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 FlaxMultiHeadSelfAttention(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.n_heads = self.config.n_heads
self.dim = self.config.dim
self.dropout = nn.Dropout(rate=self.config.attention_dropout)
... | class_definition | 6,836 | 9,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,300 |
class FlaxFFN(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout = nn.Dropout(rate=self.config.dropout)
self.chunk_size_feed_forward = self.config.chunk_size_feed_forward
self.seq_len_dim = 1
self... | class_definition | 9,772 | 10,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,301 |
class FlaxTransformerBlock(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
assert (
self.config.dim % self.config.n_heads == 0
), f"Hidden size {self.config.dim} not dividable by number of heads {self.config.n_... | class_definition | 10,856 | 12,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,302 |
class FlaxTransformer(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxTransformerBlock(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.n_layers)
]
def __call__(
... | class_definition | 12,462 | 14,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,303 |
class FlaxTransformerEncoder(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layer = FlaxTransformer(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
attention_mask,
outpu... | class_definition | 14,240 | 15,012 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,304 |
class FlaxDistilBertLMDecoder(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))
def ... | class_definition | 15,015 | 15,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,305 |
class FlaxDistilBertPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DistilBertConfig
base_model_prefix = "distilbert"
module_class: nn.Module = None
def... | class_definition | 15,639 | 18,606 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,306 |
class FlaxDistilBertModule(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embeddings = FlaxEmbeddings(self.config, dtype=self.dtype)
self.transformer = FlaxTransformerEncoder(self.config, dtype=self.dtype)
def _... | class_definition | 18,609 | 19,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,307 |
class FlaxDistilBertModel(FlaxDistilBertPreTrainedModel):
module_class = FlaxDistilBertModule | class_definition | 20,071 | 20,168 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,308 |
class FlaxDistilBertForMaskedLMModule(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.distilbert = FlaxDistilBertModule(self.config, dtype=self.dtype)
self.vocab_transform = nn.Dense(
self.config.dim,
... | class_definition | 20,267 | 22,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,309 |
class FlaxDistilBertForMaskedLM(FlaxDistilBertPreTrainedModel):
module_class = FlaxDistilBertForMaskedLMModule | class_definition | 22,864 | 22,978 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,310 |
class FlaxDistilBertForSequenceClassificationModule(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
self.pre_classifier = nn.Dense(
self.config.dim,
dtyp... | class_definition | 23,097 | 25,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,311 |
class FlaxDistilBertForSequenceClassification(FlaxDistilBertPreTrainedModel):
module_class = FlaxDistilBertForSequenceClassificationModule | class_definition | 25,256 | 25,398 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,312 |
class FlaxDistilBertForMultipleChoiceModule(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
self.pre_classifier = nn.Dense(
self.config.dim,
dtype=self.d... | class_definition | 25,560 | 27,677 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,313 |
class FlaxDistilBertForMultipleChoice(FlaxDistilBertPreTrainedModel):
module_class = FlaxDistilBertForMultipleChoiceModule | class_definition | 27,925 | 28,051 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,314 |
class FlaxDistilBertForTokenClassificationModule(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.dropout)
self.classifier = nn... | class_definition | 28,350 | 29,753 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,315 |
class FlaxDistilBertForTokenClassification(FlaxDistilBertPreTrainedModel):
module_class = FlaxDistilBertForTokenClassificationModule | class_definition | 29,999 | 30,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,316 |
class FlaxDistilBertForQuestionAnsweringModule(nn.Module):
config: DistilBertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.distilbert = FlaxDistilBertModule(config=self.config, dtype=self.dtype)
self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)
asser... | class_definition | 30,291 | 32,034 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,317 |
class FlaxDistilBertForQuestionAnswering(FlaxDistilBertPreTrainedModel):
module_class = FlaxDistilBertForQuestionAnsweringModule | class_definition | 32,338 | 32,470 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/modeling_flax_distilbert.py | null | 8,318 |
class DistilBertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" DistilBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclas... | class_definition | 1,015 | 8,036 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/distilbert/tokenization_distilbert_fast.py | null | 8,319 |
class DacFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs an Dac 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 regardi... | class_definition | 967 | 7,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/feature_extraction_dac.py | null | 8,320 |
class DacConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`DacModel`]. It is used to instantiate a
Dac model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configu... | class_definition | 826 | 4,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/configuration_dac.py | null | 8,321 |
class DacOutput(ModelOutput):
"""
Args:
loss (`torch.Tensor`):
Loss from the encoder model, comprising the weighted combination of the commitment and codebook losses.
