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 UniSpeechModel(UniSpeechPreTrainedModel):
def __init__(self, config: UniSpeechConfig):
super().__init__(config)
self.config = config
self.feature_extractor = UniSpeechFeatureEncoder(config)
self.feature_projection = UniSpeechFeatureProjection(config)
if config.mask_tim... | class_definition | 61,604 | 66,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,700 |
class UniSpeechForPreTraining(UniSpeechPreTrainedModel):
def __init__(self, config: UniSpeechConfig):
super().__init__(config)
self.unispeech = UniSpeechModel(config)
self.dropout_features = nn.Dropout(config.feat_quantizer_dropout)
self.quantizer = UniSpeechGumbelVectorQuantizer(co... | class_definition | 67,102 | 73,433 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,701 |
class UniSpeechForCTC(UniSpeechPreTrainedModel):
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
self.unispeech = UniSpeechModel(config)
self.dropout = nn.Dropout(config.final_dropout)
self.target_lang = target_lang
if config.vocab_s... | class_definition | 74,068 | 80,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,702 |
class UniSpeechForSequenceClassification(UniSpeechPreTrainedModel):
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 UniSpeech adapters (c... | class_definition | 81,122 | 86,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,703 |
class Idefics2ImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics image processor.
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
Only has an ... | class_definition | 6,019 | 27,452 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/image_processing_idefics2.py | null | 7,704 |
class Idefics2ImagesKwargs(ImagesKwargs, total=False):
image_seq_len: Optional[int] | class_definition | 1,382 | 1,469 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/processing_idefics2.py | null | 7,705 |
class Idefics2ProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: Idefics2ImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"is_split_into_words": False,
},
"images_kwargs": {},
} | class_definition | 1,472 | 1,775 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/processing_idefics2.py | null | 7,706 |
class Idefics2Processor(ProcessorMixin):
r"""
Constructs a IDEFICS2 processor which wraps a LLama tokenizer and IDEFICS2 image processor into a single processor.
[`IdeficsProcessor`] offers all the functionalities of [`Idefics2ImageProcessor`] and [`LlamaTokenizerFast`]. See
the docstring of [`~Idefics... | class_definition | 1,778 | 12,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/processing_idefics2.py | null | 7,707 |
class Idefics2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics2VisionModel`]. It is used to instantiate a
Idefics2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the de... | class_definition | 829 | 4,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/configuration_idefics2.py | null | 7,708 |
class Idefics2PerceiverConfig(PretrainedConfig):
r"""
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_act (`str` or `function`, *optional*, defaults to `"sil... | class_definition | 4,850 | 7,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/configuration_idefics2.py | null | 7,709 |
class Idefics2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics2Model`]. It is used to instantiate a
Idefics2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a s... | class_definition | 7,496 | 11,986 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/configuration_idefics2.py | null | 7,710 |
class Idefics2BaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics2 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 hidd... | class_definition | 1,687 | 4,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,711 |
class Idefics2CausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics2 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).
... | class_definition | 4,709 | 7,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,712 |
class Idefics2VisionEmbeddings(nn.Module):
"""
This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable
resolution.
The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://arxiv.org/abs... | class_definition | 7,360 | 10,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,713 |
class Idefics2VisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidde... | class_definition | 10,297 | 13,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,714 |
class Idefics2VisionFlashAttention2(Idefics2VisionAttention):
"""
Idefics2Vision flash attention module. This module inherits from `Idefics2VisionAttention` 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
... | class_definition | 13,789 | 18,516 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,715 |
class Idefics2VisionMLP(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.hidd... | class_definition | 18,749 | 19,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,716 |
class Idefics2MLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
output_size: int,
hidden_act: str,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.up_proj = nn.Linear(hid... | class_definition | 19,332 | 19,919 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,717 |
class Idefics2MultiheadAttentionPoolingHead(nn.Module):
"""Multihead Attention Pooling."""
def __init__(self, config: Idefics2VisionConfig):
super().__init__()
self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
self.attention = torch.nn.MultiheadAttention(config.hidden_si... | class_definition | 20,037 | 21,128 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,718 |
class Idefics2EncoderLayer(nn.Module):
def __init__(self, config: Idefics2VisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = IDEFICS_VISION_ATTENTION_CLASSES[config._attn_implementation](config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=c... | class_definition | 21,131 | 23,122 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,719 |
class Idefics2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Idefics2EncoderLayer`].
