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 AriaGroupedExpertsGemm(nn.Module):
"""
Grouped GEMM (General Matrix Multiplication) module for efficient expert computation.
This module utilizes the grouped_gemm library (https://github.com/fanshiqing/grouped_gemm)
for optimized performance. If the grouped_gemm library is not installed, it gracef... | class_definition | 11,046 | 12,537 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,200 |
class AriaGroupedExpertsMLP(nn.Module):
"""
Grouped MLP module for Mixture of Experts.
Args:
config (`AriaTextConfig`):
Configuration object for the model.
"""
def __init__(self, config: AriaTextConfig) -> None:
super().__init__()
self.config = config
se... | class_definition | 12,540 | 13,735 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,201 |
class AriaTextMoELayer(nn.Module):
"""
Aria Text Mixture of Experts (MoE) Layer.
This layer applies a gating mechanism to route input tokens to different experts.
Args:
config (`AriaTextConfig`):
Configuration object for the text component of the model.
"""
def __init__(se... | class_definition | 13,915 | 16,806 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,202 |
class AriaTextAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: AriaTextConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", conf... | class_definition | 20,085 | 23,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,203 |
class AriaTextDecoderLayer(nn.Module):
"""
Aria Text Decoder Layer.
This class defines a single decoder layer in the language model, incorporating self-attention and Mixture of Experts (MoE) feed-forward network.
Args:
config (`AriaTextConfig`):
Configuration object for the text co... | class_definition | 23,661 | 26,124 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,204 |
class AriaTextPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
"""
config_class = AriaConfig
base_model_prefix = "model"
_no_split_modules = ["AriaTextDecoderLayer", "AriaGroupedExperts... | class_definition | 26,127 | 27,446 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,205 |
class AriaPreTrainedModel(PreTrainedModel):
config_class = AriaTextConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["AriaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_sup... | class_definition | 28,478 | 29,507 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,206 |
class AriaTextRotaryEmbedding(nn.Module):
def __init__(self, config: AriaTextConfig, 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_typ... | class_definition | 29,510 | 32,711 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,207 |
class AriaTextModel(AriaTextPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`AriaTextDecoderLayer`]
Args:
config: AriaTextConfig
"""
def __init__(self, config: AriaTextConfig):
super().__init__(config)
self.padding... | class_definition | 37,526 | 48,779 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,208 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 48,782 | 48,844 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,209 |
class AriaTextForCausalLM(AriaTextPreTrainedModel, GenerationMixin):
"""
Aria model for causal language modeling tasks.
This class extends `LlamaForCausalLM` to incorporate the Mixture of Experts (MoE) approach,
allowing for more efficient and scalable language modeling.
Args:
config (`Ari... | class_definition | 48,847 | 54,380 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,210 |
class AriaCausalLMOutputWithPast(ModelOutput):
"""
Base class for Aria 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).
logits ... | class_definition | 54,394 | 56,978 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,211 |
class AriaForConditionalGeneration(AriaPreTrainedModel, GenerationMixin):
config_class = AriaConfig
_supports_flash_attn_2 = False
_supports_sdpa = False
_tied_weights_keys = ["language_model.lm_head.weight"]
def __init__(self, config: AriaConfig):
super().__init__(config)
self.vis... | class_definition | 59,618 | 70,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,212 |
class AriaImageProcessor(BaseImageProcessor):
"""
A vision processor for the Aria model that handles image preprocessing.
Initialize the AriaImageProcessor.
Args:
image_mean (`list`, *optional*, defaults to [0.5, 0.5, 0.5]):
Mean values for normalization.
image_std (`list`, ... | class_definition | 4,320 | 22,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/image_processing_aria.py | null | 7,213 |
class AriaProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {
"max_image_size": 980,
"split_image": False,
},
"return_tensors": TensorType.PYTORCH,
} | class_definition | 1,809 | 2,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/processing_aria.py | null | 7,214 |
class AriaProcessor(ProcessorMixin):
"""
AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
Args:
image_processor (`AriaImageProcessor`, *optional*):
The AriaImageProcessor to use for image preprocessing.
tokeniz... | class_definition | 2,112 | 7,628 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/processing_aria.py | null | 7,215 |
class AriaTextConfig(PretrainedConfig):
r"""
This class handles the configuration for the text component of the Aria model.
Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria
[rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architec... | class_definition | 1,652 | 12,483 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/configuration_aria.py | null | 7,216 |
class AriaConfig(PretrainedConfig):
r"""
This class handles the configuration for both vision and text components of the Aria model,
as well as additional parameters for image token handling and projector mapping.
Instantiating a configuration with the defaults will yield a similar configuration to that... | class_definition | 12,486 | 16,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/configuration_aria.py | null | 7,217 |
class OwlViTProcessor(ProcessorMixin):
r"""
Constructs an OWL-ViT processor which wraps [`OwlViTImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`]
into a single processor that interits both the image processor and tokenizer functionalities. See the
[`~OwlViTProcessor.__call__`] and [`~OwlViTPr... | class_definition | 1,064 | 15,276 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/processing_owlvit.py | null | 7,218 |
class OwlViTTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults wil... | class_definition | 1,022 | 5,737 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/configuration_owlvit.py | null | 7,219 |
class OwlViTVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
an OWL-ViT image encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaul... | class_definition | 5,740 | 9,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/configuration_owlvit.py | null | 7,220 |
class OwlViTConfig(PretrainedConfig):
r"""
[`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
configs. Instantiating a configuration with t... | class_definition | 9,785 | 12,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/configuration_owlvit.py | null | 7,221 |
class OwlViTOnnxConfig(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"}),
("a... | class_definition | 12,851 | 14,322 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/configuration_owlvit.py | null | 7,222 |
class OwlViTOutput(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)`):
The... | class_definition | 2,202 | 4,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,223 |
class OwlViTObjectDetectionOutput(ModelOutput):
"""
Output type of [`OwlViTForObjectDetection`].
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
Total loss as a linear combination of a negative log-likehood (cross-entropy) for class pr... | class_definition | 6,770 | 9,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,224 |
class OwlViTImageGuidedObjectDetectionOutput(ModelOutput):
"""
Output type of [`OwlViTForObjectDetection.image_guided_detection`].
Args:
logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`):
Classification logits (including no-object) for all queries.
t... | class_definition | 9,636 | 12,563 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,225 |
class OwlViTVisionEmbeddings(nn.Module):
def __init__(self, config: OwlViTVisionConfig):
super().__init__()
self.patch_size = config.patch_size
self.config = config
self.embed_dim = config.hidden_size
self.class_embedding = nn.Parameter(torch.randn(config.hidden_size))
... | class_definition | 12,566 | 16,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,226 |
class OwlViTTextEmbeddings(nn.Module):
def __init__(self, config: OwlViTTextConfig):
super().__init__()
self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embedding = nn.Embedding(config.max_position_embeddings, config.hidden_size)
# position_id... | class_definition | 16,149 | 17,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,227 |
class OwlViTAttention(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 | 17,358 | 22,252 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,228 |
class OwlViTMLP(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 | 22,334 | 22,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,229 |
class OwlViTEncoderLayer(nn.Module):
def __init__(self, config: OwlViTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = OwlViTAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = OwlViTMLP(confi... | class_definition | 23,009 | 24,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,230 |
class OwlViTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = OwlViTConfig
base_model_prefix = "owlvit"
supports_gradient_checkpointing = True
_no_split_module... | class_definition | 24,965 | 27,656 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,231 |
class OwlViTEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`OwlViTEncoderLayer`].
