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 TvpAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of att... | class_definition | 15,134 | 19,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,200 |
class TvpIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interme... | class_definition | 19,784 | 20,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,201 |
class TvpOutputLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 20,351 | 20,965 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,202 |
class TvpEncodeLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = TvpAttention(config)
self.intermediate = TvpIntermediate(config)
self.output = TvpOutputLayer(config)
def forward(
self,
hidden_states,
attention_mask=None,
... | class_definition | 20,968 | 21,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,203 |
class TvpEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([TvpEncodeLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 21,915 | 24,405 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,204 |
class TvpPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidden... | class_definition | 24,487 | 25,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,205 |
class TvpPreTrainedModel(PreTrainedModel):
"""An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = TvpConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, mod... | class_definition | 25,048 | 26,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,206 |
class TvpFrameDownPadPrompter(nn.Module):
"""
Pad frames extracted from videos only at the bottom.
"""
def __init__(self, config):
if config.visual_prompter_apply not in ("add", "replace", "remove"):
raise ValueError("`visual_prompter_apply` must be in (add, replace, remove)")
... | class_definition | 28,835 | 30,430 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,207 |
class TvpFramePadPrompter(nn.Module):
"""
Pad frames extracted from videos in the surroundings.
"""
def __init__(self, config):
if config.visual_prompter_apply not in ("add", "replace", "remove"):
raise ValueError("`visual_prompter_apply` must be in (add, replace, remove)")
... | class_definition | 30,433 | 34,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,208 |
class TvpModel(TvpPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.vision_model = TvpVisionModel(config)
self.embeddings = TvpTextInputEmbeddings(config)
self.visual_embeddings = TvpVisualInputEmbedding(config)
self.enco... | class_definition | 34,724 | 39,902 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,209 |
class TvpVideoGroundingHead(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_0 = nn.Linear(config.hidden_size, config.hidden_size * 2)
self.layer_1 = nn.Linear(config.hidden_size * 2, 2)
self.activation_0 = nn.ReLU()
self.activation_1 = nn.Sigmoid()
... | class_definition | 39,905 | 40,401 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,210 |
class TvpForVideoGrounding(TvpPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = TvpModel(config)
self.video_grounding_head = TvpVideoGroundingHead(config)
self.post_init()
@add_start_docstrings_to_model_forward(TVP_... | class_definition | 40,564 | 43,603 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,211 |
class TvpConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TvpModel`]. It is used to instantiate an Tvp
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | class_definition | 928 | 9,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/configuration_tvp.py | null | 8,212 |
class ASTFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Audio Spectrogram Transformer (AST) feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this supercla... | class_definition | 1,181 | 9,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py | null | 8,213 |
class ASTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ASTModel`]. It is used to instantiate an AST
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | class_definition | 855 | 5,880 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/configuration_audio_spectrogram_transformer.py | null | 8,214 |
class ASTEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings.
"""
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
self.distillation_token = nn.Parameter(torch.ze... | class_definition | 1,731 | 3,446 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,215 |
class ASTPatchEmbeddings(nn.Module):
"""
This class turns `input_values` into the initial `hidden_states` (patch embeddings) of shape `(batch_size,
seq_length, hidden_size)` to be consumed by a Transformer.
