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 DepthAnythingConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DepthAnythingModel`]. It is used to instantiate a DepthAnything
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults... | class_definition | 925 | 7,937 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/configuration_depth_anything.py | null | 8,100 |
class DepthAnythingReassembleLayer(nn.Module):
def __init__(self, config, channels, factor):
super().__init__()
self.projection = nn.Conv2d(in_channels=config.reassemble_hidden_size, out_channels=channels, kernel_size=1)
# up/down sampling depending on factor
if factor > 1:
... | class_definition | 2,721 | 3,626 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,101 |
class DepthAnythingReassembleStage(nn.Module):
"""
This class reassembles the hidden states of the backbone into image-like feature representations at various
resolutions.
This happens in 3 stages:
1. Take the patch embeddings and reshape them to image-like feature representations.
2. Project t... | class_definition | 3,629 | 5,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,102 |
class DepthAnythingPreActResidualLayer(nn.Module):
"""
ResidualConvUnit, pre-activate residual unit.
Args:
config (`[DepthAnythingConfig]`):
Model configuration class defining the model architecture.
"""
def __init__(self, config):
super().__init__()
self.activ... | class_definition | 5,412 | 6,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,103 |
class DepthAnythingFeatureFusionLayer(nn.Module):
"""Feature fusion layer, merges feature maps from different stages.
Args:
config (`[DepthAnythingConfig]`):
Model configuration class defining the model architecture.
"""
def __init__(self, config):
super().__init__()
... | class_definition | 6,593 | 7,972 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,104 |
class DepthAnythingFeatureFusionStage(nn.Module):
# Copied from transformers.models.dpt.modeling_dpt.DPTFeatureFusionStage.__init__ with DPT->DepthAnything
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList()
for _ in range(len(config.neck_hidden_sizes)):
... | class_definition | 7,975 | 9,142 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,105 |
class DepthAnythingPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DepthAnythingConfig
base_model_prefix = "depth_anything"
main_input_name = "pixel_values"
... | class_definition | 9,259 | 10,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,106 |
class DepthAnythingNeck(nn.Module):
"""
DepthAnythingNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as
input and produces another list of tensors as output. For DepthAnything, it includes 2 stages:
* DepthAnythingReassembleStage
* DepthA... | class_definition | 10,223 | 12,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,107 |
class DepthAnythingDepthEstimationHead(nn.Module):
"""
Output head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples
the predictions to the input resolution after the first convolutional layer (details can be found in the DPT paper's
supplementary material... | class_definition | 12,075 | 14,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,108 |
class DepthAnythingForDepthEstimation(DepthAnythingPreTrainedModel):
_no_split_modules = ["DPTViTEmbeddings"]
def __init__(self, config):
super().__init__(config)
self.backbone = load_backbone(config)
self.neck = DepthAnythingNeck(config)
self.head = DepthAnythingDepthEstimatio... | class_definition | 14,529 | 18,497 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/depth_anything/modeling_depth_anything.py | null | 8,109 |
class RobertaTokenizer(PreTrainedTokenizer):
"""
Constructs a RoBERTa tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is... | class_definition | 2,301 | 16,450 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/tokenization_roberta.py | null | 8,110 |
class TFRobertaEmbeddings(keras.layers.Layer):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.padding_idx = 1
self.config = config
self.hidden_size = config.hidde... | class_definition | 2,011 | 6,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,111 |
class TFRobertaPooler(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
... | class_definition | 6,299 | 7,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,112 |
class TFRobertaSelfAttention(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the ... | class_definition | 7,372 | 14,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,113 |
class TFRobertaSelfOutput(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Lay... | class_definition | 14,293 | 15,626 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,114 |
class TFRobertaAttention(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFRobertaSelfAttention(config, name="self")
self.dense_output = TFRobertaSelfOutput(config, name="output")
def prune_heads(self, heads):
... | class_definition | 15,720 | 17,564 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,115 |
class TFRobertaIntermediate(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 17,661 | 18,689 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,116 |
class TFRobertaOutput(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNo... | class_definition | 18,780 | 20,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,117 |
class TFRobertaLayer(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFRobertaAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
i... | class_definition | 20,205 | 24,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,118 |
class TFRobertaEncoder(keras.layers.Layer):
def __init__(self, config: RobertaConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFRobertaLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_sta... | class_definition | 25,044 | 28,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,119 |
class TFRobertaMainLayer(keras.layers.Layer):
config_class = RobertaConfig
def __init__(self, config, add_pooling_layer=True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.num_hidden_layers = config.num_hidden_layers
... | class_definition | 28,157 | 38,631 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,120 |
class TFRobertaPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RobertaConfig
base_model_prefix = "roberta" | class_definition | 38,634 | 38,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,121 |
class TFRobertaModel(TFRobertaPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name="roberta")
@unpack_inputs
@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_s... | class_definition | 44,781 | 48,643 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,122 |
class TFRobertaLMHead(keras.layers.Layer):
"""Roberta Head for masked language modeling."""