Args:
config: Idefics2Config
"""
def __init__(self, config: Idefics2Config):
super().__init__()
self.confi... | class_definition | 23,218 | 27,111 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,720 |
class Idefics2PreTrainedModel(PreTrainedModel):
config_class = Idefics2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Idefics2VisionAttention", "Idefics2MLP", "Idefics2PerceiverLayer", "Idefics2DecoderLayer"]
_skip_keys_device_placement = "past_key_va... | class_definition | 28,173 | 29,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,721 |
class Idefics2VisionTransformer(Idefics2PreTrainedModel):
_supports_sdpa = False
config_class = Idefics2VisionConfig
def __init__(self, config: Idefics2VisionConfig):
super().__init__(config)
embed_dim = config.hidden_size
self.config = config
self.embeddings = Idefics2Visi... | class_definition | 30,607 | 33,842 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,722 |
class Idefics2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Idefics2RMSNorm 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 | 34,608 | 35,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,723 |
class Idefics2PerceiverAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None) -> None:
"""Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
super().__init__()
self.layer_idx = None
self.hidden_size... | class_definition | 35,337 | 40,383 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,724 |
class Idefics2PerceiverFlashAttention2(Idefics2PerceiverAttention):
"""
Idefics2 flash attention module. This module inherits from `Idefics2PerceiverAttention` 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 ... | class_definition | 40,629 | 46,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,725 |
class Idefics2PerceiverLayer(nn.Module):
def __init__(self, config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.n_latents = config.resampler_n_latents
self.depth = config.resampler_depth
self.rms_norm_eps = config.rms_norm_eps
self.... | class_definition | 46,945 | 49,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,726 |
class Idefics2PerceiverResampler(Idefics2PreTrainedModel):
_supports_sdpa = False
config_class = Idefics2PerceiverConfig
def __init__(self, config) -> None:
super().__init__(config)
self.hidden_size = config.hidden_size
self.hidden_act = config.hidden_act
self.n_latents = co... | class_definition | 50,988 | 53,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,727 |
class Idefics2Connector(nn.Module):
def __init__(self, config):
super().__init__()
self.modality_projection = Idefics2MLP(
hidden_size=config.vision_config.hidden_size,
intermediate_size=config.text_config.intermediate_size,
output_size=config.text_config.hidden_s... | class_definition | 53,113 | 53,888 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,728 |
class Idefics2Model(Idefics2PreTrainedModel):
def __init__(self, config: Idefics2Config):
super().__init__(config)
self.padding_idx = self.config.text_config.pad_token_id
self.vocab_size = self.config.text_config.vocab_size
self.vision_model = Idefics2VisionTransformer._from_config(... | class_definition | 58,754 | 70,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,729 |
class Idefics2ForConditionalGeneration(Idefics2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = Idefics2Model(config)
self.image_token_id = self.config.image_token_id
self.lm_head = nn.L... | class_definition | 70,579 | 82,726 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics2/modeling_idefics2.py | null | 7,730 |
class XLMRobertaXLEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
... | class_definition | 1,936 | 6,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,731 |
class XLMRobertaXLSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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_... | class_definition | 6,126 | 13,484 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,732 |
class XLMRobertaXLSdpaSelfAttention(XLMRobertaXLSelfAttention):
def __init__(self, config, position_embedding_type=None):
super().__init__(config, position_embedding_type=position_embedding_type)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = version.pa... | class_definition | 13,586 | 19,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,733 |
class XLMRobertaXLSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense... | class_definition | 19,234 | 19,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,734 |
class XLMRobertaXLAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.self = XLMROBERTAXL_SELF_ATTENTION_CLASSES[config._attn_implementation](
... | class_definition | 19,833 | 21,914 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,735 |
