text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
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|---|---|---|---|---|---|---|---|
class ZoeDepthConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ZoeDepthForDepthEstimation`]. It is used to instantiate an ZoeDepth
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults ... | class_definition | 980 | 12,683 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/configuration_zoedepth.py | null | 5,500 |
class ZoeDepthDepthEstimatorOutput(ModelOutput):
"""
Extension of `DepthEstimatorOutput` to include domain logits (ZoeDepth specific).
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1)... | class_definition | 1,347 | 3,177 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,501 |
class ZoeDepthReassembleStage(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. Map the N + 1 tokens to a set of N tokens, by taking into account the readout ([CLS]) token according t... | class_definition | 3,180 | 6,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,502 |
class ZoeDepthReassembleLayer(nn.Module):
def __init__(self, config, channels, factor):
super().__init__()
# projection
hidden_size = config.backbone_hidden_size
self.projection = nn.Conv2d(in_channels=hidden_size, out_channels=channels, kernel_size=1)
# up/down sampling dep... | class_definition | 6,610 | 7,581 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,503 |
class ZoeDepthFeatureFusionStage(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList()
for _ in range(len(config.neck_hidden_sizes)):
self.layers.append(ZoeDepthFeatureFusionLayer(config))
def forward(self, hidden_states):
# reversi... | class_definition | 7,676 | 8,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,504 |
class ZoeDepthPreActResidualLayer(nn.Module):
"""
ResidualConvUnit, pre-activate residual unit.
Args:
config (`[ZoeDepthConfig]`):
Model configuration class defining the model architecture.
"""
# Ignore copy
def __init__(self, config):
super().__init__()
se... | class_definition | 8,668 | 10,589 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,505 |
class ZoeDepthFeatureFusionLayer(nn.Module):
"""Feature fusion layer, merges feature maps from different stages.
Args:
config (`[ZoeDepthConfig]`):
Model configuration class defining the model architecture.
align_corners (`bool`, *optional*, defaults to `True`):
The alig... | class_definition | 10,684 | 12,124 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,506 |
class ZoeDepthNeck(nn.Module):
"""
ZoeDepthNeck. 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 ZoeDepth, it includes 2 stages:
* ZoeDepthReassembleStage
* ZoeDepthFeatureFusionStage... | class_definition | 12,127 | 14,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,507 |
class ZoeDepthRelativeDepthEstimationHead(nn.Module):
"""
Relative depth estimation 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 DPT's paper's
su... | class_definition | 14,343 | 16,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,508 |
class LogBinomialSoftmax(nn.Module):
def __init__(self, n_classes=256, act=torch.softmax):
"""Compute log binomial distribution for n_classes
Args:
n_classes (`int`, *optional*, defaults to 256):
Number of output classes.
act (`torch.nn.Module`, *optional*, d... | class_definition | 16,418 | 18,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,509 |
class ZoeDepthConditionalLogBinomialSoftmax(nn.Module):
def __init__(
self,
config,
in_features,
condition_dim,
n_classes=256,
bottleneck_factor=2,
):
"""Per-pixel MLP followed by a Conditional Log Binomial softmax.
Args:
in_features (... | class_definition | 18,427 | 20,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,510 |
class ZoeDepthSeedBinRegressor(nn.Module):
def __init__(self, config, n_bins=16, mlp_dim=256, min_depth=1e-3, max_depth=10):
"""Bin center regressor network.
Can be "normed" or "unnormed". If "normed", bin centers are bounded on the (min_depth, max_depth) interval.
Args:
config... | class_definition | 20,992 | 23,277 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,511 |
class ZoeDepthAttractorLayer(nn.Module):
def __init__(
self,
config,
n_bins,
n_attractors=16,
min_depth=1e-3,
max_depth=10,
memory_efficient=False,
):
"""
Attractor layer for bin centers. Bin centers are bounded on the interval (min_depth, ... | class_definition | 24,241 | 28,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,512 |
class ZoeDepthAttractorLayerUnnormed(nn.Module):
def __init__(
self,
config,
n_bins,
n_attractors=16,
min_depth=1e-3,
max_depth=10,
memory_efficient=True,
):
"""
Attractor layer for bin centers. Bin centers are unbounded
"""
... | class_definition | 28,457 | 31,735 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,513 |
class ZoeDepthProjector(nn.Module):
def __init__(self, in_features, out_features, mlp_dim=128):
"""Projector MLP.