Args:
config: OwlViTConfig
"""
def __init__(self, config: OwlViTConfig):
super().__init__()
self.layers = nn.M... | class_definition | 34,907 | 38,950 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,232 |
class OwlViTTextTransformer(nn.Module):
def __init__(self, config: OwlViTTextConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = OwlViTTextEmbeddings(config)
self.encoder = OwlViTEncoder(config)
self.final_layer_norm = nn.... | class_definition | 38,953 | 42,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,233 |
class OwlViTTextModel(OwlViTPreTrainedModel):
config_class = OwlViTTextConfig
def __init__(self, config: OwlViTTextConfig):
super().__init__(config)
self.text_model = OwlViTTextTransformer(config)
# Initialize weights and apply final processing
self.post_init()
def get_inpu... | class_definition | 42,367 | 44,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,234 |
class OwlViTVisionTransformer(nn.Module):
def __init__(self, config: OwlViTVisionConfig):
super().__init__()
self.config = config
self.embeddings = OwlViTVisionEmbeddings(config)
self.pre_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.encoder = ... | class_definition | 44,352 | 46,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,235 |
class OwlViTVisionModel(OwlViTPreTrainedModel):
config_class = OwlViTVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: OwlViTVisionConfig):
super().__init__(config)
self.vision_model = OwlViTVisionTransformer(config)
# Initialize weights and apply final proces... | class_definition | 46,794 | 48,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,236 |
class OwlViTModel(OwlViTPreTrainedModel):
config_class = OwlViTConfig
def __init__(self, config: OwlViTConfig):
super().__init__(config)
if not isinstance(config.text_config, OwlViTTextConfig):
raise TypeError(
"config.text_config is expected to be of type OwlViTTex... | class_definition | 48,864 | 58,114 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,237 |
class OwlViTBoxPredictionHead(nn.Module):
def __init__(self, config: OwlViTConfig, out_dim: int = 4):
super().__init__()
width = config.vision_config.hidden_size
self.dense0 = nn.Linear(width, width)
self.dense1 = nn.Linear(width, width)
self.gelu = nn.GELU()
self.de... | class_definition | 58,117 | 58,755 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,238 |
class OwlViTClassPredictionHead(nn.Module):
def __init__(self, config: OwlViTConfig):
super().__init__()
out_dim = config.text_config.hidden_size
self.query_dim = config.vision_config.hidden_size
self.dense0 = nn.Linear(self.query_dim, out_dim)
self.logit_shift = nn.Linear(... | class_definition | 58,758 | 60,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,239 |
class OwlViTForObjectDetection(OwlViTPreTrainedModel):
config_class = OwlViTConfig
def __init__(self, config: OwlViTConfig):
super().__init__(config)
self.owlvit = OwlViTModel(config)
self.class_head = OwlViTClassPredictionHead(config)
self.box_head = OwlViTBoxPredictionHead(co... | class_definition | 60,743 | 81,451 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/modeling_owlvit.py | null | 7,240 |
class OwlViTFeatureExtractor(OwlViTImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use OwlViTImageProcessor instead.",
FutureWarning,
... | class_definition | 815 | 1,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | null | 7,241 |
class OwlViTImageProcessor(BaseImageProcessor):
r"""
Constructs an OWL-ViT image processor.
