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 PatchTSTForPretrainingOutput(ModelOutput):
"""
Output type of [`PatchTSTForPretraining`].
Parameters:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
MSE loss.
prediction_outputs (`torch.FloatTensor` of shape `(batch_size, sequen... | class_definition | 37,707 | 39,226 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,300 |
class PatchTSTForRegressionOutput(ModelOutput):
"""
Output type of [`PatchTSTForRegression`].
Parameters:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
MSE loss.
regression_outputs (`torch.FloatTensor` of shape `(batch_size, num_targ... | class_definition | 39,240 | 40,735 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,301 |
class PatchTSTForPredictionOutput(ModelOutput):
"""
Output type of [`PatchTSTForPrediction`].
Parameters:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
MSE loss.
prediction_outputs (`torch.FloatTensor` of shape `(batch_size, predicti... | class_definition | 40,749 | 42,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,302 |
class PatchTSTForClassificationOutput(ModelOutput):
"""
Output type of [`PatchTSTForClassification`].
Parameters:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence ... | class_definition | 42,723 | 44,360 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,303 |
class SamplePatchTSTOutput(ModelOutput):
"""
Base class for time series model's predictions outputs that contains the sampled values from the chosen
distribution.
Parameters:
sequences `(batch_size, num_samples, prediction_length, num_targets)`):
Sampled values from the chosen d... | class_definition | 44,374 | 44,755 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,304 |
class PatchTSTStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
... | class_definition | 46,485 | 48,223 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,305 |
class PatchTSTMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "... | class_definition | 48,396 | 50,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,306 |
class PatchTSTNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling_dim") e... | class_definition | 50,967 | 52,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,307 |
class PatchTSTScaler(nn.Module):
def __init__(self, config: PatchTSTConfig):
super().__init__()
if config.scaling == "mean" or config.scaling is True:
self.scaler = PatchTSTMeanScaler(config)
elif config.scaling == "std":
self.scaler = PatchTSTStdScaler(config)
... | class_definition | 52,169 | 53,377 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,308 |
class PatchTSTModel(PatchTSTPreTrainedModel):
def __init__(self, config: PatchTSTConfig):
super().__init__(config)
self.scaler = PatchTSTScaler(config)
self.patchifier = PatchTSTPatchify(config)
self.do_mask_input = config.do_mask_input
# get num_patches information from Pat... | class_definition | 53,522 | 58,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,309 |
class PatchTSTMaskPretrainHead(nn.Module):
"""
Pretraining head for mask modelling
"""
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity()
self.linear = nn.Linear(config.d_mod... | class_definition | 58,353 | 59,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,310 |
class PatchTSTForPretraining(PatchTSTPreTrainedModel):
def __init__(self, config: PatchTSTConfig):
super().__init__(config)
config.do_mask_input = True
self.model = PatchTSTModel(config=config)
self.head = PatchTSTMaskPretrainHead(config)
# Initialize weights and apply fina... | class_definition | 59,642 | 64,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,311 |
class PatchTSTClassificationHead(nn.Module):
def __init__(self, config: PatchTSTConfig):
super().__init__()
self.use_cls_token = config.use_cls_token
self.pooling_type = config.pooling_type
self.flatten = nn.Flatten(start_dim=1)
self.dropout = nn.Dropout(config.head_dropout) ... | class_definition | 64,210 | 65,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,312 |
class PatchTSTForClassification(PatchTSTPreTrainedModel):
def __init__(self, config: PatchTSTConfig):
super().__init__(config)
# Turn off masking
if config.do_mask_input:
logger.warning("Setting `do_mask_input` parameter to False.")
config.do_mask_input = False
... | class_definition | 66,014 | 69,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,313 |
class PatchTSTPredictionHead(nn.Module):
def __init__(self, config: PatchTSTConfig, num_patches, distribution_output=None):
super().__init__()
self.share_projection = config.share_projection
self.num_input_channels = config.num_input_channels
self.use_cls_token = config.use_cls_toke... | class_definition | 69,956 | 74,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,314 |
class PatchTSTForPrediction(PatchTSTPreTrainedModel):
def __init__(self, config: PatchTSTConfig):
super().__init__(config)
# Turn off masking
if config.do_mask_input:
logger.warning("Setting `do_mask_input` parameter to False.")