class XLMRobertaXLIntermediate(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:
sel... | class_definition | 21,987 | 22,560 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,736 |
class XLMRobertaXLOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = hidden_states + inpu... | class_definition | 22,563 | 22,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,737 |
class XLMRobertaXLLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = XLMRobertaXLAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_atte... | class_definition | 22,923 | 26,797 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,738 |
class XLMRobertaXLEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([XLMRobertaXLLayer(config) for _ in range(config.num_hidden_layers)])
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | class_definition | 26,800 | 30,417 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,739 |
class XLMRobertaXLPooler(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 t... | class_definition | 30,484 | 31,051 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,740 |
class XLMRobertaXLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = XLMRobertaXLConfig
base_model_prefix = "roberta"
_no_split_modules = ["XLMRobertaXLEmbeddings",... | class_definition | 31,054 | 32,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,741 |
class XLMRobertaXLModel(XLMRobertaXLPreTrainedModel):
"""
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](https://... | class_definition | 36,109 | 46,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,742 |
class XLMRobertaXLForCausalLM(XLMRobertaXLPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `RobertaLMHeadModel... | class_definition | 47,049 | 55,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,743 |
class XLMRobertaXLForMaskedLM(XLMRobertaXLPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `RobertaForMaskedLM` ma... | class_definition | 55,382 | 59,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,744 |
class XLMRobertaXLLMHead(nn.Module):
"""XLM-RoBERTa-XL Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | class_definition | 59,086 | 60,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,745 |
class XLMRobertaXLForSequenceClassification(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.classifier = XLMRobe... | class_definition | 60,414 | 64,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,746 |
class XLMRobertaXLForMultipleChoice(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta = XLMRobertaXLModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
self.init... | class_definition | 64,421 | 67,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,747 |
class XLMRobertaXLForTokenClassification(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_drop... | class_definition | 68,225 | 71,536 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,748 |
class XLMRobertaXLClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.class... | class_definition | 71,539 | 72,317 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,749 |
class XLMRobertaXLForQuestionAnswering(XLMRobertaXLPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XLMRobertaXLModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_... | class_definition | 72,624 | 76,834 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/modeling_xlm_roberta_xl.py | null | 7,750 |
class XLMRobertaXLConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XLMRobertaXLModel`] or a [`TFXLMRobertaXLModel`].
It is used to instantiate a XLM_ROBERTA_XL model according to the specified arguments, defining the model
architecture. Instantiating a con... | class_definition | 859 | 6,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py | null | 7,751 |
class XLMRobertaXLOnnxConfig(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 | 6,808 | 7,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlm_roberta_xl/configuration_xlm_roberta_xl.py | null | 7,752 |
class LlavaNextProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {
"do_pad": True,
},
} | class_definition | 1,109 | 1,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/processing_llava_next.py | null | 7,753 |
class LlavaNextProcessor(ProcessorMixin):
r"""
Constructs a LLaVa-NeXT processor which wraps a LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
[`LlavaNextProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~LlavaNextP... | class_definition | 1,330 | 11,485 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/processing_llava_next.py | null | 7,754 |
class LlavaNextCausalLMOutputWithPast(ModelOutput):
"""
Base class for LlavaNext 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).