audio_values (`torch.Tensor` of shape `(batch_size, input_length)`):
Reconstructed audio data.
qu... | class_definition | 1,126 | 2,203 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,322 |
class DacEncoderOutput(ModelOutput):
"""
Args:
loss (`torch.Tensor`):
Loss from the encoder model, comprising the weighted combination of the commitment and codebook losses.
quantized_representation (`torch.Tensor` of shape `(batch_size, dimension, time_steps)`, *optional*):
... | class_definition | 2,217 | 3,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,323 |
class DacDecoderOutput(ModelOutput):
"""
Args:
audio_values (`torch.FloatTensor` of shape `(batch_size, input_length)`, *optional*):
Decoded audio values, obtained using the decoder part of Dac.
"""
audio_values: torch.FloatTensor = None | class_definition | 3,318 | 3,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,324 |
class Snake1d(nn.Module):
"""
A 1-dimensional Snake activation function module.
"""
def __init__(self, hidden_dim):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, hidden_dim, 1))
def forward(self, hidden_states):
shape = hidden_states.shape
hidden_states... | class_definition | 3,596 | 4,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,325 |
class DacVectorQuantize(nn.Module):
"""
Implementation of VQ similar to Karpathy's repo (https://github.com/karpathy/deep-vector-quantization)
Additionally uses following tricks from improved VQGAN
(https://arxiv.org/pdf/2110.04627.pdf):
1. Factorized codes: Perform nearest neighbor lookup in l... | class_definition | 4,169 | 7,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,326 |
class DacResidualUnit(nn.Module):
"""
A residual unit composed of Snake1d and weight-normalized Conv1d layers with dilations.
"""
def __init__(self, dimension: int = 16, dilation: int = 1):
super().__init__()
pad = ((7 - 1) * dilation) // 2
self.snake1 = Snake1d(dimension)
... | class_definition | 7,752 | 9,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,327 |
class DacEncoderBlock(nn.Module):
"""Encoder block used in DAC encoder."""
def __init__(self, config: DacConfig, stride: int = 1, stride_index: int = 1):
super().__init__()
dimension = config.encoder_hidden_size * 2**stride_index
self.res_unit1 = DacResidualUnit(dimension // 2, dilatio... | class_definition | 9,109 | 10,051 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,328 |
class DacDecoderBlock(nn.Module):
"""Decoder block used in DAC decoder."""
