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 TweetTokenizer:
r"""
Examples:
```python
>>> # Tokenizer for tweets.
>>> from nltk.tokenize import TweetTokenizer
>>> tknzr = TweetTokenizer()
>>> s0 = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--"
>>> tknzr.tokenize(s0)
['This', 'is', 'a', 'cooool', ... | class_definition | 23,801 | 25,648 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bertweet/tokenization_bertweet.py | null | 4,900 |
class TFHubertGroupNorm(keras.layers.Layer):
"""
From tensorflow-addons https://www.tensorflow.org/addons/api_docs/python/tfa/layers/GroupNormalization
"""
def __init__(
self,
groups: int = 32,
axis: int = -1,
epsilon: float = 1e-3,
center: bool = True,
s... | class_definition | 6,550 | 14,627 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,901 |
class TFHubertWeightNormConv1D(keras.layers.Conv1D):
"""Adapted from https://www.tensorflow.org/probability/api_docs/python/tfp/layers/weight_norm/WeightNorm"""
def __init__(self, filters, kernel_size, groups, explicit_padding, **kwargs):
super().__init__(
filters=filters,
kerne... | class_definition | 14,743 | 17,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,902 |
class TFHubertNoLayerNormConvLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
se... | class_definition | 17,207 | 18,315 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,903 |
class TFHubertLayerNormConvLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self... | class_definition | 18,433 | 19,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,904 |
class TFHubertGroupNormConvLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, layer_id: int = 0, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.in_conv_dim = config.conv_dim[layer_id] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self... | class_definition | 20,003 | 21,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,905 |
class TFHubertPositionalConvEmbedding(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.conv = TFHubertWeightNormConv1D(
filters=config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
gro... | class_definition | 21,590 | 22,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,906 |
class TFHubertSamePadLayer(keras.layers.Layer):
def __init__(self, num_conv_pos_embeddings, **kwargs):
super().__init__(**kwargs)
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def call(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hid... | class_definition | 22,869 | 23,258 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,907 |
class TFHubertFeatureEncoder(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs: Any) -> None:
super().__init__(**kwargs)
if config.feat_extract_norm == "group":
conv_layers = [TFHubertGroupNormConvLayer(config, layer_id=0, name=f"conv_layers.{0}")] + [
... | class_definition | 23,261 | 24,660 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,908 |
class TFHubertFeatureExtractor(TFHubertFeatureEncoder):
def __init__(self, config, **kwargs):
super().__init__(config, **kwargs)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self._... | class_definition | 24,663 | 25,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,909 |
class TFHubertFeatureProjection(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.layer_norm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
self.projection = keras.layers.Dense(
units=con... | class_definition | 25,066 | 26,441 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,910 |
class TFHubertAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super... | class_definition | 26,538 | 34,114 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,911 |
class TFHubertFeedForward(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.intermediate_dropout = keras.layers.Dropout(config.activation_dropout)
self.intermediate_dense = keras.layers.Dense(
units=config.intermediate_size... | class_definition | 34,225 | 36,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,912 |
class TFHubertEncoderLayer(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFHubertAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,... | class_definition | 36,218 | 38,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,913 |
class TFHubertEncoderLayerStableLayerNorm(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFHubertAttention(
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.att... | class_definition | 38,803 | 41,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,914 |
class TFHubertEncoder(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.pos_conv_embed = TFHubertPositionalConvEmbedding(config, name="pos_conv_embed")
self.layer_norm = keras.layers.LayerNormalization(epsilo... | class_definition | 41,344 | 44,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,915 |
class TFHubertEncoderStableLayerNorm(keras.layers.Layer):
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.pos_conv_embed = TFHubertPositionalConvEmbedding(config, name="pos_conv_embed")
self.layer_norm = keras.layers.LayerNorma... | class_definition | 44,879 | 48,320 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,916 |
class TFHubertMainLayer(keras.layers.Layer):
config_class = HubertConfig
def __init__(self, config: HubertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.feature_extractor = TFHubertFeatureEncoder(config, name="feature_extractor")
self.feature_projection ... | class_definition | 48,343 | 54,152 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,917 |
class TFHubertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HubertConfig
base_model_prefix = "hubert"
