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
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|---|---|---|---|---|---|---|---|
class MobileNetV2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MobileNetV2Config
load_tf_weights = load_tf_weights_in_mobilenet_v2
base_model_prefix = "mobilen... | class_definition | 18,309 | 19,203 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,600 |
class MobileNetV2Model(MobileNetV2PreTrainedModel):
def __init__(self, config: MobileNetV2Config, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
# Output channels for the projection layers
channels = [16, 24, 24, 32, 32, 32, 64, 64, 64, 64, 96, 96, 96... | class_definition | 20,617 | 24,706 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,601 |
class MobileNetV2ForImageClassification(MobileNetV2PreTrainedModel):
def __init__(self, config: MobileNetV2Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilenet_v2 = MobileNetV2Model(config)
last_hidden_size = self.mobilenet_v2.conv_1x1.convol... | class_definition | 24,919 | 28,365 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,602 |
class MobileNetV2DeepLabV3Plus(nn.Module):
"""
The neural network from the paper "Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation" https://arxiv.org/abs/1802.02611
"""
def __init__(self, config: MobileNetV2Config) -> None:
super().__init__()
self.a... | class_definition | 28,368 | 30,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,603 |
class MobileNetV2ForSemanticSegmentation(MobileNetV2PreTrainedModel):
def __init__(self, config: MobileNetV2Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.mobilenet_v2 = MobileNetV2Model(config, add_pooling_layer=False)
self.segmentation_head = Mo... | class_definition | 30,813 | 34,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,604 |
class MobileNetV2FeatureExtractor(MobileNetV2ImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use MobileNetV2ImageProcessor instead.",
... | class_definition | 831 | 1,221 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/feature_extraction_mobilenet_v2.py | null | 7,605 |
class FlaxVisionEncoderDecoderModule(nn.Module):
config: VisionEncoderDecoderConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
encoder_config = self.config.encoder
decoder_config = self.config.decoder
# Copied from `modeling_hybrid_clip.py` with modifications.
from ...... | class_definition | 10,042 | 13,933 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | null | 7,606 |
class FlaxVisionEncoderDecoderModel(FlaxPreTrainedModel):
r"""
[`FlaxVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with the module (flax.nn.Module) of one of the base vision model classes of the library as encoder module and
another one as d... | class_definition | 13,998 | 41,532 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/modeling_flax_vision_encoder_decoder.py | null | 7,607 |
class VisionEncoderDecoderConfig(PretrainedConfig):
r"""
[`VisionEncoderDecoderConfig`] is the configuration class to store the configuration of a
[`VisionEncoderDecoderModel`]. It is used to instantiate a Vision-Encoder-Text-Decoder model according to the
specified arguments, defining the encoder and d... | class_definition | 1,050 | 4,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py | null | 7,608 |
class VisionEncoderDecoderEncoderOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
... | class_definition | 4,857 | 5,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py | null | 7,609 |
class VisionEncoderDecoderDecoderOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict()
common_inputs["input_ids"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
common_inputs["attention_mask"] = {0: "batch", 1: "past_d... | class_definition | 5,440 | 6,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py | null | 7,610 |
class VisionEncoderDecoderOnnxConfig(OnnxConfig):
@property
def inputs(self) -> None:
pass
def get_encoder_config(self, encoder_config: PretrainedConfig) -> OnnxConfig:
r"""
Returns ONNX encoder config for `VisionEncoderDecoder` model.
Args:
encoder_config (`Pre... | class_definition | 6,823 | 8,337 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py | null | 7,611 |
class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
r"""
[`TFVisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture
with one of the base vision model classes of the library as encoder and another one of the base model clas... | class_definition | 10,282 | 36,236 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py | null | 7,612 |
class VisionEncoderDecoderModel(PreTrainedModel, GenerationMixin):
r"""
[`VisionEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with
one of the base vision model classes of the library as encoder and another one as decoder when created with the
:met... | class_definition | 8,987 | 34,434 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py | null | 7,613 |
class ChineseClipProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {} | class_definition | 947 | 1,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/processing_chinese_clip.py | null | 7,614 |
class ChineseCLIPProcessor(ProcessorMixin):
r"""
Constructs a Chinese-CLIP processor which wraps a Chinese-CLIP image processor and a Chinese-CLIP tokenizer into a
single processor.