def __init__(self, config: DacConfig, stride: int = 1, stride_index: int = 1):
super().__init__()
input_dim = config.decoder_hidden_size // 2**stride_index
output_dim = config.decoder_hidden_size // 2 ** (stride... | class_definition | 10,054 | 11,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,329 |
class DacResidualVectorQuantize(nn.Module):
"""
ResidualVectorQuantize block - Introduced in SoundStream: An end2end neural audio codec (https://arxiv.org/abs/2107.03312)
"""
def __init__(self, config: DacConfig):
super().__init__()
n_codebooks = config.n_codebooks
quantizer_dr... | class_definition | 11,153 | 17,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,330 |
class DacDecoder(nn.Module):
"""DAC Decoder"""
def __init__(self, config: DacConfig):
super().__init__()
input_channel = config.hidden_size
channels = config.decoder_hidden_size
strides = config.upsampling_ratios
# Add first conv layer
self.conv1 = nn.Conv1d(in... | class_definition | 17,751 | 18,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,331 |
class DacEncoder(nn.Module):
"""DAC Encoder"""
def __init__(self, config: DacConfig):
super().__init__()
strides = config.downsampling_ratios
# Create first convolution
self.conv1 = nn.Conv1d(1, config.encoder_hidden_size, kernel_size=7, padding=3)
self.block = []
... | class_definition | 18,908 | 20,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,332 |
class DacPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
"""
config_class = DacConfig
base_model_prefix = "dac"
main_input_name = "input_values"
def _init_weights(self, module):
... | class_definition | 20,035 | 23,085 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,333 |
class DacModel(DacPreTrainedModel):
def __init__(self, config: DacConfig):
super().__init__(config)
self.config = config
self.encoder = DacEncoder(config)
self.decoder = DacDecoder(config)
self.quantizer = DacResidualVectorQuantize(config)
self.bits_per_codebook = ... | class_definition | 24,478 | 30,265 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dac/modeling_dac.py | null | 8,334 |
class EncodecOutput(ModelOutput):
"""
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, nb_chunks, chunk_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_values (`torch.FlaotTensor` of shape `(batch_size, sequence_length)`, *optional*)
... | class_definition | 1,216 | 1,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,335 |
class EncodecEncoderOutput(ModelOutput):
"""
Args:
audio_codes (`torch.LongTensor` of shape `(batch_size, nb_chunks, chunk_length)`, *optional*):
Discret code embeddings computed using `model.encode`.
audio_scales (`torch.Tensor` of shape `(batch_size, nb_chunks)`, *optional*):
... | class_definition | 1,719 | 2,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,336 |
class EncodecDecoderOutput(ModelOutput):
"""
Args:
audio_values (`torch.FloatTensor` of shape `(batch_size, segment_length)`, *optional*):
Decoded audio values, obtained using the decoder part of Encodec.
"""
audio_values: torch.FloatTensor = None | class_definition | 2,257 | 2,542 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,337 |
class EncodecConv1d(nn.Module):
"""Conv1d with asymmetric or causal padding and normalization."""
def __init__(
self, config, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, dilation: int = 1
):
super().__init__()
self.causal = config.use_causal_conv
... | class_definition | 2,545 | 6,689 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,338 |
class EncodecConvTranspose1d(nn.Module):
"""ConvTranspose1d with asymmetric or causal padding and normalization."""
def __init__(self, config, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1):
super().__init__()
self.causal = config.use_causal_conv
self.trim_right... | class_definition | 6,692 | 9,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,339 |
class EncodecLSTM(nn.Module):
"""
LSTM without worrying about the hidden state, nor the layout of the data. Expects input as convolutional layout.
"""
def __init__(self, config, dimension):
super().__init__()
self.lstm = nn.LSTM(dimension, dimension, config.num_lstm_layers)
def for... | class_definition | 9,122 | 9,675 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,340 |
class EncodecResnetBlock(nn.Module):
"""
Residual block from SEANet model as used by EnCodec.
"""
def __init__(self, config: EncodecConfig, dim: int, dilations: List[int]):
super().__init__()
kernel_sizes = (config.residual_kernel_size, 1)
if len(kernel_sizes) != len(dilations):... | class_definition | 9,678 | 10,891 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,341 |
class EncodecEncoder(nn.Module):
"""SEANet encoder as used by EnCodec."""
def __init__(self, config: EncodecConfig):
super().__init__()
model = [EncodecConv1d(config, config.audio_channels, config.num_filters, config.kernel_size)]
scaling = 1
# Downsample to raw audio scale
... | class_definition | 10,894 | 12,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,342 |
class EncodecDecoder(nn.Module):
"""SEANet decoder as used by EnCodec."""
def __init__(self, config: EncodecConfig):
super().__init__()
scaling = int(2 ** len(config.upsampling_ratios))
model = [EncodecConv1d(config, config.hidden_size, scaling * config.num_filters, config.kernel_size)]... | class_definition | 12,143 | 13,486 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,343 |
class EncodecEuclideanCodebook(nn.Module):
"""Codebook with Euclidean distance."""
def __init__(self, config: EncodecConfig):
super().__init__()
embed = torch.zeros(config.codebook_size, config.codebook_dim)
self.codebook_size = config.codebook_size
self.register_buffer("inite... | class_definition | 13,489 | 14,766 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,344 |
class EncodecVectorQuantization(nn.Module):
"""
Vector quantization implementation. Currently supports only euclidean distance.