This image processor inherits from [`ImageProcessingMixin`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
do_resi... | class_definition | 4,062 | 29,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/owlvit/image_processing_owlvit.py | null | 7,242 |
class VipLlavaCausalLMOutputWithPast(ModelOutput):
"""
Base class for VipLlava 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 | 1,404 | 3,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vipllava/modeling_vipllava.py | null | 7,243 |
class VipLlavaMultiModalProjector(nn.Module):
def __init__(self, config: VipLlavaConfig):
super().__init__()
self.projector_layernorm = nn.LayerNorm(
len(config.vision_feature_layers) * config.vision_config.hidden_size, eps=config.projector_layernorm_eps
)
self.linear_1 ... | class_definition | 3,999 | 4,940 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vipllava/modeling_vipllava.py | null | 7,244 |
class VipLlavaPreTrainedModel(PreTrainedModel):
config_class = VipLlavaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["VipLlavaVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_flash_attn_2... | class_definition | 6,115 | 7,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vipllava/modeling_vipllava.py | null | 7,245 |
class VipLlavaForConditionalGeneration(VipLlavaPreTrainedModel, GenerationMixin):
def __init__(self, config: VipLlavaConfig):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = VipLlavaMultiModalProjector(config)
sel... | class_definition | 12,474 | 28,223 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vipllava/modeling_vipllava.py | null | 7,246 |
class VipLlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VipLlavaForConditionalGeneration`]. It is used to instantiate an
VipLlava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the def... | class_definition | 886 | 5,181 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vipllava/configuration_vipllava.py | null | 7,247 |
class IJepaPatchEmbeddings(nn.Module):
"""
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__(self, config):
... | class_definition | 1,617 | 3,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,248 |
class IJepaEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: IJepaConfig, use_mask_token: bool = False) -> None:
super().__init__()
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hi... | class_definition | 3,572 | 7,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,249 |
class IJepaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IJepaConfig
base_model_prefix = "ijepa"
main_input_name = "pixel_values"
supports_gradient_checkpo... | class_definition | 7,031 | 8,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,250 |
class IJepaSelfAttention(nn.Module):
def __init__(self, config: IJepaConfig) -> 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 m... | class_definition | 8,497 | 11,341 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,251 |
class IJepaSdpaSelfAttention(IJepaSelfAttention):
def __init__(self, config: IJepaConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self,
hidden_states: torch.FloatTensor,
head_mask: Optional[to... | class_definition | 11,344 | 13,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,252 |
class IJepaSelfOutput(nn.Module):
"""
The residual connection is defined in IJepaLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.dense = nn.Linear(c... | class_definition | 13,393 | 14,042 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,253 |
class IJepaAttention(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.attention = IJepaSelfAttention(config)
self.output = IJepaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) ... | class_definition | 14,045 | 15,730 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,254 |
class IJepaSdpaAttention(IJepaAttention):
def __init__(self, config: IJepaConfig) -> None:
super().__init__(config)
self.attention = IJepaSdpaSelfAttention(config) | class_definition | 15,733 | 15,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,255 |
class IJepaIntermediate(nn.Module):
def __init__(self, config: IJepaConfig) -> 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 | 15,919 | 16,507 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,256 |
class IJepaOutput(nn.Module):
def __init__(self, config: IJepaConfig) -> 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... | class_definition | 16,510 | 17,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,257 |
class IJepaLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = IJEPA_ATTENT... | class_definition | 17,137 | 18,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,258 |
class IJepaEncoder(nn.Module):
def __init__(self, config: IJepaConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([IJepaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | class_definition | 18,868 | 20,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,259 |
class IJepaPooler(nn.Module):
def __init__(self, config: IJepaConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state correspo... | class_definition | 20,798 | 21,341 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,260 |
class IJepaModel(IJepaPreTrainedModel):
def __init__(self, config: IJepaConfig, add_pooling_layer: bool = False, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = IJepaEmbeddings(config, use_mask_token=use_mask_token)
self.encoder = IJepaE... | class_definition | 23,417 | 27,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,261 |
class IJepaForImageClassification(IJepaPreTrainedModel):
def __init__(self, config: IJepaConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.ijepa = IJepaModel(config, add_pooling_layer=False)