"""
def __init__(self, config):
super().__init__()
patch_size = config.patc... | class_definition | 3,449 | 4,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,216 |
class ASTSelfAttention(nn.Module):
def __init__(self, config: ASTConfig) -> None:
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size,} is not a multi... | class_definition | 4,385 | 7,225 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,217 |
class ASTSdpaSelfAttention(ASTSelfAttention):
def __init__(self, config: ASTConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self,
hidden_states: torch.FloatTensor,
head_mask: Optional[torch.Te... | class_definition | 7,314 | 9,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,218 |
class ASTSelfOutput(nn.Module):
"""
The residual connection is defined in ASTLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.... | class_definition | 9,434 | 10,077 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,219 |
class ASTAttention(nn.Module):
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.attention = ASTSelfAttention(config)
self.output = ASTSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) == 0:
... | class_definition | 10,158 | 11,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,220 |
class ASTSdpaAttention(ASTAttention):
def __init__(self, config: ASTConfig) -> None:
super().__init__(config)
self.attention = ASTSdpaSelfAttention(config) | class_definition | 11,920 | 12,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,221 |
class ASTIntermediate(nn.Module):
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | class_definition | 12,179 | 12,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,222 |
class ASTOutput(nn.Module):
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Ten... | class_definition | 12,841 | 13,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,223 |
class ASTLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = AST_ATTENTION_CL... | class_definition | 13,541 | 15,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,224 |
class ASTEncoder(nn.Module):
def __init__(self, config: ASTConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([ASTLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidd... | class_definition | 15,334 | 17,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,225 |
class ASTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ASTConfig
base_model_prefix = "audio_spectrogram_transformer"
main_input_name = "input_values"
suppo... | class_definition | 17,258 | 18,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,226 |
class ASTModel(ASTPreTrainedModel):
def __init__(self, config: ASTConfig) -> None:
super().__init__(config)
self.config = config
self.embeddings = ASTEmbeddings(config)
self.encoder = ASTEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm... | class_definition | 20,845 | 24,117 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,227 |
class ASTMLPHead(nn.Module):
def __init__(self, config: ASTConfig):
super().__init__()
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dense = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
def forward(self,... | class_definition | 24,120 | 24,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,228 |
class ASTForAudioClassification(ASTPreTrainedModel):
def __init__(self, config: ASTConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.audio_spectrogram_transformer = ASTModel(config)
# Classifier head
self.classifier = ASTMLPHead(config)
... | class_definition | 24,862 | 28,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/audio_spectrogram_transformer/modeling_audio_spectrogram_transformer.py | null | 8,229 |
class FuyuProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_attention_mask": True,
"return_overflowing_tokens": False,
"return_special_token... | class_definition | 1,549 | 2,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/processing_fuyu.py | null | 8,230 |
class FuyuProcessor(ProcessorMixin):
r"""
Constructs a Fuyu processor which wraps a Fuyu image processor and a Llama tokenizer into a single processor.
[`FuyuProcessor`] offers all the functionalities of [`FuyuImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~FuyuProcessor.__call__`] and [`~FuyuP... | class_definition | 13,931 | 33,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/processing_fuyu.py | null | 8,231 |
class FuyuBatchFeature(BatchFeature):
"""
BatchFeature class for Fuyu image processor and processor.
The outputs dictionary from the processors contains a mix of tensors and lists of tensors.
"""
def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
"""
... | class_definition | 1,955 | 6,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/image_processing_fuyu.py | null | 8,232 |
class FuyuImageProcessor(BaseImageProcessor):
"""
This class should handle the image processing part before the main FuyuForCausalLM. In particular, it should
handle:
- Processing Images:
Taking a batch of images as input. If the images are variable-sized, it resizes them based on the desired p... | class_definition | 6,248 | 33,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/image_processing_fuyu.py | null | 8,233 |
class FuyuConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FuyuForCausalLM`]. It is used to instantiate an
Fuyu model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 828 | 9,957 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/configuration_fuyu.py | null | 8,234 |
class FuyuPreTrainedModel(PreTrainedModel):
config_class = FuyuConfig
base_model_prefix = "fuyu"
supports_gradient_checkpointing = True
_no_split_modules = []
_skip_keys_device_placement = "past_key_values"
def _init_weights(self, module):
std = self.config.initializer_range