... | class_definition | 5,821 | 8,429 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/modeling_llava_next.py | null | 7,755 |
class LlavaNextMultiModalProjector(nn.Module):
def __init__(self, config: LlavaNextConfig):
super().__init__()
self.linear_1 = nn.Linear(
config.vision_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
)
self.act = ACT2FN[config.pro... | class_definition | 8,534 | 9,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/modeling_llava_next.py | null | 7,756 |
class LlavaNextPreTrainedModel(PreTrainedModel):
config_class = LlavaNextConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlavaNextVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_flash_att... | class_definition | 10,436 | 11,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/modeling_llava_next.py | null | 7,757 |
class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel, GenerationMixin):
def __init__(self, config: LlavaNextConfig):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = LlavaNextMultiModalProjector(config)
... | class_definition | 17,359 | 48,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/modeling_llava_next.py | null | 7,758 |
class LlavaNextImageProcessor(BaseImageProcessor):
r"""
Constructs a LLaVa-NeXT image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
for processing high resolution images as explained in the [LLaVa paper](https://arxiv.org/abs/2310.03744).
Args:
do_resize... | class_definition | 4,537 | 36,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/image_processing_llava_next.py | null | 7,759 |
class LlavaNextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaNextForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the... | class_definition | 831 | 6,770 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_next/configuration_llava_next.py | null | 7,760 |
class ByT5Tokenizer(PreTrainedTokenizer):
"""
Construct a ByT5 tokenizer. ByT5 simply uses raw bytes utf-8 encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Arg... | class_definition | 853 | 10,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/byt5/tokenization_byt5.py | null | 7,761 |
class AltCLIPTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AltCLIPTextModel`]. It is used to instantiate a
AltCLIP text model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will... | class_definition | 840 | 7,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/configuration_altclip.py | null | 7,762 |
class AltCLIPVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an
AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield... | class_definition | 7,227 | 11,414 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/configuration_altclip.py | null | 7,763 |
class AltCLIPConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AltCLIPModel`]. It is used to instantiate an
AltCLIP model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 11,417 | 18,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/configuration_altclip.py | null | 7,764 |
class AltCLIPOutput(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)`):
Th... | class_definition | 8,037 | 9,914 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,765 |
class AltRobertaEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding... | class_definition | 10,019 | 14,202 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,766 |
class AltRobertaSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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_si... | class_definition | 14,310 | 21,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,767 |
class AltRobertaSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 21,744 | 22,356 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,768 |
class AltRobertaAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = ALT_ROBERTA_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = AltRober... | class_definition | 22,562 | 24,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,769 |
class AltRobertaIntermediate(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.... | class_definition | 24,810 | 25,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,770 |
class AltRobertaOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 25,457 | 26,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,771 |
class AltRobertaLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = AltRobertaAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attentio... | class_definition | 26,171 | 30,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,772 |
class AltRobertaEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([AltRobertaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_st... | class_definition | 30,210 | 34,012 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,773 |
class AltRobertaPooler(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... | class_definition | 34,088 | 34,653 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,774 |
class AltCLIPAttention(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 = self... | class_definition | 34,742 | 39,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,775 |
class AltCLIPMLP(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.hidden_size... | class_definition | 39,558 | 40,131 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,776 |
class AltCLIPEncoderLayer(nn.Module):
def __init__(self, config: AltCLIPConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = AltCLIPAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = AltCLIPMLP(c... | class_definition | 40,134 | 42,091 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,777 |
class AltCLIPEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`AltCLIPEncoderLayer`].