config.do_mask_input = False
... | class_definition | 74,502 | 82,427 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,315 |
class PatchTSTRegressionHead(nn.Module):
"""
Regression head
"""
def __init__(self, config: PatchTSTConfig, distribution_output=None):
super().__init__()
self.y_range = config.output_range
self.use_cls_token = config.use_cls_token
self.pooling_type = config.pooling_type
... | class_definition | 82,430 | 84,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,316 |
class PatchTSTForRegression(PatchTSTPreTrainedModel):
def __init__(self, config: PatchTSTConfig):
super().__init__(config)
# Turn off masking
if config.do_mask_input:
logger.warning("Setting `do_mask_input` parameter to False.")
config.do_mask_input = False
... | class_definition | 84,914 | 91,724 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtst/modeling_patchtst.py | null | 7,317 |
class BlenderbotLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size,... | class_definition | 2,422 | 3,085 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,318 |
class BlenderbotScaledWordEmbedding(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, embed... | class_definition | 3,187 | 3,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,319 |
class BlenderbotAttention(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 | 3,770 | 11,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,320 |
class BlenderbotEncoderLayer(nn.Module):
def __init__(self, config: BlenderbotConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_at... | class_definition | 11,354 | 14,504 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,321 |
class BlenderbotDecoderLayer(nn.Module):
def __init__(self, config: BlenderbotConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BLENDERBOT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_at... | class_definition | 14,622 | 20,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,322 |
class BlenderbotPreTrainedModel(PreTrainedModel):
config_class = BlenderbotConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=... | class_definition | 20,521 | 21,535 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,323 |
class BlenderbotEncoder(BlenderbotPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BlenderbotEncoderLayer`].
Args:
config: BlenderbotConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(sel... | class_definition | 29,912 | 37,692 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,324 |
class BlenderbotDecoder(BlenderbotPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotDecoderLayer`]
Args:
config: BlenderbotConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: Blenderbo... | class_definition | 37,695 | 50,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,325 |
class BlenderbotModel(BlenderbotPreTrainedModel):
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight"]
def __init__(self, config: BlenderbotConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = ma... | class_definition | 50,329 | 56,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,326 |
class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: Blen... | class_definition | 56,843 | 63,851 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,327 |
class BlenderbotDecoderWrapper(BlenderbotPreTrainedModel):
"""
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__(con... | class_definition | 63,948 | 64,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,328 |
class BlenderbotForCausalLM(BlenderbotPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = Bl... | class_definition | 64,555 | 73,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_blenderbot.py | null | 7,329 |
class TFBlenderbotLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
def call(
... | class_definition | 4,101 | 4,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,330 |
class TFBlenderbotAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
s... | class_definition | 4,953 | 12,533 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,331 |
class TFBlenderbotEncoderLayer(keras.layers.Layer):
def __init__(self, config: BlenderbotConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_... | class_definition | 12,637 | 16,322 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,332 |
class TFBlenderbotDecoderLayer(keras.layers.Layer):
def __init__(self, config: BlenderbotConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFBlenderbotAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_... | class_definition | 16,426 | 23,234 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,333 |
class TFBlenderbotPreTrainedModel(TFPreTrainedModel):
config_class = BlenderbotConfig
base_model_prefix = "model" | class_definition | 23,237 | 23,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,334 |
class TFBlenderbotEncoder(keras.layers.Layer):
config_class = BlenderbotConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFBlenderbotEncoderLayer`].
Args:
config: BlenderbotConfig
"""
def __init__(self, config: Blenderbot... | class_definition | 32,001 | 39,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,335 |
class TFBlenderbotDecoder(keras.layers.Layer):
config_class = BlenderbotConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFBlenderbotDecoderLayer`]
Args:
config: BlenderbotConfig
embed_tokens: output embedding
"""
def __init__(self,... | class_definition | 39,846 | 51,924 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,336 |
class TFBlenderbotMainLayer(keras.layers.Layer):
config_class = BlenderbotConfig
def __init__(self, config: BlenderbotConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim... | class_definition | 51,947 | 57,023 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,337 |
class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
def __init__(self, config: BlenderbotConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFBlenderbotMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_deco... | class_definition | 57,179 | 61,933 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,338 |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | class_definition | 62,002 | 62,808 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,339 |
class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__... | class_definition | 62,958 | 72,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_tf_blenderbot.py | null | 7,340 |
class BlenderbotConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BlenderbotModel`]. It is used to instantiate an
Blenderbot model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yi... | class_definition | 1,119 | 7,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/configuration_blenderbot.py | null | 7,341 |
class BlenderbotOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
... | class_definition | 7,838 | 18,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/configuration_blenderbot.py | null | 7,342 |
class FlaxBlenderbotAttention(nn.Module):
config: BlenderbotConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // ... | class_definition | 11,759 | 19,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,343 |
class FlaxBlenderbotEncoderLayer(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotAttention(
config=self.config,
embed_dim=self.embed_dim,
num_... | class_definition | 19,272 | 21,571 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,344 |
class FlaxBlenderbotEncoderLayerCollection(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.e... | class_definition | 21,685 | 23,654 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,345 |
class FlaxBlenderbotDecoderLayer(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxBlenderbotAttention(
config=self.config,
embed_dim=self.embed_dim,
num_... | class_definition | 23,762 | 27,332 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,346 |
class FlaxBlenderbotDecoderLayerCollection(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBlenderbotDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.d... | class_definition | 27,446 | 30,181 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,347 |
class FlaxBlenderbotEncoder(nn.Module):
config: BlenderbotConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = s... | class_definition | 30,184 | 32,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,348 |
class FlaxBlenderbotDecoder(nn.Module):
config: BlenderbotConfig
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = s... | class_definition | 32,701 | 35,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,349 |
class FlaxBlenderbotModule(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.shared = nn.Embed(
self.config.vocab_size,
self.config.d_model,
embedding_init=jax.nn.initializers.normal(self.con... | class_definition | 35,695 | 38,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,350 |
class FlaxBlenderbotPreTrainedModel(FlaxPreTrainedModel):
config_class = BlenderbotConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: BlenderbotConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dty... | class_definition | 38,159 | 52,783 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,351 |
class FlaxBlenderbotModel(FlaxBlenderbotPreTrainedModel):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxBlenderbotModule | class_definition | 52,946 | 53,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,352 |
class FlaxBlenderbotForConditionalGenerationModule(nn.Module):
config: BlenderbotConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.model = FlaxBlenderbotModule(config=self.config, dtype=self.dtype)
self.lm_head ... | class_definition | 53,375 | 55,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,353 |
class FlaxBlenderbotForConditionalGeneration(FlaxBlenderbotPreTrainedModel):
module_class = FlaxBlenderbotForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(BLENDERBOT_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions,... | class_definition | 56,125 | 63,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/modeling_flax_blenderbot.py | null | 7,354 |
class BlenderbotTokenizer(PreTrainedTokenizer):
"""
Constructs a Blenderbot 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... | class_definition | 2,524 | 18,201 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/tokenization_blenderbot.py | null | 7,355 |
class BlenderbotTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Blenderbot 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... | class_definition | 1,185 | 12,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blenderbot/tokenization_blenderbot_fast.py | null | 7,356 |
class MusicgenMelodyDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`MusicgenMelodyDecoder`]. It is used to instantiate a