if ... | class_definition | 2,217 | 2,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/modeling_fuyu.py | null | 8,235 |
class FuyuForCausalLM(FuyuPreTrainedModel, GenerationMixin):
def __init__(self, config: FuyuConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.text_config.vocab_size
self.language_model = AutoModelForCausalLM.from_config(config.text_conf... | class_definition | 7,653 | 18,836 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/fuyu/modeling_fuyu.py | null | 8,236 |
class GPTJAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.config = config
max_positions = config.max_position_embeddings
self.attn_dropout = nn.Dropout(config.attn_pdrop)
self.resid_dropout = nn.Dropout(config.resid_pdrop)
s... | class_definition | 2,958 | 10,594 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,237 |
class GPTJFlashAttention2(GPTJAttention):
"""
GPTJ flash attention module. This module inherits from `GPTJAttention` 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 | 10,597 | 17,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,238 |
class GPTJMLP(nn.Module):
def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * embed_dim
super().__init__()
embed_dim = config.n_embd
self.fc_in = nn.Linear(embed_dim, intermediate_size)
self.fc_out = nn.Linear(intermediate_size, embed_dim)
self.... | class_definition | 17,320 | 18,053 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,239 |
class GPTJBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = GPTJ_ATTENTION_CLASSES[config.... | class_definition | 18,056 | 19,911 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,240 |
class GPTJPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
is_parallelizable = True
supports_gradient_checkpointi... | class_definition | 19,914 | 21,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,241 |
class GPTJModel(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.n_embd
self.vocab_size = config.vocab_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.drop = nn.Dropout(config.embd_pdrop)
self.h =... | class_definition | 28,378 | 44,689 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,242 |
class GPTJForCausalLM(GPTJPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPTJModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
# Model parallel
sel... | class_definition | 44,830 | 51,281 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,243 |
class GPTJForSequenceClassification(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTJModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Model parallel
... | class_definition | 52,086 | 57,626 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,244 |
class GPTJForQuestionAnswering(GPTJPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPTJModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Model parallel
self... | class_definition | 57,930 | 62,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_gptj.py | null | 8,245 |
class TFGPTJAttention(keras.layers.Layer):
def __init__(self, config: GPTJConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_attention_heads
if sel... | class_definition | 2,700 | 11,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,246 |
class TFGPTJMLP(keras.layers.Layer):
def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs):
super().__init__(**kwargs)
embed_dim = config.n_embd
self.fc_in = keras.layers.Dense(
intermediate_size, kernel_initializer=get_initializer(config.initializer_range), n... | class_definition | 11,021 | 12,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,247 |
class TFGPTJBlock(keras.layers.Layer):
def __init__(self, config: GPTJConfig, **kwargs):
super().__init__(**kwargs)
inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
... | class_definition | 12,474 | 14,675 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,248 |
class TFGPTJMainLayer(keras.layers.Layer):
config_class = GPTJConfig
def __init__(self, config: GPTJConfig, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_h... | class_definition | 14,698 | 22,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,249 |
class TFGPTJPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpecte... | class_definition | 22,569 | 23,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,250 |
class TFGPTJModel(TFGPTJPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPTJMainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING)
@add_code_samp... | class_definition | 29,683 | 32,010 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,251 |
class TFGPTJForCausalLM(TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPTJMainLayer(config, name="transformer")
self.lm_head = keras.layers.Dense(
config.vocab_... | class_definition | 32,151 | 37,174 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,252 |
class TFGPTJForSequenceClassification(TFGPTJPreTrainedModel, TFSequenceClassificationLoss):
_keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_la... | class_definition | 37,979 | 43,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,253 |
class TFGPTJForQuestionAnswering(TFGPTJPreTrainedModel, TFQuestionAnsweringLoss):
_keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = con... | class_definition | 43,586 | 48,086 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_tf_gptj.py | null | 8,254 |
class GPTJConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 999 | 5,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/configuration_gptj.py | null | 8,255 |
class GPTJOnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_pas... | class_definition | 5,756 | 8,783 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/configuration_gptj.py | null | 8,256 |
class FlaxGPTJAttention(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_... | class_definition | 6,399 | 13,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,257 |