[`ChineseCLIPProcessor`] offers all the functionalities of [`ChineseCLIPImageProcessor`] and [`BertTokenizerFast`].
S... | class_definition | 1,033 | 7,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/processing_chinese_clip.py | null | 7,615 |
class ChineseCLIPFeatureExtractor(ChineseCLIPImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ChineseCLIPImageProcessor instead.",
... | class_definition | 856 | 1,246 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/feature_extraction_chinese_clip.py | null | 7,616 |
class ChineseCLIPOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
... | class_definition | 2,229 | 4,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,617 |
class ChineseCLIPTextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.posit... | class_definition | 4,316 | 7,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,618 |
class ChineseCLIPVisionEmbeddings(nn.Module):
def __init__(self, config: ChineseCLIPVisionConfig):
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... | class_definition | 7,600 | 11,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,619 |
class ChineseCLIPTextSelfAttention(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.hidd... | class_definition | 11,541 | 18,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,620 |
class ChineseCLIPTextSelfOutput(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 | 19,003 | 19,620 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,621 |
class ChineseCLIPTextAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = CHINESE_CLIP_TEXT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output... | class_definition | 19,833 | 21,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,622 |
class ChineseCLIPVisionAttention(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_... | class_definition | 21,993 | 25,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,623 |
class ChineseCLIPTextIntermediate(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:
... | class_definition | 25,724 | 26,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,624 |
class ChineseCLIPTextOutput(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_pro... | class_definition | 26,394 | 27,013 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,625 |
class ChineseCLIPVisionMLP(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.h... | class_definition | 27,106 | 27,689 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,626 |
class ChineseCLIPTextLayer(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 = ChineseCLIPTextAttention(config)
self.is_decoder = config.is_decoder
self.add_cros... | class_definition | 27,782 | 31,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,627 |
class ChineseCLIPVisionLayer(nn.Module):
def __init__(self, config: ChineseCLIPConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = ChineseCLIPVisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.m... | class_definition | 31,747 | 33,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,628 |
class ChineseCLIPTextPooler(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 takin... | class_definition | 33,401 | 33,971 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,629 |
class ChineseCLIPPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ChineseCLIPConfig
base_model_prefix = "chinese_clip"
supports_gradient_checkpointing = True
... | class_definition | 33,974 | 37,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,630 |
class ChineseCLIPTextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ChineseCLIPTextLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | class_definition | 44,370 | 48,182 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,631 |
class ChineseCLIPVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`ChineseCLIPVisionEncoderLayer`].
Args:
config: ChineseCLIPConfig
"""
def __init__(self, config: ChineseCLIPConfig):
super().__ini... | class_definition | 48,185 | 51,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,632 |
class ChineseCLIPVisionTransformer(nn.Module):
def __init__(self, config: ChineseCLIPVisionConfig):
super().__init__()
self.config = config
embed_dim = config.hidden_size
self.embeddings = ChineseCLIPVisionEmbeddings(config)
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=co... | class_definition | 51,587 | 54,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,633 |
class ChineseCLIPTextModel(ChineseCLIPPreTrainedModel):
"""
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 | 54,141 | 63,425 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,634 |
class ChineseCLIPVisionModel(ChineseCLIPPreTrainedModel):
config_class = ChineseCLIPVisionConfig
main_input_name = "pixel_values"
_no_split_modules = ["ChineseCLIPVisionEmbeddings", "ChineseCLIPVisionAttention"]
def __init__(self, config: ChineseCLIPVisionConfig):
super().__init__(config)
... | class_definition | 63,572 | 65,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,635 |
class ChineseCLIPModel(ChineseCLIPPreTrainedModel):