"""
def __init__(self, config: EncodecConfig):
super().__init__()
self.codebook = EncodecEuclideanCodebook(config)
def encode(self, hidden_states):
... | class_definition | 14,769 | 15,370 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,345 |
class EncodecResidualVectorQuantizer(nn.Module):
"""Residual Vector Quantizer."""
def __init__(self, config: EncodecConfig):
super().__init__()
self.codebook_size = config.codebook_size
self.frame_rate = config.frame_rate
self.num_quantizers = config.num_quantizers
self.... | class_definition | 15,373 | 17,395 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,346 |
class EncodecPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EncodecConfig
base_model_prefix = "encodec"
main_input_name = "input_values"
def _init_weights(... | class_definition | 17,398 | 18,918 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,347 |
class EncodecModel(EncodecPreTrainedModel):
def __init__(self, config: EncodecConfig):
super().__init__(config)
self.config = config
self.encoder = EncodecEncoder(config)
self.decoder = EncodecDecoder(config)
self.quantizer = EncodecResidualVectorQuantizer(config)
... | class_definition | 21,769 | 33,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/modeling_encodec.py | null | 8,348 |
class EncodecConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`EncodecModel`]. It is used to instantiate a
Encodec model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 886 | 8,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/configuration_encodec.py | null | 8,349 |
class EncodecFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs an EnCodec 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... | class_definition | 959 | 9,872 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/encodec/feature_extraction_encodec.py | null | 8,350 |
class BridgeTowerVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the vision configuration of a [`BridgeTowerModel`]. Instantiating a
configuration with the defaults will yield a similar configuration to that of the bridgetower-base
[BridgeTower/bridgetower-base](https://hug... | class_definition | 857 | 3,806 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/configuration_bridgetower.py | null | 8,351 |
class BridgeTowerTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the text configuration of a [`BridgeTowerModel`]. The default values here
are copied from RoBERTa. Instantiating a configuration with the defaults will yield a similar configuration to that
of the bridgetower-ba... | class_definition | 3,809 | 9,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/configuration_bridgetower.py | null | 8,352 |
class BridgeTowerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`BridgeTowerModel`]. It is used to instantiate a
BridgeTower model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 9,385 | 14,789 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/configuration_bridgetower.py | null | 8,353 |
class BridgeTowerProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return_... | class_definition | 923 | 1,457 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/processing_bridgetower.py | null | 8,354 |
class BridgeTowerProcessor(ProcessorMixin):
r"""
Constructs a BridgeTower processor which wraps a Roberta tokenizer and BridgeTower image processor into a single
processor.
[`BridgeTowerProcessor`] offers all the functionalities of [`BridgeTowerImageProcessor`] and
[`RobertaTokenizerFast`]. See the... | class_definition | 1,460 | 4,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/processing_bridgetower.py | null | 8,355 |
class BridgeTowerModelOutput(ModelOutput):
"""
Output type of [`BridgeTowerModel`].
Args:
text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_size)`):
Sequence of hidden-states at the text output of the last layer of the model.
image_features (... | class_definition | 6,005 | 8,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,356 |
class BridgeTowerContrastiveOutput(ModelOutput):
"""
Output type of ['BridgeTowerForContrastiveLearning']
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`:
Image-text contrastive loss.