main_input_name = "input_values"
@property
def... | class_definition | 54,155 | 55,125 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,918 |
class TFHubertModel(TFHubertPreTrainedModel):
def __init__(self, config: HubertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.hubert = TFHubertMainLayer(config, name="hubert")
@add_start_docstrings_to_model_forward(HUBERT_INPUTS_DOCSTRI... | class_definition | 61,133 | 63,997 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,919 |
class TFHubertForCTC(TFHubertPreTrainedModel):
def __init__(self, config: HubertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.hubert = TFHubertMainLayer(config, name="hubert")
self.dropout = keras.layers.Dropout(config.final_dropout)
self.lm_head = ker... | class_definition | 64,167 | 70,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_tf_hubert.py | null | 4,920 |
class HubertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HubertModel`]. It is used to instantiate an
Hubert model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 841 | 14,908 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/configuration_hubert.py | null | 4,921 |
class HubertNoLayerNormConvLayer(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 | 7,434 | 8,161 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,922 |
class HubertLayerNormConvLayer(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 | 8,274 | 9,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,923 |
class HubertGroupNormConvLayer(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 | 9,364 | 10,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,924 |
class HubertPositionalConvEmbedding(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 | 10,262 | 12,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,925 |
class HubertSamePadLayer(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.n... | class_definition | 12,473 | 12,836 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,926 |
class HubertFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [HubertGroupNormConvLayer(config, layer_id=0)] + [
HubertNoLayerNormConvL... | class_definition | 12,945 | 14,641 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,927 |
class HubertFeatureExtractor(HubertFeatureEncoder):
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].__... | class_definition | 14,644 | 15,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,928 |
class HubertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.feat_proj_layer_norm = config.feat_proj_layer_norm
if self.feat_proj_layer_norm:
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = ... | class_definition | 15,023 | 15,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,929 |
class HubertAttention(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,
c... | class_definition | 15,879 | 23,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,930 |
class HubertFlashAttention2(HubertAttention):
"""
Hubert flash attention module. This module inherits from `HubertAttention` 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 wit... | class_definition | 23,367 | 29,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,931 |
class HubertSdpaAttention(HubertAttention):
# Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with Bart->Hubert
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tenso... | class_definition | 29,818 | 35,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,932 |
class HubertFeedForward(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):
s... | class_definition | 35,954 | 36,922 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,933 |
class HubertEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = HUBERT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
... | class_definition | 37,047 | 38,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,934 |
class HubertAttnAdapterLayer(nn.Module):
def __init__(self, config):
"""
Implements adapter modules directly with 3D tensor weight as parameters and without using ModuleList to speed
up training throughput.
"""
super().__init__()
self.input_dim = config.adapter_attn_d... | class_definition | 38,511 | 39,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,935 |
class HubertEncoderLayerStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = HUBERT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_... | class_definition | 39,530 | 41,250 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,936 |
class HubertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = HubertPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidde... | class_definition | 41,352 | 45,182 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,937 |
class HubertEncoderStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = HubertPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropo... | class_definition | 45,299 | 49,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,938 |
class HubertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HubertConfig
base_model_prefix = "hubert"
main_input_name = "input_values"
supports_gradient_chec... | class_definition | 49,301 | 52,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,939 |
class HubertModel(HubertPreTrainedModel):
def __init__(self, config: HubertConfig):
super().__init__(config)
self.config = config
self.feature_extractor = HubertFeatureEncoder(config)
self.feature_projection = HubertFeatureProjection(config)
if config.mask_time_prob > 0.0 or... | class_definition | 55,898 | 61,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,940 |