Musicgen Melody decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration w... | class_definition | 853 | 6,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py | null | 7,357 |
class MusicgenMelodyConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MusicgenMelodyModel`]. It is used to instantiate a
Musicgen Melody model according to the specified arguments, defining the text encoder, audio encoder and Musicgen Melody decoder
configs... | class_definition | 6,716 | 11,930 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/configuration_musicgen_melody.py | null | 7,358 |
class MusicgenMelodyOutputWithPast(ModelOutput):
"""
Base class for Musicgen Melody autoregressive outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatT... | class_definition | 2,124 | 4,747 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,359 |
class MusicgenMelodySinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int):
super().__init__()
self.embedding_dim = embedding_dim
self.make_weights(num_positions, emb... | class_definition | 5,842 | 8,047 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,360 |
class MusicgenMelodyAttention(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 | 8,143 | 15,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,361 |
class MusicgenMelodyFlashAttention2(MusicgenMelodyAttention):
"""
MusicgenMelody flash attention module. This module inherits from `MusicgenMelodyAttention` 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 | 15,655 | 22,151 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,362 |
class MusicgenMelodySdpaAttention(MusicgenMelodyAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head... | class_definition | 22,251 | 28,068 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,363 |
class MusicgenMelodyDecoderLayer(nn.Module):
def __init__(self, config: MusicgenMelodyDecoderConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = MUSICGEN_MELODY_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num... | class_definition | 28,248 | 31,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,364 |
class MusicgenMelodyPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MusicgenMelodyDecoderConfig
base_model_prefix = "model"
supports_gradient_checkpointing = Tru... | class_definition | 32,012 | 32,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,365 |
class MusicgenMelodyDecoder(MusicgenMelodyPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MusicgenMelodyDecoderLayer`]
"""
def __init__(self, config: MusicgenMelodyDecoderConfig):
super().__init__(config)
self.dropout = config... | class_definition | 44,905 | 53,602 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,366 |
class MusicgenMelodyModel(MusicgenMelodyPreTrainedModel):
def __init__(self, config: MusicgenMelodyDecoderConfig):
super().__init__(config)
self.decoder = MusicgenMelodyDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self... | class_definition | 53,906 | 56,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,367 |
class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel, GenerationMixin):
def __init__(self, config: MusicgenMelodyDecoderConfig):
super().__init__(config)
self.model = MusicgenMelodyModel(config)
self.num_codebooks = config.num_codebooks
self.lm_heads = nn.ModuleList(
... | class_definition | 56,924 | 79,696 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,368 |
class MusicgenMelodyForConditionalGeneration(PreTrainedModel, GenerationMixin):
config_class = MusicgenMelodyConfig
main_input_name = "input_ids"
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports_sdpa = True
def __init__(
self,
config: MusicgenMelody... | class_definition | 80,274 | 129,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py | null | 7,369 |
class MusicgenMelodyFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a MusicgenMelody feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for mor... | class_definition | 1,173 | 15,226 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py | null | 7,370 |
class MusicgenMelodyProcessor(ProcessorMixin):
r"""
Constructs a MusicGen Melody processor which wraps a Wav2Vec2 feature extractor - for raw audio waveform processing - and a T5 tokenizer into a single processor
class.
[`MusicgenProcessor`] offers all the functionalities of [`MusicgenMelodyFeatureExtr... | class_definition | 828 | 8,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen_melody/processing_musicgen_melody.py | null | 7,371 |
class ClapFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a CLAP feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regard... | class_definition | 1,048 | 18,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/feature_extraction_clap.py | null | 7,372 |
class ClapTextModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
... | class_definition | 5,265 | 7,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,373 |
class ClapAudioModelOutput(ModelOutput):
"""
ClapAudio model output to mimic the output of the original implementation.
Args:
audio_embeds (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
The Audio embeddings obtained by applying the projection layer to the pooler_output.
... | class_definition | 7,034 | 8,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,374 |
class ClapOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for audio-text similarity.
logits_per_audio (`torch.FloatTensor` of shape `(audio_batch_size, text_batch_size)`):
The s... | class_definition | 8,841 | 10,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,375 |
class ClapDropPath(nn.Module):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is a slightly
refactored version of the `SwinDropPath` implementation.