logits (`torch.FloatTensor` of shape `(batch... | class_definition | 8,014 | 10,336 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,357 |
class BridgeTowerResidualAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attn = nn.MultiheadAttention(config.hidden_size, config.hidden_size // 64)
self.ln_1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.mlp = nn.ModuleDict(
... | class_definition | 10,339 | 12,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,358 |
class BridgeTowerTransformer(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.num_hidden_layers = config.num_hidden_layers
if config.remove_last_layer:
self.resblocks = nn.ModuleList(
[BridgeTowerResidua... | class_definition | 12,107 | 13,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,359 |
class BridgeTowerVisionEmbeddings(nn.Module):
def __init__(self, config: BridgeTowerVisionConfig):
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 | 13,238 | 17,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,360 |
class BridgeTowerVisionTransformer(nn.Module):
def __init__(self, config):
super().__init__()
self.embeddings = BridgeTowerVisionEmbeddings(config)
self.ln_pre = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.transformer = BridgeTowerTransformer(config)
sel... | class_definition | 17,081 | 19,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,361 |
class BridgeTowerLinkTower(nn.Module):
def __init__(self, config):
super().__init__()
self.link_tower_type = config.link_tower_type
self.hidden_size = config.hidden_size
if config.link_tower_type in ["add", "scaled_add", "interpolate"]:
if config.link_tower_type == "scale... | class_definition | 19,509 | 20,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,362 |
class BridgeTowerSelfOutput(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 | 20,943 | 21,556 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,363 |
class BridgeTowerIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self... | class_definition | 21,652 | 22,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,364 |
class BridgeTowerOutput(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 | 22,314 | 22,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,365 |
class BridgeTowerPooler(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 th... | class_definition | 23,019 | 23,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,366 |
class BridgeTowerSelfAttention(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_s... | class_definition | 23,694 | 31,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,367 |
class BridgeTowerAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = BRIDGE_TOWER_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = Bridge... | class_definition | 31,245 | 33,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,368 |
class BridgeTowerBertCrossLayer(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 = BridgeTowerAttention(config)
self.is_decoder = config.is_decoder
self.add_cro... | class_definition | 33,392 | 35,729 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,369 |
class BridgeTowerTextLayer(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 = BridgeTowerAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_at... | class_definition | 35,732 | 39,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,370 |
class BridgeTowerTextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([BridgeTowerTextLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | class_definition | 39,785 | 43,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,371 |
class BridgeTowerTextEmbeddings(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.Embe... | class_definition | 43,707 | 47,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,372 |
class BridgeTowerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BridgeTowerConfig
base_model_prefix = "bridgetower"
supports_gradient_checkpointing = False
... | class_definition | 48,665 | 50,662 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,373 |
class BridgeTowerVisionModel(BridgeTowerPreTrainedModel):
config_class = BridgeTowerVisionConfig
def __init__(self, config):
super().__init__(config)
self.visual = BridgeTowerVisionTransformer(config)
@property
def dtype(self):
return self.visual.embeddings.patch_embedding.weig... | class_definition | 50,665 | 51,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,374 |
class BridgeTowerTextModel(BridgeTowerPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in *Attention is
all you need*_ by As... | class_definition | 51,165 | 60,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,375 |
class BridgeTowerModel(BridgeTowerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
vision_config = config.vision_config
text_config = config.text_config
if config.share_cross_modal_transformer_layers:
self.cross_modal_te... | class_definition | 60,403 | 74,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,376 |
class BridgeTowerPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
se... | class_definition | 74,863 | 75,540 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,377 |
class BridgeTowerMLMHead(nn.Module):
def __init__(self, config, weight=None):
super().__init__()
self.config = config
self.transform = BridgeTowerPredictionHeadTransform(config)
self.decoder = nn.Linear(config.hidden_size, config.text_config.vocab_size, bias=False)
self.bias ... | class_definition | 75,543 | 76,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,378 |
class BridgeTowerITMHead(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.fc = nn.Linear(hidden_size, 2)
def forward(self, x):
itm_score = self.fc(x)
return itm_score | class_definition | 76,142 | 76,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,379 |
class BridgeTowerForMaskedLM(BridgeTowerPreTrainedModel):
_tied_weights_keys = ["mlm_score.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.bridgetower = BridgeTowerModel(config)