class HubertForCTC(HubertPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.hubert = HubertModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_size is None:... | class_definition | 62,107 | 68,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,941 |
class HubertForSequenceClassification(HubertPreTrainedModel):
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 Hubert adapters (config.add... | class_definition | 69,278 | 74,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hubert/modeling_hubert.py | null | 4,942 |
class GroupViTTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroupViTTextModel`]. It is used to instantiate an
GroupViT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will ... | class_definition | 1,017 | 5,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/configuration_groupvit.py | null | 4,943 |
class GroupViTVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GroupViTVisionModel`]. It is used to instantiate
an GroupViT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | class_definition | 5,462 | 10,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/configuration_groupvit.py | null | 4,944 |
class GroupViTConfig(PretrainedConfig):
r"""
[`GroupViTConfig`] is the configuration class to store the configuration of a [`GroupViTModel`]. It is used to
instantiate a GroupViT model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration w... | class_definition | 10,393 | 17,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/configuration_groupvit.py | null | 4,945 |
class GroupViTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
(... | class_definition | 17,588 | 19,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/configuration_groupvit.py | null | 4,946 |
class GroupViTCrossAttentionLayer(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.attn = GroupViTAttention(config)
self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp = GroupViTMLP(config)
self.norm_post = nn... | class_definition | 5,713 | 6,294 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,947 |
class GroupViTAssignAttention(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.scale = config.hidden_size**-0.5
self.q_proj = nn.Linear(config.hidden_size, config.hidden_size)
self.k_proj = nn.Linear(config.hidden_size, config.hidden_size)
... | class_definition | 6,297 | 7,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,948 |
class GroupViTTokenAssign(nn.Module):
def __init__(self, config: GroupViTVisionConfig, num_group_token, num_output_group):
super().__init__()
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)... | class_definition | 7,816 | 10,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,949 |
class GroupViTModelOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
... | class_definition | 10,494 | 13,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,950 |
class GroupViTPatchEmbeddings(nn.Module):
"""
Image to Patch Embedding.
"""
def __init__(
self,
image_size: int = 224,
patch_size: Union[int, Tuple[int, int]] = 16,
num_channels: int = 3,
embed_dim: int = 768,
):
super().__init__()
image_size ... | class_definition | 13,035 | 14,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,951 |
class GroupViTVisionEmbeddings(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.patch_embeddings = GroupViTPatchEmbeddings(
image_size=config.image_size,
patch_size=config.patch_size,
num_channels=config.num_channels,
... | class_definition | 14,454 | 17,603 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,952 |
class GroupViTTextEmbeddings(nn.Module):
def __init__(self, config: GroupViTTextConfig):
super().__init__()
embed_dim = config.hidden_size
self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_d... | class_definition | 17,698 | 19,284 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,953 |
class GroupViTStage(nn.Module):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
):
... | class_definition | 19,287 | 22,645 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,954 |
class GroupViTMLP(nn.Module):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
):
super().__init__()
self.config = config
self.activatio... | class_definition | 22,648 | 23,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,955 |
class GroupViTMixerMLP(GroupViTMLP):
def forward(self, x):
x = super().forward(x.transpose(1, 2))
return x.transpose(1, 2) | class_definition | 23,649 | 23,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,956 |
class GroupViTAttention(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 = sel... | class_definition | 23,794 | 28,908 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,957 |
class GroupViTEncoderLayer(nn.Module):
def __init__(self, config: GroupViTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = GroupViTAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = GroupViTM... | class_definition | 29,013 | 30,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,958 |
class GroupViTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GroupViTConfig
base_model_prefix = "groupvit"
supports_gradient_checkpointing = True
def _init... | class_definition | 30,977 | 33,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,959 |
class GroupViTVisionEncoder(nn.Module):
def __init__(self, config: GroupViTVisionConfig) -> None:
super().__init__()
self.config = config
self.stages = nn.ModuleList(
[
GroupViTStage(
config=config,
depth=config.depths[i],
... | class_definition | 38,393 | 40,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,960 |
class GroupViTTextEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self-attention layers. Each layer is a
[`GroupViTEncoderLayer`].