Args:
config: AltCLIPConfig
"""
def __init__(self, config: AltCLIPConfig):
super().__init__()
self.config = ... | class_definition | 42,094 | 46,497 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,778 |
class AltCLIPVisionEmbeddings(nn.Module):
def __init__(self, config: AltCLIPVisionConfig):
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 | 46,593 | 50,425 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,779 |
class AltCLIPPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AltCLIPConfig
base_model_prefix = "altclip"
supports_gradient_checkpointing = True
_no_split_mod... | class_definition | 50,428 | 53,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,780 |
class AltCLIPVisionTransformer(nn.Module):
def __init__(self, config: AltCLIPVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = AltCLIPVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_n... | class_definition | 53,353 | 55,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,781 |
class AltCLIPVisionModel(AltCLIPPreTrainedModel):
config_class = AltCLIPVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: AltCLIPVisionConfig):
super().__init__(config)
self.vision_model = AltCLIPVisionTransformer(config)
# Initialize weights and apply final p... | class_definition | 55,749 | 57,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,782 |
class AltRobertaModel(AltCLIPPreTrainedModel):
"""
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 Vasw... | class_definition | 57,851 | 66,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,783 |
class AltCLIPTextModel(AltCLIPPreTrainedModel):
config_class = AltCLIPTextConfig
def __init__(self, config):
super().__init__(config)
self.roberta = AltRobertaModel(config, add_pooling_layer=False)
self.transformation = nn.Linear(config.hidden_size, config.project_dim)
self.pre_... | class_definition | 66,985 | 70,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,784 |
class AltCLIPModel(AltCLIPPreTrainedModel):
config_class = AltCLIPConfig
def __init__(self, config: AltCLIPConfig):
super().__init__(config)
if not isinstance(config.vision_config, AltCLIPVisionConfig):
raise TypeError(
"config.vision_config is expected to be of typ... | class_definition | 70,501 | 80,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/modeling_altclip.py | null | 7,785 |
class AltClipProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {} | class_definition | 1,003 | 1,082 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/processing_altclip.py | null | 7,786 |
class AltCLIPProcessor(ProcessorMixin):
r"""
Constructs a AltCLIP processor which wraps a CLIP image processor and a XLM-Roberta tokenizer into a single
processor.
[`AltCLIPProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`XLMRobertaTokenizerFast`]. See
the [`~AltCLIPProces... | class_definition | 1,085 | 6,870 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/altclip/processing_altclip.py | null | 7,787 |
class ImageGPTImageProcessor(BaseImageProcessor):
r"""
Constructs a ImageGPT image processor. This image processor can be used to resize images to a smaller resolution
(such as 32x32 or 64x64), normalize them and finally color quantize them to obtain sequences of "pixel values"
(color clusters).
Ar... | class_definition | 1,677 | 14,304 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/image_processing_imagegpt.py | null | 7,788 |
class ImageGPTFeatureExtractor(ImageGPTImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ImageGPTImageProcessor instead.",
FutureWa... | class_definition | 821 | 1,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/feature_extraction_imagegpt.py | null | 7,789 |
class ImageGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the de... | class_definition | 964 | 6,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/configuration_imagegpt.py | null | 7,790 |
class ImageGPTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
preprocessor: "FeatureExtractio... | class_definition | 6,413 | 8,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/configuration_imagegpt.py | null | 7,791 |
class ImageGPTLayerNorm(nn.Module):
def __init__(self, hidden_size: Tuple[int], eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.Tensor(hidden_size))
def forward(self, tensor: torch.Tensor) -> tuple:
# input is not mean centered
retu... | class_definition | 6,149 | 6,634 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,792 |
class ImageGPTAttention(nn.Module):
def __init__(self, config, is_cross_attention: Optional[bool] = False, layer_idx: Optional[int] = None):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_pos... | class_definition | 6,637 | 16,514 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,793 |
class ImageGPTMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
... | class_definition | 16,517 | 17,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,794 |
class ImageGPTBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = ImageGPTLayerNorm(hidden_size, eps=config.layer_norm_epsilon)
... | class_definition | 17,188 | 20,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,795 |
class ImageGPTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ImageGPTConfig
load_tf_weights = load_tf_weights_in_imagegpt
base_model_prefix = "transformer"
... | class_definition | 20,437 | 22,589 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,796 |
class ImageGPTModel(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
... | class_definition | 27,391 | 39,576 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,797 |
class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.transformer = ImageGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size... | class_definition | 39,785 | 46,365 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,798 |
class ImageGPTForImageClassification(ImageGPTPreTrainedModel):
def __init__(self, config: ImageGPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = ImageGPTModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
... | class_definition | 46,638 | 52,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/imagegpt/modeling_imagegpt.py | null | 7,799 |
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