self.mlm_score = BridgeTowerMLMHead(config)
# Initialize weights and app... | class_definition | 76,534 | 80,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,380 |
class BridgeTowerForImageAndTextRetrieval(BridgeTowerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bridgetower = BridgeTowerModel(config)
self.itm_score = BridgeTowerITMHead(config.hidden_size * 2)
# Initialize weights and apply final processing
... | class_definition | 80,962 | 84,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,381 |
class BridgeTowerContrastiveHead(nn.Module):
def __init__(self, hidden_size, embed_size):
super().__init__()
self.fc = nn.Linear(hidden_size, embed_size)
def forward(self, x):
x = self.fc(x)
return x | class_definition | 84,716 | 84,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,382 |
class BridgeTowerForContrastiveLearning(BridgeTowerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bridgetower = BridgeTowerModel(config)
self.itc_text_head = BridgeTowerContrastiveHead(config.hidden_size, config.contrastive_hidden_size)
self.itc_image_h... | class_definition | 85,137 | 91,386 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/modeling_bridgetower.py | null | 8,383 |
class BridgeTowerImageProcessor(BaseImageProcessor):
r"""
Constructs a BridgeTower 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... | class_definition | 4,382 | 26,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bridgetower/image_processing_bridgetower.py | null | 8,384 |
class MT5LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the MT5 style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
... | class_definition | 4,113 | 5,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,385 |
class MT5DenseActDense(nn.Module):
def __init__(self, config: MT5Config):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.act = ACT... | class_definition | 5,290 | 6,151 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,386 |
class MT5DenseGatedActDense(nn.Module):
def __init__(self, config: MT5Config):
super().__init__()
self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)... | class_definition | 6,237 | 7,526 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,387 |
class MT5LayerFF(nn.Module):
def __init__(self, config: MT5Config):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = MT5DenseGatedActDense(config)
else:
self.DenseReluDense = MT5DenseActDense(config)
self.layer_norm = MT5LayerNorm(config.d_mod... | class_definition | 7,601 | 8,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,388 |
class MT5Attention(nn.Module):
def __init__(
self,
config: MT5Config,
has_relative_attention_bias=False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_bi... | class_definition | 8,349 | 19,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,389 |
class MT5LayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = MT5Attention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
)
... | class_definition | 19,669 | 21,022 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,390 |
class MT5LayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = MT5Attention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = MT5LayerNorm(config.d_model, eps=config.layer_norm_ep... | class_definition | 21,109 | 22,525 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,391 |
class MT5Block(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(
MT5LayerSelfAttention(config, has_relative_att... | class_definition | 22,598 | 26,739 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,392 |
class MT5ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config: MT5Config):
super().__init__()
self.dense = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(p=config.classifier_dropout)
self.out_proj = nn.... | class_definition | 31,366 | 32,082 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,393 |
class MT5PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MT5Config
load_tf_weights = load_tf_weights_in_mt5
base_model_prefix = "transformer"
is_parallelizab... | class_definition | 32,174 | 38,609 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,394 |
class MT5Stack(MT5PreTrainedModel):
def __init__(self, config, embed_tokens=None):
super().__init__(config)
self.embed_tokens = embed_tokens
self.is_decoder = config.is_decoder
self.block = nn.ModuleList(
[MT5Block(config, has_relative_attention_bias=bool(i == 0), layer... | class_definition | 38,682 | 59,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,395 |
class MT5Model(MT5PreTrainedModel):
r"""
Examples:
```python
>>> from transformers import MT5Model, AutoTokenizer
>>> model = MT5Model.from_pretrained("google/mt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhande... | class_definition | 69,896 | 80,097 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,396 |
class MT5ForConditionalGeneration(MT5PreTrainedModel, GenerationMixin):
r"""
Examples:
```python
>>> from transformers import MT5ForConditionalGeneration, AutoTokenizer
>>> model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("go... | class_definition | 80,200 | 94,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,397 |
class MT5EncoderModel(MT5PreTrainedModel):
r"""
Examples:
```python
>>> from transformers import MT5EncoderModel, AutoTokenizer
>>> model = MT5EncoderModel.from_pretrained("google/mt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt,... | class_definition | 94,944 | 100,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,398 |
class MT5ForSequenceClassification(MT5PreTrainedModel):
_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
# Copied from transformers.models.t5.modeling_t5.T5ForS... | class_definition | 100,615 | 106,909 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mt5/modeling_mt5.py | null | 8,399 |
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