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.dense = keras.layers.Dense(
... | class_definition | 48,646 | 51,064 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,123 |
class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]
def __init__(self, config, *inputs... | class_definition | 51,175 | 54,850 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,124 |
class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head.decoder.weight"]
def __init__(self, config: Roberta... | class_definition | 54,853 | 61,461 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,125 |
class TFRobertaClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.init... | class_definition | 61,464 | 63,018 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,126 |
class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
def __init__(self, config, *inputs,... | class_definition | 63,246 | 66,692 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,127 |
class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"lm_head"]
_keys_to_ignore_on_load_missing = [r"dropout"]
def __init_... | class_definition | 66,929 | 71,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,128 |
class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
_keys_to_ignore_on_load_missing = [r"dropo... | class_definition | 71,368 | 75,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,129 |
class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"pooler", r"lm_head"]
def __init__(self, config, *inputs, **kwargs)... | class_definition | 75,440 | 79,874 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_tf_roberta.py | null | 8,130 |
class FlaxRobertaEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.word_embeddings = nn.Embed(
self.config.vocab_size,
... | class_definition | 5,972 | 7,799 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,131 |
class FlaxRobertaSelfAttention(nn.Module):
config: RobertaConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.head_dim = self.config.hidden_size // self.config.num_attention_heads
if self.config.hidden_size % self.config.num_a... | class_definition | 7,901 | 15,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,132 |
class FlaxRobertaSelfOutput(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dt... | class_definition | 15,897 | 16,717 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,133 |
class FlaxRobertaAttention(nn.Module):
config: RobertaConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = FlaxRobertaSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
self.output = FlaxRobertaSelfOutput(self.config, dtype=self.dtype)
... | class_definition | 16,815 | 18,231 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,134 |
class FlaxRobertaIntermediate(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 18,332 | 18,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,135 |
class FlaxRobertaOutput(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=... | class_definition | 19,011 | 19,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,136 |
class FlaxRobertaLayer(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxRobertaAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
self.intermediate = FlaxRobertaIntermediate(self.confi... | class_definition | 19,929 | 22,087 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,137 |
class FlaxRobertaLayerCollection(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxRobertaCheckpointLayer = remat(FlaxRobertaLayer, static_argnums... | class_definition | 22,191 | 25,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,138 |
class FlaxRobertaEncoder(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = FlaxRobertaLayerCollection(
self.config,
dtype=self.dtype,
gradient_ch... | class_definition | 25,313 | 26,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,139 |
class FlaxRobertaPooler(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=... | class_definition | 26,654 | 27,179 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,140 |
class FlaxRobertaLMHead(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.ini... | class_definition | 27,182 | 28,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,141 |
class FlaxRobertaClassificationHead(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 28,508 | 29,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,142 |
class FlaxRobertaPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RobertaConfig
base_model_prefix = "roberta"
module_class: nn.Module = None
def __init_... | class_definition | 29,759 | 37,722 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,143 |
class FlaxRobertaModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
gradient_checkpointing: bool = False
def setup(self):
self.embeddings = FlaxRobertaEmbeddings(self.config, dtype=self.dtype)
self.e... | class_definition | 37,817 | 40,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,144 |
class FlaxRobertaModel(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaModule | class_definition | 40,714 | 40,802 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,145 |
class FlaxRobertaForMaskedLMModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta = FlaxRobertaModule(
config=self.config,
add_pooling_layer=False,
dtype=self.dtype,
... | class_definition | 40,924 | 42,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,146 |
class FlaxRobertaForMaskedLM(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaForMaskedLMModule | class_definition | 42,768 | 42,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,147 |
class FlaxRobertaForSequenceClassificationModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta = FlaxRobertaModule(
config=self.config,
dtype=self.dtype,
add_pooling_layer=F... | class_definition | 43,039 | 44,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,148 |
class FlaxRobertaForSequenceClassification(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaForSequenceClassificationModule | class_definition | 44,795 | 44,928 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,149 |
class FlaxRobertaForMultipleChoiceModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta = FlaxRobertaModule(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=self... | class_definition | 45,226 | 47,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,150 |
class FlaxRobertaForMultipleChoice(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaForMultipleChoiceModule | class_definition | 47,592 | 47,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,151 |
class FlaxRobertaForTokenClassificationModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta = FlaxRobertaModule(
config=self.config,
dtype=self.dtype,
add_pooling_layer=Fals... | class_definition | 48,143 | 49,946 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,152 |
class FlaxRobertaForTokenClassification(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaForTokenClassificationModule | class_definition | 50,181 | 50,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,153 |
class FlaxRobertaForQuestionAnsweringModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta = FlaxRobertaModule(
config=self.config,
dtype=self.dtype,
add_pooling_layer=False,... | class_definition | 50,603 | 52,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,154 |
class FlaxRobertaForQuestionAnswering(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaForQuestionAnsweringModule | class_definition | 52,616 | 52,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,155 |
class FlaxRobertaForCausalLMModule(nn.Module):
config: RobertaConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta = FlaxRobertaModule(
config=self.config,
add_pooling_layer=False,
dtype=self.dtype,
... | class_definition | 52,897 | 55,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,156 |
class FlaxRobertaForCausalLM(FlaxRobertaPreTrainedModel):
module_class = FlaxRobertaForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_v... | class_definition | 55,287 | 56,824 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | null | 8,157 |
class RobertaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RobertaModel`] or a [`TFRobertaModel`]. It is
used to instantiate a RoBERTa model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the de... | class_definition | 956 | 6,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/configuration_roberta.py | null | 8,158 |
class RobertaOnnxConfig(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 | 6,811 | 7,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/configuration_roberta.py | null | 8,159 |
class RobertaEmbeddings(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(co... | class_definition | 2,012 | 6,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,160 |
class RobertaSelfAttention(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_size}... | class_definition | 6,285 | 13,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,161 |
class RobertaSdpaSelfAttention(RobertaSelfAttention):
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.parse(get_to... | class_definition | 13,730 | 19,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,162 |
class RobertaSelfOutput(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)
de... | class_definition | 19,426 | 20,035 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,163 |
class RobertaAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = ROBERTA_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = RobertaSelfOutp... | class_definition | 20,250 | 22,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,164 |
class RobertaIntermediate(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.int... | class_definition | 22,454 | 23,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,165 |
class RobertaOutput(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 | 23,089 | 23,700 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,166 |
class RobertaLayer(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 = RobertaAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = co... | class_definition | 23,785 | 27,707 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,167 |
class RobertaEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([RobertaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: ... | class_definition | 27,794 | 31,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,168 |
class RobertaPooler(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 hi... | class_definition | 31,657 | 32,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,169 |
class RobertaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RobertaConfig
base_model_prefix = "roberta"
supports_gradient_checkpointing = True
_no_split_mod... | class_definition | 32,222 | 33,550 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,170 |
class RobertaModel(RobertaPreTrainedModel):
"""
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://arxiv.org/... | class_definition | 37,515 | 48,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,171 |
class RobertaForCausalLM(RobertaPreTrainedModel, 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` as a sta... | class_definition | 48,394 | 55,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,172 |
class RobertaForMaskedLM(RobertaPreTrainedModel):
_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` make sure `c... | class_definition | 55,171 | 59,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,173 |
class RobertaLMHead(nn.Module):
"""Roberta 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)
self.deco... | class_definition | 59,064 | 60,126 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,174 |
class RobertaForSequenceClassification(RobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.roberta = RobertaModel(config, add_pooling_layer=False)
self.classifier = RobertaClassificationH... | class_definition | 60,354 | 64,356 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,175 |
class RobertaForMultipleChoice(RobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.roberta = RobertaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and... | class_definition | 64,593 | 68,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,176 |
class RobertaForTokenClassification(RobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.c... | class_definition | 68,541 | 71,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,177 |
class RobertaClassificationHead(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.classifier... | class_definition | 71,701 | 72,474 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,178 |
class RobertaForQuestionAnswering(RobertaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = RobertaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
... | class_definition | 72,767 | 77,097 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/modeling_roberta.py | null | 8,179 |
class RobertaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" RoBERTa tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2
tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sente... | class_definition | 1,134 | 10,953 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta/tokenization_roberta_fast.py | null | 8,180 |
class VitDetConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VitDetModel`]. It is used to instantiate an
VitDet model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the default... | class_definition | 883 | 7,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/configuration_vitdet.py | null | 8,181 |
class VitDetEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) to be consumed by a Transformer.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size ... | class_definition | 1,300 | 5,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,182 |
class VitDetAttention(nn.Module):
"""Multi-head Attention block with relative position embeddings."""