class FlaxGPTJMLP(nn.Module):
config: GPTJConfig
intermediate_size: int
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
kernel_init = jax.nn.initializers.normal(self.config.initializer_range)
self.fc_in = nn.Dense(self.intermediate_size, dtyp... | class_definition | 13,326 | 14,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,258 |
class FlaxGPTJBlock(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
hidden_size = self.config.hidden_size
inner_dim = self.config.n_inner if self.config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(epsilon=self.config.layer_norm_e... | class_definition | 14,223 | 15,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,259 |
class FlaxGPTJPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTJConfig
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__(
... | class_definition | 15,591 | 21,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,260 |
class FlaxGPTJBlockCollection(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGPTJBlock(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_... | class_definition | 21,437 | 22,874 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,261 |
class FlaxGPTJModule(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embed_dim = self.config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.config.hidden_size,
embedding_init=jax.nn.initializers.normal(s... | class_definition | 22,877 | 24,817 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,262 |
class FlaxGPTJModel(FlaxGPTJPreTrainedModel):
module_class = FlaxGPTJModule | class_definition | 24,973 | 25,052 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,263 |
class FlaxGPTJForCausalLMModule(nn.Module):
config: GPTJConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxGPTJModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
dtype=self.dtype,
kernel_init=... | class_definition | 25,178 | 26,734 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,264 |
class FlaxGPTJForCausalLM(FlaxGPTJPreTrainedModel):
module_class = FlaxGPTJForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = s... | class_definition | 26,874 | 28,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gptj/modeling_flax_gptj.py | null | 8,265 |
class DistilBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DistilBertModel`] or a [`TFDistilBertModel`]. It
is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration... | class_definition | 917 | 5,534 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/configuration_distilbert.py | null | 8,266 |
class DistilBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 5,537 | 5,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/configuration_distilbert.py | null | 8,267 |
class TFEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dim = config.dim
self.initializer_range = config.initializer_range
... | class_definition | 1,811 | 4,250 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,268 |
class TFMultiHeadSelfAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.n_heads = config.n_heads
self.dim = config.dim
self.dropout = keras.layers.Dropout(config.attention_dropout)
self.output_attentions = config.output_atten... | class_definition | 4,253 | 8,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,269 |
class TFFFN(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dropout = keras.layers.Dropout(config.dropout)
self.lin1 = keras.layers.Dense(
config.hidden_dim, kernel_initializer=get_initializer(config.initializer_range), name="lin1"
... | class_definition | 8,813 | 10,011 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,270 |
class TFTransformerBlock(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.n_heads = config.n_heads
self.dim = config.dim
self.hidden_dim = config.hidden_dim
self.dropout = keras.layers.Dropout(config.dropout)
self.activation ... | class_definition | 10,014 | 13,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,271 |
class TFTransformer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.n_layers = config.n_layers
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.layer = [TFTransformerBloc... | class_definition | 13,045 | 15,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,272 |
class TFDistilBertMainLayer(keras.layers.Layer):
config_class = DistilBertConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.num_hidden_layers = config.num_hidden_layers
self.output_attentions = config.output_attentions
sel... | class_definition | 15,854 | 18,981 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,273 |
class TFDistilBertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DistilBertConfig
base_model_prefix = "distilbert" | class_definition | 19,034 | 19,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,274 |
class TFDistilBertModel(TFDistilBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertMainLayer(config, name="distilbert") # Embeddings
@unpack_inputs
@add_start_docstrings_to_model_forward(DISTILBERT_... | class_definition | 24,501 | 26,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,275 |
class TFDistilBertLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.dim = config.dim
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each... | class_definition | 26,190 | 27,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,276 |
class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.config = config
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.vocab_tra... | class_definition | 27,792 | 32,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,277 |
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.distilbert = TFDistilBertMainLayer(config, name="distil... | class_definition | 32,390 | 36,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,278 |