config_class = ChineseCLIPConfig
def __init__(self, config: ChineseCLIPConfig):
super().__init__(config)
if not isinstance(config.text_config, ChineseCLIPTextConfig):
raise TypeError(
"config.text_config is expecte... | class_definition | 65,909 | 76,353 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/modeling_chinese_clip.py | null | 7,636 |
class ChineseCLIPImageProcessor(BaseImageProcessor):
r"""
Constructs a Chinese-CLIP image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` i... | class_definition | 1,457 | 15,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/image_processing_chinese_clip.py | null | 7,637 |
class ChineseCLIPTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used to instantiate a
Chinese CLIP model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults ... | class_definition | 1,045 | 7,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/configuration_chinese_clip.py | null | 7,638 |
class ChineseCLIPVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used to instantiate an
ChineseCLIP model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the default... | class_definition | 7,022 | 11,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/configuration_chinese_clip.py | null | 7,639 |
class ChineseCLIPConfig(PretrainedConfig):
r"""
[`ChineseCLIPConfig`] is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used
to instantiate Chinese-CLIP model according to the specified arguments, defining the text model and vision model
configs. Instantiating a conf... | class_definition | 11,355 | 19,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/configuration_chinese_clip.py | null | 7,640 |
class ChineseCLIPOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
... | class_definition | 19,208 | 20,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/chinese_clip/configuration_chinese_clip.py | null | 7,641 |
class VivitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VivitModel`]. It is used to instantiate a ViViT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar co... | class_definition | 782 | 5,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/configuration_vivit.py | null | 7,642 |
class VivitImageProcessor(BaseImageProcessor):
r"""
Constructs a Vivit image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter ... | class_definition | 1,893 | 19,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/image_processing_vivit.py | null | 7,643 |
class VivitTubeletEmbeddings(nn.Module):
"""
Construct Vivit Tubelet embeddings.
This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of
shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder.
The seq_len (the nu... | class_definition | 1,430 | 3,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,644 |
class VivitEmbeddings(nn.Module):
"""
Vivit Embeddings.
Creates embeddings from a video using VivitTubeletEmbeddings, adds CLS token and positional embeddings.
"""
def __init__(self, config):
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
... | class_definition | 3,364 | 6,652 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,645 |
class VivitSelfAttention(nn.Module):
def __init__(self, config: VivitConfig) -> 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 | 6,739 | 9,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,646 |
class VivitSdpaSelfAttention(VivitSelfAttention):
def __init__(self, config: VivitConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentio... | class_definition | 9,675 | 11,637 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,647 |
class VivitSelfOutput(nn.Module):
"""
The residual connection is defined in VivitLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: VivitConfig) -> None:
super().__init__()
self.dense = nn.Linear(c... | class_definition | 11,721 | 12,370 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,648 |
class VivitAttention(nn.Module):
def __init__(self, config: VivitConfig) -> None:
super().__init__()
self.attention = VivitSelfAttention(config)
self.output = VivitSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(heads) ... | class_definition | 12,453 | 14,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,649 |
class VivitSdpaAttention(VivitAttention):
def __init__(self, config: VivitConfig) -> None:
super().__init__(config)
self.attention = VivitSdpaSelfAttention(config) | class_definition | 14,225 | 14,408 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,650 |
class VivitIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ... | class_definition | 14,411 | 15,062 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,651 |
class VivitOutput(nn.Module):
def __init__(self, config):
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, input_tensor):
hidden_states = self.dense(hidd... | class_definition | 15,065 | 15,532 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,652 |
class VivitLayer(nn.Module):
"""This corresponds to the EncoderBlock class in the scenic/vivit implementation."""