# Classifier head
self.classifier = nn.Linear(conf... | class_definition | 28,336 | 32,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modeling_ijepa.py | null | 7,262 |
class IJepaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IJepaModel`]. It is used to instantiate an IJEPA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 714 | 4,800 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/configuration_ijepa.py | null | 7,263 |
class IJepaEmbeddings(ViTEmbeddings):
def __init__(self, config: IJepaConfig, use_mask_token: bool = False) -> None:
super().__init__(config, use_mask_token)
# Remove cls_token from IJepaEmbeddings, as it is not used in the model
del self.cls_token
num_patches = self.patch_embeddings... | class_definition | 527 | 3,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modular_ijepa.py | null | 7,264 |
class IJepaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IJepaConfig
base_model_prefix = "ijepa"
main_input_name = "pixel_values"
supports_gradient_checkpo... | class_definition | 3,707 | 5,170 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modular_ijepa.py | null | 7,265 |
class IJepaModel(IJepaPreTrainedModel, ViTModel):
def __init__(self, config: IJepaConfig, add_pooling_layer: bool = False, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = IJepaEmbeddings(config, use_mask_token=use_mask_token) | class_definition | 5,986 | 6,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modular_ijepa.py | null | 7,266 |
class IJepaForImageClassification(IJepaPreTrainedModel, ViTForImageClassification):
def __init__(self, config: IJepaConfig):
super().__init__(config)
self.ijepa = IJepaModel(config, add_pooling_layer=False)
self.post_init()
def forward(
self,
pixel_values: Optional[torch... | class_definition | 6,920 | 10,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ijepa/modular_ijepa.py | null | 7,267 |
class Idefics3ImageProcessor(BaseImageProcessor):
r"""
Constructs a Idefics3 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 | 10,321 | 42,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/image_processing_idefics3.py | null | 7,268 |
class Idefics3ImagesKwargs(ImagesKwargs, total=False):
return_row_col_info: Optional[bool]
max_image_size: Optional[Dict[str, int]] | class_definition | 2,941 | 3,080 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/processing_idefics3.py | null | 7,269 |
class Idefics3ProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: Idefics3ImagesKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"is_split_into_words": False,
},
"images_kwargs": {
"return_row_... | class_definition | 3,083 | 3,436 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/processing_idefics3.py | null | 7,270 |
class Idefics3Processor(ProcessorMixin):
r"""
Constructs a Idefics3 processor which wraps a LLama tokenizer and Idefics3 image processor into a single processor.
[`Idefics3Processor`] offers all the functionalities of [`Idefics3ImageProcessor`] and [`Idefics3TokenizerFast`]. See
the docstring of [`~Ide... | class_definition | 3,549 | 16,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/processing_idefics3.py | null | 7,271 |
class Idefics3VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics3VisionModel`]. It is used to instantiate a
Idefics3 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the de... | class_definition | 829 | 4,886 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/configuration_idefics3.py | null | 7,272 |
class Idefics3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Idefics3Model`]. It is used to instantiate a
Idefics3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a s... | class_definition | 4,889 | 8,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/configuration_idefics3.py | null | 7,273 |
class Idefics3BaseModelOutputWithPast(ModelOutput):
"""
Base class for Idefics3 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 | 1,636 | 4,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,274 |
class Idefics3CausalLMOutputWithPast(ModelOutput):
"""
Base class for Idefics causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
... | class_definition | 4,513 | 7,128 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,275 |
class Idefics3VisionEmbeddings(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 | 7,241 | 10,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,276 |
class Idefics3VisionAttention(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 | 10,178 | 13,667 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,277 |
class Idefics3VisionFlashAttention2(Idefics3VisionAttention):
"""
Idefics3Vision flash attention module. This module inherits from `Idefics3VisionAttention` 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 | 13,785 | 18,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,278 |
class Idefics3VisionMLP(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 | 18,745 | 19,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,279 |
class Idefics3SimpleMLP(nn.Module):
def __init__(self, config):
super().__init__()
input_size = config.vision_config.hidden_size * (config.scale_factor**2)
output_size = config.text_config.hidden_size
self.proj = nn.Linear(input_size, output_size, bias=False)
def forward(self, x... | class_definition | 19,328 | 19,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,280 |
class Idefics3EncoderLayer(nn.Module):
def __init__(self, config: Idefics3VisionConfig):
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 | 19,787 | 21,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,281 |
class Idefics3Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Idefics3EncoderLayer`].
