_stages: int,
dilatio... | class_definition | 19,375 | 27,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,438 |
class TFMobileViTEncoder(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.layers = []
# segmentation architectures like DeepLab and PSPNet modify the strides
# of the classification back... | class_definition | 27,150 | 30,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,439 |
class TFMobileViTMainLayer(keras.layers.Layer):
config_class = MobileViTConfig
def __init__(self, config: MobileViTConfig, expand_output: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.expand_output = expand_output
self.conv_stem = TFMobileViTConvL... | class_definition | 30,558 | 35,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,440 |
class TFMobileViTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileViTConfig
base_model_prefix = "mobilevit"
main_input_name = "pixel_values" | class_definition | 35,437 | 35,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,441 |
class TFMobileViTModel(TFMobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig, expand_output: bool = True, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.expand_output = expand_output
self.mobilevit = TFMobileViTMainLayer(c... | class_definition | 39,333 | 40,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,442 |
class TFMobileViTForImageClassification(TFMobileViTPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: MobileViTConfig, *inputs, **kwargs) -> None:
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mobilevit = TFMobileViTMainLayer(co... | class_definition | 40,898 | 43,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,443 |
class TFMobileViTASPPPooling(keras.layers.Layer):
def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int, **kwargs) -> None:
super().__init__(**kwargs)
self.global_pool = keras.layers.GlobalAveragePooling2D(keepdims=True, name="global_pool")
self.conv_1x1 = TFMobil... | class_definition | 43,856 | 45,226 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,444 |
class TFMobileViTASPP(keras.layers.Layer):
"""
ASPP module defined in DeepLab papers: https://arxiv.org/abs/1606.00915, https://arxiv.org/abs/1706.05587
"""
def __init__(self, config: MobileViTConfig, **kwargs) -> None:
super().__init__(**kwargs)
in_channels = config.neck_hidden_sizes[... | class_definition | 45,229 | 48,080 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,445 |
class TFMobileViTDeepLabV3(keras.layers.Layer):
"""
DeepLabv3 architecture: https://arxiv.org/abs/1706.05587
"""
def __init__(self, config: MobileViTConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.aspp = TFMobileViTASPP(config, name="aspp")
self.dropout = keras.layer... | class_definition | 48,083 | 49,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,446 |
class TFMobileViTForSemanticSegmentation(TFMobileViTPreTrainedModel):
def __init__(self, config: MobileViTConfig, **kwargs) -> None:
super().__init__(config, **kwargs)
self.num_labels = config.num_labels
self.mobilevit = TFMobileViTMainLayer(config, expand_output=False, name="mobilevit")
... | class_definition | 49,630 | 54,675 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | null | 7,447 |
class MobileViTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MobileViTModel`]. It is used to instantiate a
MobileViT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield ... | class_definition | 912 | 6,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/configuration_mobilevit.py | null | 7,448 |
class MobileViTOnnxConfig(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"})])
@property
def outputs(self) -> Mapping[s... | class_definition | 6,885 | 7,531 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/configuration_mobilevit.py | null | 7,449 |
class MobileViTFeatureExtractor(MobileViTImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use MobileViTImageProcessor instead.",
Futu... | class_definition | 824 | 1,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilevit/feature_extraction_mobilevit.py | null | 7,450 |
class IdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_p... | class_definition | 1,155 | 2,942 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,451 |
class IdeficsVisionEmbeddings(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Pa... | class_definition | 3,020 | 7,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,452 |
class IdeficsVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim ... | class_definition | 8,071 | 12,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,453 |
class IdeficsVisionMLP(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)
self.fc2 = nn.Linear(config.intermediate_size, config.hidde... | class_definition | 12,899 | 13,478 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,454 |
class IdeficsVisionEncoderLayer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IdeficsVisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.... | class_definition | 13,588 | 15,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,455 |
class IdeficsVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`IdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, config: IdeficsVisionConfig):
super().__init__(... | class_definition | 15,674 | 20,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,456 |
class IdeficsVisionTransformer(nn.Module):
def __init__(self, config: IdeficsVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = IdeficsVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_n... | class_definition | 20,186 | 22,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision.py | null | 7,457 |
class IdeficsBaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidde... | class_definition | 1,916 | 4,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,458 |
class IdeficsCausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics 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).