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VIVIT_ATTENTION_CL... | class_definition | 15,628 | 17,216 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,653 |
class VivitEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([VivitLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states,
... | class_definition | 17,219 | 19,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,654 |
class VivitPooler(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):
# We "pool" the model by simply taking the hidden state corresponding
... | class_definition | 19,031 | 19,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,655 |
class VivitPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VivitConfig
base_model_prefix = "vivit"
main_input_name = "pixel_values"
supports_gradient_checkpo... | class_definition | 19,564 | 20,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,656 |
class VivitModel(VivitPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = VivitEmbeddings(config)
self.encoder = VivitEncoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=conf... | class_definition | 22,958 | 29,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,657 |
class VivitForVideoClassification(VivitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.vivit = VivitModel(config, add_pooling_layer=False)
# Classifier head
self.classifier = nn.Linear(config.hidden_size, confi... | class_definition | 29,565 | 35,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vivit/modeling_vivit.py | null | 7,658 |
class UperNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to
instantiate an UperNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaul... | class_definition | 906 | 6,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/configuration_upernet.py | null | 7,659 |
class UperNetConvModule(nn.Module):
"""
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
"""
def __init__(
self,
in_ch... | class_definition | 1,237 | 2,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,660 |
class UperNetPyramidPoolingBlock(nn.Module):
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
super().__init__()
self.layers = [
nn.AdaptiveAvgPool2d(pool_scale),
UperNetConvModule(in_channels, channels, kernel_size=1),
]
for i, la... | class_definition | 2,353 | 2,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,661 |
class UperNetPyramidPoolingModule(nn.Module):
"""
Pyramid Pooling Module (PPM) used in PSPNet.
Args:
pool_scales (`Tuple[int]`):
Pooling scales used in Pooling Pyramid Module.
in_channels (`int`):
Input channels.
channels (`int`):
Channels after m... | class_definition | 2,948 | 4,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,662 |
class UperNetHead(nn.Module):
"""
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
[UPerNet](https://arxiv.org/abs/1807.10221).
"""
def __init__(self, config, in_channels):
super().__init__()
self.config = config
self.pool_scales = conf... | class_definition | 4,343 | 7,743 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,663 |
class UperNetFCNHead(nn.Module):
"""
Fully Convolution Networks for Semantic Segmentation. This head is the implementation of
[FCNNet](https://arxiv.org/abs/1411.4038>).
Args:
config:
Configuration.
in_channels (int):
Number of input channels.
kernel_size... | class_definition | 7,746 | 10,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,664 |
class UperNetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = UperNetConfig
main_input_name = "pixel_values"
_no_split_modules = []
def _init_weights(self, m... | class_definition | 10,332 | 11,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,665 |
class UperNetForSemanticSegmentation(UperNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.backbone = load_backbone(config)
# Semantic segmentation head(s)
self.decode_head = UperNetHead(config, in_channels=self.backbone.channels)
self.auxiliary... | class_definition | 12,831 | 17,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/upernet/modeling_upernet.py | null | 7,666 |
class TrOCRConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TrOCRForCausalLM`]. It is used to instantiate an
TrOCR model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 782 | 6,521 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/configuration_trocr.py | null | 7,667 |
class TrOCRProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {} | class_definition | 959 | 1,036 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/processing_trocr.py | null | 7,668 |
class TrOCRProcessor(ProcessorMixin):
r"""
Constructs a TrOCR processor which wraps a vision image processor and a TrOCR tokenizer into a single processor.
[`TrOCRProcessor`] offers all the functionalities of [`ViTImageProcessor`/`DeiTImageProcessor`] and
[`RobertaTokenizer`/`XLMRobertaTokenizer`]. See... | class_definition | 1,039 | 6,322 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/processing_trocr.py | null | 7,669 |
class TrOCRLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# TrOCR is set up so that if padding_idx is specified then offset the embedding ids by 2
# and a... | class_definition | 1,534 | 2,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,670 |
class TrOCRScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_... | class_definition | 2,537 | 3,023 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,671 |
class TrOCRSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embeddin... | class_definition | 3,026 | 6,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,672 |
class TrOCRAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper."""