def __init__(self, config, input_size=None):
"""
Args:
config (`VitDetConfig`):
Model configuration.
input_size (`Tuple[int]`, *optional*):
... | class_definition | 8,763 | 11,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,183 |
class VitDetDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> tor... | class_definition | 12,643 | 13,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,184 |
class VitDetLayerNorm(nn.Module):
"""
A LayerNorm variant, popularized by Transformers, that performs point-wise mean and variance normalization over the
channel dimension for inputs that have shape (batch_size, channels, height, width).
https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b... | class_definition | 13,126 | 14,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,185 |
class VitDetResBottleneckBlock(nn.Module):
"""
The standard bottleneck residual block without the last activation layer. It contains 3 conv layers with kernels
1x1, 3x3, 1x1.
"""
def __init__(self, config, in_channels, out_channels, bottleneck_channels):
"""
Args:
config... | class_definition | 14,035 | 15,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,186 |
class VitDetMlp(nn.Module):
def __init__(self, config, in_features: int, hidden_features: int) -> None:
super().__init__()
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = ACT2FN[config.hidden_act]
self.fc2 = nn.Linear(hidden_features, in_features)
self.drop = nn... | class_definition | 15,398 | 15,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,187 |
class VitDetLayer(nn.Module):
"""This corresponds to the Block class in the original implementation."""
def __init__(
self, config: VitDetConfig, drop_path_rate: float = 0, window_size: int = 0, use_residual_block: bool = False
) -> None:
super().__init__()
dim = config.hidden_size... | class_definition | 18,595 | 21,409 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,188 |
class VitDetEncoder(nn.Module):
def __init__(self, config: VitDetConfig) -> None:
super().__init__()
self.config = config
depth = config.num_hidden_layers
# stochastic depth decay rule
drop_path_rate = [x.item() for x in torch.linspace(0, config.drop_path_rate, depth)]
... | class_definition | 21,412 | 23,852 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,189 |
class VitDetPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitDetConfig
base_model_prefix = "vitdet"
main_input_name = "pixel_values"
supports_gradient_chec... | class_definition | 24,349 | 26,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,190 |
class VitDetModel(VitDetPreTrainedModel):
def __init__(self, config: VitDetConfig):
super().__init__(config)
self.config = config
self.embeddings = VitDetEmbeddings(config)
self.encoder = VitDetEncoder(config)
# Initialize weights and apply final processing
self.pos... | class_definition | 28,625 | 31,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,191 |
class VitDetBackbone(VitDetPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.embeddings = VitDetEmbeddings(config)
self.encoder = VitDetEncoder(config)
self.num_features = [config.hidden_size for _ in ra... | class_definition | 32,076 | 34,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitdet/modeling_vitdet.py | null | 8,192 |
class TvpProcessor(ProcessorMixin):
r"""
Constructs an TVP processor which wraps a TVP image processor and a Bert tokenizer into a single processor.
[`TvpProcessor`] offers all the functionalities of [`TvpImageProcessor`] and [`BertTokenizerFast`]. See the
[`~TvpProcessor.__call__`] and [`~TvpProcessor... | class_definition | 794 | 6,979 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/processing_tvp.py | null | 8,193 |
class TvpImageProcessor(BaseImageProcessor):
r"""
Constructs a Tvp 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 in t... | class_definition | 2,491 | 22,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/image_processing_tvp.py | null | 8,194 |
class TvpVideoGroundingOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Temporal-Distance IoU loss for video grounding.
logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
Contains start_tim... | class_definition | 1,333 | 2,773 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,195 |
class TvpLoss(nn.Module):
"""
This class computes the losses for `TvpForVideoGrounding`. The process happens in two steps: 1) we compute
hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched
ground-truth / prediction (supervise class and box).
... | class_definition | 2,776 | 5,951 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,196 |
class TvpVisionModel(nn.Module):
def __init__(self, config):
super().__init__()
self.backbone = load_backbone(config)
if config.backbone_config is not None:
in_channels = config.backbone_config.hidden_sizes[-1]
elif hasattr(self.backbone, "config") and hasattr(self.backb... | class_definition | 5,954 | 7,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,197 |
class TvpVisualInputEmbedding(nn.Module):
"""
Takes input of both image and video (multi-frame)
"""
def __init__(self, config):
super().__init__()
# sequence embedding
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.row_po... | class_definition | 7,699 | 13,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,198 |
class TvpTextInputEmbeddings(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.position... | class_definition | 13,371 | 15,131 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/tvp/modeling_tvp.py | null | 8,199 |
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