l... | class_definition | 4,830 | 7,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,459 |
class IdeficsDecoupledEmbedding(nn.Embedding):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
regular `weight` can be trained or frozen (i.e. ... | class_definition | 9,882 | 15,010 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,460 |
class IdeficsDecoupledLinear(nn.Linear):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partiall... | class_definition | 15,013 | 17,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,461 |
class IdeficsRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
IdeficsRMSNorm 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 | 17,657 | 18,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,462 |
class IdeficsEmbedding(torch.nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0, ... | class_definition | 18,561 | 20,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,463 |
class IdeficsMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Linear(intermediate_size, hidden_si... | class_definition | 22,107 | 22,667 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,464 |
class IdeficsAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
hidden_size: int,
num_heads: int,
dropout: float = 0.0,
is_cross_attention: bool = False,
config: PretrainedConfig = None,
qk_layer_... | class_definition | 22,709 | 30,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,465 |
class IdeficsDecoderLayer(nn.Module):
def __init__(self, config: IdeficsConfig, layer_idx: int = None):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = IdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
... | class_definition | 30,368 | 33,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,466 |
class IdeficsGatedCrossAttentionLayer(nn.Module):
def __init__(self, config: IdeficsConfig, layer_idx: int = None):
super().__init__()
self.hidden_size = config.hidden_size
self.cross_attn = IdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attentio... | class_definition | 33,743 | 40,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,467 |
class IdeficsPreTrainedModel(PreTrainedModel):
config_class = IdeficsConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
_supports_sdpa = True
_supports_cache_class = True
_supports_static_ca... | class_definition | 41,917 | 43,013 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,468 |
class IdeficsModel(IdeficsPreTrainedModel):
"""
Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
Args:
config: IdeficsConfig
"""
def __init__(self, config: IdeficsConfig):
super().__init__(config)
self.config = con... | class_definition | 47,474 | 69,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,469 |
class IdeficsForVisionText2Text(IdeficsPreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config, vision_model=None):
super().__init__(config)
self.model = IdeficsMo... | class_definition | 69,217 | 81,324 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_idefics.py | null | 7,470 |
class IdeficsPerceiverResampler(nn.Module):
def __init__(
self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a ResNet or ViT or
... | class_definition | 2,215 | 5,126 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/perceiver.py | null | 7,471 |
class IdeficsPerceiverAttention(nn.Module):
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.embed_dim, self.n... | class_definition | 5,129 | 8,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/perceiver.py | null | 7,472 |
class IdeficsMLP(nn.Module):
def __init__(self, intermediate_size, config: IdeficsConfig):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__()
self.embed_dim = config.vision_config.embed_dim
self.ln = nn.LayerNorm(self.embed_dim)
self.fc = nn.L... | class_definition | 8,633 | 9,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/perceiver.py | null | 7,473 |
class IdeficsImagesKwargs(ImagesKwargs, total=False):
transform: Optional[Callable]
image_size: Optional[Dict[str, int]]
image_mean: Optional[Union[float, List[float]]]
image_std: Optional[Union[float, List[float]]] | class_definition | 1,285 | 1,516 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/processing_idefics.py | null | 7,474 |
class IdeficsTextKwargs(TextKwargs, total=False):
add_eos_token: Optional[bool]
add_end_of_utterance_token: Optional[bool] | class_definition | 1,519 | 1,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/processing_idefics.py | null | 7,475 |
class IdeficsProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: IdeficsTextKwargs
images_kwargs: IdeficsImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": False,
"padding": "longest",
"add_eos_token": False,
},
"images_kwar... | class_definition | 1,652 | 2,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/processing_idefics.py | null | 7,476 |
class IdeficsProcessor(ProcessorMixin):
r"""
Constructs a IDEFICS processor which wraps a LLama tokenizer and IDEFICS image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`IdeficsImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~IdeficsProc... | class_definition | 7,230 | 23,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/processing_idefics.py | null | 7,477 |
class TFIdeficsVisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`tf.Tensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_project... | class_definition | 1,226 | 2,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,478 |
class TFIdeficsVisionEmbeddings(tf.keras.layers.Layer):
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
... | class_definition | 2,928 | 8,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,479 |
class TFIdeficsVisionAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attent... | class_definition | 8,372 | 14,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,480 |
class TFIdeficsVisionMLP(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.activation_fn = get_tf_activation(config.hidden_act)
self.fc1 = tf.keras.layers.Dense(config.intermediate_size, name="fc1")
self.fc2 = t... | class_definition | 14,141 | 15,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,481 |
class TFIdeficsVisionEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFIdeficsVisionAttention(config, name="self_attn")
self.layer_norm1 = tf.keras.layers.L... | class_definition | 15,181 | 17,726 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,482 |
class TFIdeficsVisionEncoder(tf.keras.layers.Layer):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`TFIdeficsVisionEncoderLayer`].