def __init__(
self,
config,
embed_dim: int,
num_heads: int,
kdim: int = None,
vdim: int = None,
dropout: float = 0.0,
is_decoder: bool = False,
... | class_definition | 6,053 | 13,011 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,673 |
class TrOCRDecoderLayer(nn.Module):
def __init__(self, config: TrOCRConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = TrOCRAttention(
config,
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dr... | class_definition | 13,014 | 19,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,674 |
class TrOCRPreTrainedModel(PreTrainedModel):
config_class = TrOCRConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["TrOCRDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1... | class_definition | 19,006 | 19,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,675 |
class TrOCRDecoder(TrOCRPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TrOCRDecoderLayer`]
Args:
config: TrOCRConfig
"""
def __init__(self, config: TrOCRConfig):
super().__init__(config)
self.dropout = config.dropou... | class_definition | 20,563 | 33,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,676 |
class TrOCRDecoderWrapper(TrOCRPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | class_definition | 33,491 | 33,935 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,677 |
class TrOCRForCausalLM(TrOCRPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["output_projection.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = Tr... | class_definition | 34,142 | 44,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/trocr/modeling_trocr.py | null | 7,678 |
class UniSpeechConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`UniSpeechModel`]. It is used to instantiate an
UniSpeech model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | class_definition | 844 | 17,453 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/configuration_unispeech.py | null | 7,679 |
class UniSpeechForPreTrainingOutput(ModelOutput):
"""
Output type of [`UniSpeechForPreTrainingOutput`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the contrasti... | class_definition | 2,079 | 4,328 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,680 |
class UniSpeechNoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 9,660 | 10,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,681 |
class UniSpeechLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 10,506 | 11,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,682 |
class UniSpeechGroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,
... | class_definition | 11,602 | 12,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,683 |
class UniSpeechPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddings // 2,
... | class_definition | 12,621 | 14,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,684 |
class UniSpeechSamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:, :, : -sel... | class_definition | 14,526 | 14,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,685 |
class UniSpeechFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [UniSpeechGroupNormConvLayer(config, layer_id=0)] + [
UniSpeechNoLayer... | class_definition | 15,004 | 16,758 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,686 |
class UniSpeechFeatureExtractor(UniSpeechFeatureEncoder):
def __init__(self, config):
super().__init__(config)
warnings.warn(
f"The class `{self.__class__.__name__}` has been depreciated "
"and will be removed in Transformers v5. "
f"Use `{self.__class__.__bases__... | class_definition | 16,761 | 17,143 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,687 |
class UniSpeechFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.feat_proj_d... | class_definition | 17,258 | 17,911 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,688 |
class UniSpeechAttention(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 | 18,002 | 25,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,689 |
class UniSpeechFlashAttention2(UniSpeechAttention):
"""
UniSpeech flash attention module. This module inherits from `UniSpeechAttention` 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 ... | class_definition | 25,499 | 31,965 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,690 |
class UniSpeechSdpaAttention(UniSpeechAttention):
# Copied from transformers.models.bart.modeling_bart.BartSdpaAttention.forward with Bart->UniSpeech
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[to... | class_definition | 31,968 | 37,869 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,691 |
class UniSpeechFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
... | class_definition | 38,134 | 39,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,692 |
class UniSpeechEncoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = UNISPEECH_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.attention_dropout,
... | class_definition | 39,236 | 40,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,693 |
class UniSpeechAttnAdapterLayer(nn.Module):
def __init__(self, config):
"""
Implements adapter modules directly with 3D tensor weight as parameters and without using ModuleList to speed
up training throughput.
"""
super().__init__()
self.input_dim = config.adapter_att... | class_definition | 40,712 | 41,594 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,694 |
class UniSpeechEncoderLayerStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = UNISPEECH_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.hidden_size,
num_heads=config.num_attention_heads,
dropout=config.atte... | class_definition | 41,740 | 43,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,695 |
class UniSpeechEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = UniSpeechPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config... | class_definition | 43,577 | 47,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,696 |
class UniSpeechEncoderStableLayerNorm(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.pos_conv_embed = UniSpeechPositionalConvEmbedding(config)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn... | class_definition | 47,536 | 51,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,697 |
class UniSpeechGumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
def __init__(self, config):
super().__init__()
self.num_groups = co... | class_definition | 51,547 | 54,628 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,698 |
class UniSpeechPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = UniSpeechConfig
base_model_prefix = "unispeech"
main_input_name = "input_values"
supports_grad... | class_definition | 54,631 | 58,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/unispeech/modeling_unispeech.py | null | 7,699 |
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