Args:
config: GroupViTTextConfig
"""
def __init__(self, config: GroupViTTextConfig):
super().__init__()
... | class_definition | 40,687 | 45,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,961 |
class GroupViTTextTransformer(nn.Module):
def __init__(self, config: GroupViTTextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = GroupViTTextEmbeddings(config)
self.encoder = GroupViTTextEncoder(config)
self.final_laye... | class_definition | 45,110 | 49,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,962 |
class GroupViTTextModel(GroupViTPreTrainedModel):
config_class = GroupViTTextConfig
def __init__(self, config: GroupViTTextConfig):
super().__init__(config)
self.text_model = GroupViTTextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
de... | class_definition | 49,745 | 51,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,963 |
class GroupViTVisionTransformer(nn.Module):
def __init__(self, config: GroupViTVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = GroupViTVisionEmbeddings(config)
self.encoder = GroupViTVisionEncoder(config)
self.l... | class_definition | 51,739 | 53,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,964 |
class GroupViTVisionModel(GroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: GroupViTVisionConfig):
super().__init__(config)
self.vision_model = GroupViTVisionTransformer(config)
# Initialize weights and apply fi... | class_definition | 53,946 | 55,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,965 |
class GroupViTModel(GroupViTPreTrainedModel):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig):
super().__init__(config)
if not isinstance(config.text_config, GroupViTTextConfig):
raise TypeError(
"config.text_config is expected to be of type... | class_definition | 55,938 | 68,188 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_groupvit.py | null | 4,966 |
class TFGroupViTModelOutput(ModelOutput):
"""
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
The scaled... | class_definition | 8,170 | 10,627 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,967 |
class TFGroupViTCrossAttentionLayer(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.attn = TFGroupViTAttention(config, name="attn")
self.norm2 = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm2")
... | class_definition | 10,630 | 12,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,968 |
class TFGroupViTAssignAttention(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.scale = config.hidden_size**-0.5
self.q_proj = keras.layers.Dense(config.hidden_size, name="q_proj")
self.k_proj = keras.layers.Dense(conf... | class_definition | 12,136 | 14,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,969 |
class TFGroupViTTokenAssign(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_output_group: int, **kwargs):
super().__init__(**kwargs)
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = keras.layers.LayerNo... | class_definition | 14,707 | 19,196 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,970 |
class TFGroupViTPatchEmbeddings(keras.layers.Layer):
"""
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__(se... | class_definition | 19,294 | 22,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,971 |
class TFGroupViTVisionEmbeddings(keras.layers.Layer):
"""
Construct the position and patch embeddings.
"""
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = TFGroupViTPatchEmbeddings(config, name="patch_embeddings")
... | class_definition | 23,003 | 26,246 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,972 |
class TFGroupViTTextEmbeddings(keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.config = config
def build(self, input_shape: tf.TensorShape = None):
with tf.name_scope("token_embe... | class_definition | 26,346 | 28,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,973 |
class TFGroupViTStage(keras.layers.Layer):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
... | class_definition | 28,493 | 33,174 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,974 |
class TFGroupViTMLP(keras.layers.Layer):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
**kwargs,
):
super().__init__(**kwargs)
self.c... | class_definition | 33,177 | 34,774 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,975 |
class TFGroupViTMixerMLP(TFGroupViTMLP):
def call(self, x, training: bool = False):
x = super().call(hidden_states=tf.transpose(x, perm=(0, 2, 1)))
return tf.transpose(x, perm=(0, 2, 1)) | class_definition | 34,777 | 34,983 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,976 |
class TFGroupViTAttention(keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
... | class_definition | 35,059 | 41,048 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,977 |
class TFGroupViTEncoderLayer(keras.layers.Layer):
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFGroupViTAttention(config, name="self_attn")
self.layer_norm1 = keras.layers.LayerNormalization(ep... | class_definition | 41,146 | 44,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,978 |
class TFGroupViTTextEncoder(keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states,
... | class_definition | 44,257 | 46,126 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,979 |
class TFGroupViTVisionEncoder(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.stages = [
TFGroupViTStage(
config=config,
depth=config.depths[i],
num_group_token=confi... | class_definition | 46,129 | 48,236 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,980 |
class TFGroupViTTextTransformer(keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTTextEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTTextEncoder(config, name="encoder")