Args:
config: IdeficsVisionConfig
"""
def __init__(self, config: IdeficsVisionConfig, **kwargs):... | class_definition | 17,729 | 22,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,483 |
class TFIdeficsVisionTransformer(TFPreTrainedModel):
def __init__(self, config: IdeficsVisionConfig, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.embed_dim = config.hidden_size
self.embeddings = TFIdeficsVisionEmbeddings(config, name="embeddings")
... | class_definition | 22,701 | 26,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/vision_tf.py | null | 7,484 |
class IdeficsImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
image_size (`int`, *optional*, defaults to 224):
Resize to image size
image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`):
Mean to use... | class_definition | 1,728 | 7,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/image_processing_idefics.py | null | 7,485 |
class TFIdeficsPerceiverResampler(tf.keras.layers.Layer):
def __init__(
self, config: IdeficsConfig, embed_dim: int, depth: int, n_heads: int, head_dim: int, n_latents: int, **kwargs
) -> None:
"""
Instantiates a Perceiver Resampler that operates over a sequence of embeddings (say from a... | class_definition | 2,248 | 5,422 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/perceiver_tf.py | null | 7,486 |
class TFIdeficsPerceiverAttention(tf.keras.layers.Layer):
def __init__(self, embed_dim: int, n_heads: int, head_dim: int, qk_layer_norms: bool, **kwargs) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__(**kwargs... | class_definition | 5,425 | 9,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/perceiver_tf.py | null | 7,487 |
class TFIdeficsMLP(tf.keras.layers.Layer):
def __init__(self, intermediate_size, config: IdeficsConfig, **kwargs):
"""Simple MLP block with intermediate_size and embedding size"""
super().__init__(**kwargs)
self.embed_dim = config.vision_config.embed_dim
self.ln = tf.keras.layers.Lay... | class_definition | 9,116 | 10,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/perceiver_tf.py | null | 7,488 |
class TFIdeficsBaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-stat... | class_definition | 1,840 | 4,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,489 |
class TFIdeficsCausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics causal language model (or autoregressive) outputs.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits ... | class_definition | 4,644 | 7,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,490 |
class TFIdeficsDecoupledEmbedding(tf.keras.layers.Embedding):
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0,
then it will c... | class_definition | 12,477 | 17,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,491 |
class TFIdeficsDecoupledLinear(tf.keras.layers.Layer):
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `out_additional_features` > 0,
then it will create `ou... | class_definition | 17,384 | 20,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,492 |
class TFIdeficsRMSNorm(tf.keras.layers.Layer):
def __init__(self, hidden_size, eps=1e-6, **kwargs):
"""
TFIdeficsRMSNorm is equivalent to T5LayerNorm
"""
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.variance_epsilon = eps
def build(self, input_s... | class_definition | 22,651 | 23,644 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,493 |
class TFIdeficsEmbedding(tf.keras.layers.Layer):
def __init__(self, dim, max_position_embeddings=2048, base=10000, **kwargs):
super().__init__(**kwargs)
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.inv_freq = tf.constant(
... | class_definition | 23,647 | 24,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,494 |
class TFIdeficsMLP(tf.keras.layers.Layer):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
**kwargs,
):
super().__init__(**kwargs)
self.gate_proj = tf.keras.layers.Dense(intermediate_size, use_bias=False, name="gate_proj")
... | class_definition | 25,141 | 26,497 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,495 |
class TFIdeficsAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
hidden_size: int,
num_heads: int,
dropout: float = 0.0,
is_cross_attention: bool = False,
config: IdeficsConfig = None,
... | class_definition | 26,500 | 33,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,496 |
class TFIdeficsDecoderLayer(tf.keras.layers.Layer):
def __init__(self, config: IdeficsConfig, **kwargs):
super().__init__(**kwargs)
self.hidden_size = config.hidden_size
self.self_attn = TFIdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_... | class_definition | 33,412 | 37,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,497 |
class TFIdeficsGatedCrossAttentionLayer(tf.keras.layers.Layer):
def __init__(self, config: IdeficsConfig, **kwargs):
super().__init__(**kwargs)
self.hidden_size = config.hidden_size
self.cross_attn = TFIdeficsAttention(
hidden_size=self.hidden_size,
num_heads=config.n... | class_definition | 37,466 | 46,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,498 |
class TFIdeficsPreTrainedModel(TFPreTrainedModel):
config_class = IdeficsConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["TFIdeficsDecoderLayer", "TFIdeficsGatedCrossAttentionLayer"] | class_definition | 47,592 | 47,837 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,499 |
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