class TFDistilBertForTokenClassification(TFDistilBertPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")... | class_definition | 36,519 | 39,575 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,279 |
class TFDistilBertForMultipleChoice(TFDistilBertPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.dropout = keras.layers.Dropout(config.se... | class_definition | 39,818 | 44,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,280 |
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.qa_outputs = keras.layers.Dense(
... | class_definition | 44,546 | 48,844 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_tf_distilbert.py | null | 8,281 |
class Embeddings(nn.Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.dim, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.dim)
self.... | class_definition | 3,114 | 5,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,282 |
class MultiHeadSelfAttention(nn.Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.config = config
self.n_heads = config.n_heads
self.dim = config.dim
self.dropout = nn.Dropout(p=config.attention_dropout)
self.is_causal = False
# ... | class_definition | 5,288 | 9,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,283 |
class DistilBertFlashAttention2(MultiHeadSelfAttention):
"""
DistilBert flash attention module. This module inherits from `MultiHeadSelfAttention` 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 ... | class_definition | 9,668 | 14,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,284 |
class DistilBertSdpaAttention(MultiHeadSelfAttention):
def __init__(self, config: PretrainedConfig):
super().__init__(config=config)
self.dropout_prob = config.attention_dropout
self.require_contiguous_qkv = not is_torch_greater_or_equal_than_2_2
def forward(
self,
query... | class_definition | 14,048 | 17,363 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,285 |
class FFN(nn.Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.dropout = nn.Dropout(p=config.dropout)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.lin1 = nn.Linear(in_features=config.dim, out_features=con... | class_definition | 17,366 | 18,211 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,286 |
class TransformerBlock(nn.Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
# Have an even number of Configure multi-heads
if config.dim % config.n_heads != 0:
raise ValueError(f"config.n_heads {config.n_heads} must divide config.dim {config.dim} evenly")... | class_definition | 18,377 | 20,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,287 |
class Transformer(nn.Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.n_layers = config.n_layers
self.layer = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | class_definition | 20,671 | 23,888 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,288 |
class DistilBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DistilBertConfig
load_tf_weights = None
base_model_prefix = "distilbert"
supports_gradient_ch... | class_definition | 23,941 | 25,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,289 |
class DistilBertModel(DistilBertPreTrainedModel):
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.embeddings = Embeddings(config) # Embeddings
self.transformer = Transformer(config) # Encoder
self._use_flash_attention_2 = config._attn_implementation == ... | class_definition | 28,415 | 34,652 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,290 |
class DistilBertForMaskedLM(DistilBertPreTrainedModel):
_tied_weights_keys = ["vocab_projector.weight"]
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.activation = get_activation(config.activation)
self.distilbert = DistilBertModel(config)
self.voc... | class_definition | 34,787 | 39,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,291 |
class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.distilbert = DistilBertModel(config)
self.pre_classifier = nn.Linear(con... | class_definition | 39,541 | 44,692 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,292 |
class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.distilbert = DistilBertModel(config)
self.qa_outputs = nn.Linear(config.dim, config.num_labels)
if config.num_labels != 2:
raise V... | class_definition | 44,991 | 50,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,293 |
class DistilBertForTokenClassification(DistilBertPreTrainedModel):
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.distilbert = DistilBertModel(config)
self.dropout = nn.Dropout(config.dropout)
self.classifier =... | class_definition | 50,714 | 54,281 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,294 |
class DistilBertForMultipleChoice(DistilBertPreTrainedModel):
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.distilbert = DistilBertModel(config)
self.pre_classifier = nn.Linear(config.dim, config.dim)
self.classifier = nn.Linear(config.dim, 1)
s... | class_definition | 54,524 | 60,112 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_distilbert.py | null | 8,295 |
class DistilBertTokenizer(PreTrainedTokenizer):
r"""
Construct a DistilBERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
... | class_definition | 1,656 | 13,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/tokenization_distilbert.py | null | 8,296 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 13,508 | 20,256 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/tokenization_distilbert.py | null | 8,297 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 20,335 | 22,223 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/tokenization_distilbert.py | null | 8,298 |
class FlaxEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
... | class_definition | 4,826 | 6,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,299 |
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