"""
def __init__(self, drop_prob=None):
super().__init__()
self.drop_prob = drop_pr... | class_definition | 10,768 | 11,630 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,376 |
class ClapAudioAFFBlock(nn.Module):
r"""
ATTENTIONAL FEATURE FUSION Block from CLAP, since in CLAP we are always in 2D mode, it is not needed to implement
the 1D version.
"""
def __init__(self, config: ClapAudioConfig):
super().__init__()
channels = config.patch_embeds_hidden_size
... | class_definition | 11,764 | 13,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,377 |
class ClapAudioPatchEmbed(nn.Module):
"""
This module converts the hidden states reshaped as an image to patch embeddings ready to be passed to the
Transformer block.
"""
def __init__(self, config: ClapAudioConfig):
super().__init__()
img_size = (config.spec_size, config.spec_size) ... | class_definition | 13,316 | 17,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,378 |
class ClapAudioSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
... | class_definition | 17,807 | 22,684 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,379 |
class ClapAudioSelfOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
h... | class_definition | 22,776 | 23,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,380 |
class ClapAudioAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
self.self = ClapAudioSelfAttention(config, dim, num_heads, window_size)
self.output = ClapAudioSelfOutput(config, dim)
self.pruned_heads = set()
def prune_heads(self,... | class_definition | 23,309 | 25,002 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,381 |
class ClapAudioIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermed... | class_definition | 25,096 | 25,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,382 |
class ClapAudioOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = se... | class_definition | 25,747 | 26,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,383 |
class ClapAudioLayer(nn.Module):
def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.shift_size = shift_size
self.window_size = config.window_size
... | class_definition | 26,286 | 31,974 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,384 |
class ClapAudioStage(nn.Module):
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
super().__init__()
self.config = config
self.dim = dim
self.blocks = nn.ModuleList(
[
ClapAudioLayer(
config=co... | class_definition | 32,061 | 34,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,385 |
class ClapAudioPatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normaliza... | class_definition | 34,379 | 36,662 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,386 |
class ClapAudioEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_layers = len(config.depths)
self.config = config
self.patch_embed = ClapAudioPatchEmbed(config)
self.enable_fusion = config.enable_fusion
self.patch_stride = self.patch_embed.p... | class_definition | 36,665 | 45,949 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,387 |
class ClapProjectionLayer(nn.Module):
def __init__(self, config: Union[ClapAudioConfig, ClapTextConfig]):
super().__init__()
self.config = config
hidden_size = config.hidden_size
projection_dim = config.projection_dim
self.linear1 = nn.Linear(hidden_size, projection_dim)
... | class_definition | 51,753 | 52,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,388 |
class ClapTextEmbeddings(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(c... | class_definition | 52,562 | 56,741 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,389 |
class ClapTextSelfAttention(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 | 56,835 | 64,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,390 |
class ClapTextSelfOutput(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)
d... | class_definition | 64,256 | 64,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,391 |
class ClapTextAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = CLAP_TEXT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = ClapTextSelf... | class_definition | 65,049 | 67,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,392 |
class ClapTextIntermediate(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.in... | class_definition | 67,257 | 67,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,393 |
class ClapTextOutput(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 | 67,893 | 68,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,394 |
class ClapTextLayer(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 = ClapTextAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = ... | class_definition | 68,591 | 72,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,395 |
class ClapTextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ClapTextLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states... | class_definition | 72,606 | 76,404 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,396 |
class ClapTextPooler(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 h... | class_definition | 76,471 | 77,034 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,397 |
class ClapPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ClapConfig
base_model_prefix = "clap"
supports_gradient_checkpointing = False
def _init_weights(se... | class_definition | 77,037 | 78,421 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,398 |
class ClapAudioModel(ClapPreTrainedModel):
config_class = ClapAudioConfig
main_input_name = "input_features"
def __init__(self, config: ClapAudioConfig):
super().__init__(config)
self.audio_encoder = ClapAudioEncoder(config)
# Initialize weights and apply final processing
se... | class_definition | 78,424 | 80,673 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clap/modeling_clap.py | null | 7,399 |
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