Args:
config: Idefics3Config
"""
def __init__(self, config: Idefics3Config):
super().__init__()
self.confi... | class_definition | 21,874 | 25,767 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,282 |
class Idefics3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Idefics3RMSNorm 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 | 26,533 | 27,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,283 |
class Idefics3Connector(nn.Module):
def __init__(self, config):
super().__init__()
self.scale_factor = config.scale_factor
self.modality_projection = Idefics3SimpleMLP(config)
def pixel_shuffle(self, x, scale_factor=2):
bsz, seq, embed_dim = x.size()
height = width = int... | class_definition | 27,262 | 28,258 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,284 |
class Idefics3PreTrainedModel(PreTrainedModel):
config_class = Idefics3Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Idefics3VisionAttention", "Idefics3DecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
... | class_definition | 29,320 | 30,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,285 |
class Idefics3VisionTransformer(Idefics3PreTrainedModel):
config_class = Idefics3VisionConfig
_supports_sdpa = False
def __init__(self, config: Idefics3VisionConfig):
super().__init__(config)
embed_dim = config.hidden_size
self.embeddings = Idefics3VisionEmbeddings(config)
... | class_definition | 31,596 | 35,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,286 |
class Idefics3Model(Idefics3PreTrainedModel):
def __init__(self, config: Idefics3Config):
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 = Idefics3VisionTransformer._from_config(... | class_definition | 39,903 | 51,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,287 |
class Idefics3ForConditionalGeneration(Idefics3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
# Copied from transformers.models.idefics2.modeling_idefics2.Idefics2ForConditionalGeneration.__init__ with Idefics2->Idefics3
def __init__(self, config):
super().__init__(confi... | class_definition | 51,576 | 64,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics3/modeling_idefics3.py | null | 7,288 |
class PatchTSTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`PatchTSTModel`]. It is used to instantiate an
PatchTST model according to the specified arguments, defining the model architecture.
[ibm/patchtst](https://huggingface.co/ibm/patchtst) architec... | class_definition | 847 | 12,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/configuration_patchtst.py | null | 7,289 |
class PatchTSTAttention(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,
... | class_definition | 1,269 | 8,667 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,290 |
class PatchTSTBatchNorm(nn.Module):
"""
Compute batch normalization over the sequence length (time) dimension.
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.batchnorm = nn.BatchNorm1d(config.d_model, eps=config.norm_eps)
def forward(self, inputs: torch.Ten... | class_definition | 8,670 | 9,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,291 |
class PatchTSTPatchify(nn.Module):
"""
A class to patchify the time series sequence into different patches
Returns:
`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.seque... | class_definition | 14,779 | 16,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,292 |
class PatchTSTMasking(nn.Module):
"""
Class to perform random or forecast masking.
Parameters:
config (`PatchTSTConfig`): model config
Returns:
x_mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`)
Masked patched input
mask (`torch.... | class_definition | 16,829 | 19,315 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,293 |
class PatchTSTEncoderLayer(nn.Module):
"""
PatchTST encoder layer
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.channel_attention = config.channel_attention
# Multi-Head attention
self.self_attn = PatchTSTAttention(
embed_dim=config... | class_definition | 19,318 | 26,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,294 |
class PatchTSTPreTrainedModel(PreTrainedModel):
config_class = PatchTSTConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = False
def _init_weights(self, module):
"""
Initialize weights
"""
if isinstance(module, PatchTS... | class_definition | 26,349 | 27,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,295 |
class PatchTSTEmbedding(nn.Module):
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.num_input_channels = config.num_input_channels
self.share_embedding = config.share_embedding
# Input encoding: projection of feature vectors onto a d-dim vector space
if se... | class_definition | 27,667 | 29,391 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,296 |
class PatchTSTPositionalEncoding(nn.Module):
"""
Class for positional encoding
"""
def __init__(self, config: PatchTSTConfig, num_patches: int):
super().__init__()
self.use_cls_token = config.use_cls_token
self.num_input_channels = config.num_input_channels
if config.use... | class_definition | 29,394 | 32,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,297 |
class PatchTSTEncoder(PatchTSTPreTrainedModel):
"""
PatchTST Encoder
"""
def __init__(self, config: PatchTSTConfig, num_patches: int):
super().__init__(config)
self.gradient_checkpointing = False
# Input embedding: projection of feature vectors onto a d-dim vector space
... | class_definition | 32,275 | 34,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,298 |
class PatchTSTModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Parameters:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`):
Sequence of hidden-states at the output of the last layer of th... | class_definition | 35,847 | 37,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,299 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.