self.final_lay... | class_definition | 48,379 | 53,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,981 |
class TFGroupViTVisionTransformer(keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTVisionEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTVisionEncoder(config, name="encoder")
self.l... | class_definition | 53,722 | 55,891 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,982 |
class TFGroupViTTextMainLayer(keras.layers.Layer):
config_class = GroupViTTextConfig
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.text_model = TFGroupViTTextTransformer(config, name="text_model")
def get_input_embedd... | class_definition | 56,010 | 57,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,983 |
class TFGroupViTVisionMainLayer(keras.layers.Layer):
config_class = GroupViTVisionConfig
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.vision_model = TFGroupViTVisionTransformer(config, name="vision_model")
def get_... | class_definition | 58,046 | 59,477 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,984 |
class TFGroupViTMainLayer(keras.layers.Layer):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
if not isinstance(config.text_config, GroupViTTextConfig):
raise TypeError(
"config.text_config is expected ... | class_definition | 59,573 | 70,700 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,985 |
class TFGroupViTPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GroupViTConfig
base_model_prefix = "groupvit" | class_definition | 70,703 | 70,969 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,986 |
class TFGroupViTTextModel(TFGroupViTPreTrainedModel):
config_class = GroupViTTextConfig
main_input_name = "input_ids"
def __init__(self, config: GroupViTTextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTTextMainLayer(config, name="groupvi... | class_definition | 79,331 | 81,557 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,987 |
class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: GroupViTVisionConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTVisionMainLayer(config, na... | class_definition | 81,560 | 83,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,988 |
class TFGroupViTModel(TFGroupViTPreTrainedModel):
config_class = GroupViTConfig
def __init__(self, config: GroupViTConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.groupvit = TFGroupViTMainLayer(config, name="groupvit")
@unpack_inputs
@add_start_docstrings_... | class_definition | 83,778 | 90,068 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py | null | 4,989 |
class GraniteMoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
GraniteMoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_state... | class_definition | 5,357 | 6,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,990 |
class GraniteMoeRotaryEmbedding(nn.Module):
def __init__(self, config: GraniteMoeConfig, 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... | class_definition | 6,246 | 9,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,991 |
class GraniteMoeParallelExperts(nn.Module):
def __init__(self, num_experts: int, input_size: int, output_size: int) -> None:
"""
Initialize the GraniteMoeParallelExperts module.
The experts weights are stored in [num_experts, output_size, input_size] format. Such that it's comptible with
... | class_definition | 11,483 | 13,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,992 |
class GraniteMoeTopKGating(nn.Module):
def __init__(self, input_size: int, num_experts: int, top_k: int):
"""
Initialize the top-k gating mechanism.
Args:
input_size (`int`):
Size of the input.
num_experts (`int`):
Number of experts.
... | class_definition | 13,250 | 15,254 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,993 |
class GraniteMoeMoE(nn.Module):
"""
A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
Args:
config:
Configuration object with model hyperparameters.
"""
def __init__(self, config: GraniteMoeConfig):
super(GraniteMoeMoE, self).__ini... | class_definition | 15,257 | 17,387 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,994 |
class GraniteMoeAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteMoeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | class_definition | 18,239 | 22,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,995 |
class GraniteMoeFlashAttention2(GraniteMoeAttention):
"""
GraniteMoe flash attention module. This module inherits from `GraniteMoeAttention` 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 attent... | class_definition | 23,077 | 28,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,996 |
class GraniteMoeSdpaAttention(GraniteMoeAttention):
"""
GraniteMoe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`GraniteMoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""... | class_definition | 28,636 | 33,279 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,997 |
class GraniteMoeDecoderLayer(nn.Module):
def __init__(self, config: GraniteMoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GRANITEMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.block_... | class_definition | 33,442 | 37,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,998 |
class GraniteMoePreTrainedModel(PreTrainedModel):
config_class = GraniteMoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GraniteMoeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa =... | class_definition | 38,799 | 39,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granitemoe/modeling_granitemoe.py | null | 4,999 |
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