Args:
in_features (`int`):
Number of input channels.
out_features (`int`):
Number of output channels.
mlp_dim (`int`,... | class_definition | 31,738 | 32,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,514 |
class ZoeDepthMultiheadAttention(nn.Module):
"""Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
# Ignore copy
def __init__(self, hidden_size, num_attention_heads, dropout):
super().__init__()
if hidden_size % num_attention_heads != 0:
raise ValueEr... | class_definition | 32,704 | 35,669 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,515 |
class ZoeDepthTransformerEncoderLayer(nn.Module):
def __init__(self, config, dropout=0.1, activation="relu"):
super().__init__()
hidden_size = config.patch_transformer_hidden_size
intermediate_size = config.patch_transformer_intermediate_size
num_attention_heads = config.patch_trans... | class_definition | 35,672 | 36,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,516 |
class ZoeDepthPatchTransformerEncoder(nn.Module):
def __init__(self, config):
"""ViT-like transformer block
Args:
config (`ZoeDepthConfig`):
Model configuration class defining the model architecture.
"""
super().__init__()
in_channels = config.bo... | class_definition | 36,993 | 39,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,517 |
class ZoeDepthMLPClassifier(nn.Module):
def __init__(self, in_features, out_features) -> None:
super().__init__()
hidden_features = in_features
self.linear1 = nn.Linear(in_features, hidden_features)
self.activation = nn.ReLU()
self.linear2 = nn.Linear(hidden_features, out_fe... | class_definition | 39,470 | 40,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,518 |
class ZoeDepthMultipleMetricDepthEstimationHeads(nn.Module):
"""
Multiple metric depth estimation heads. A MLP classifier is used to route between 2 different heads.
"""
def __init__(self, config):
super().__init__()
bin_embedding_dim = config.bin_embedding_dim
n_attractors = c... | class_definition | 40,022 | 45,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,519 |
class ZoeDepthMetricDepthEstimationHead(nn.Module):
def __init__(self, config):
super().__init__()
bin_configuration = config.bin_configurations[0]
n_bins = bin_configuration["n_bins"]
min_depth = bin_configuration["min_depth"]
max_depth = bin_configuration["max_depth"]
... | class_definition | 45,813 | 49,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,520 |
class ZoeDepthPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ZoeDepthConfig
base_model_prefix = "zoedepth"
main_input_name = "pixel_values"
supports_gradien... | class_definition | 49,810 | 50,755 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,521 |
class ZoeDepthForDepthEstimation(ZoeDepthPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.backbone = load_backbone(config)
if hasattr(self.backbone.config, "hidden_size") and hasattr(self.backbone.config, "patch_size"):
config.backbone_hidden_size = s... | class_definition | 52,349 | 57,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/zoedepth/modeling_zoedepth.py | null | 5,522 |
class SeamlessM4TGenerationOutput(ModelOutput):
"""
Class defining the generated outputs from [`SeamlessM4TModel`], [`SeamlessM4TForTextToText`],
[`SeamlessM4TForTextToSpeech`], [`SeamlessM4TForSpeechToSpeech`] and [`SeamlessM4TForTextToSpeech`].
Args:
waveform (`torch.FloatTensor` of shape `(b... | class_definition | 1,725 | 3,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,523 |
class SeamlessM4TConformerPositionalConvEmbedding(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.hidden_size,
config.hidden_size,
kernel_size=config.num_conv_pos_embeddings,
padding=config.num_conv_pos_embeddin... | class_definition | 15,889 | 17,708 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,524 |
class SeamlessM4TConformerRotaryPositionalEmbedding(nn.Module):
"""Rotary positional embedding
Reference : https://blog.eleuther.ai/rotary-embeddings/ Paper: https://arxiv.org/pdf/2104.09864.pdf
"""
def __init__(self, config):
super().__init__()
dim = config.hidden_size // config.speech... | class_definition | 17,915 | 19,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,525 |
class SeamlessM4TConformerRelPositionalEmbedding(nn.Module):
"""Relative positional encoding module."""
def __init__(self, config):
super().__init__()
self.max_len = config.max_source_positions
self.d_model = config.hidden_size
self.pe = None
self.extend_pe(torch.tensor(... | class_definition | 19,660 | 22,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,526 |
class SeamlessM4TConformerSamePadLayer(nn.Module):
def __init__(self, num_conv_pos_embeddings):
super().__init__()
self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0
def forward(self, hidden_states):
if self.num_pad_remove > 0:
hidden_states = hidden_states[:... | class_definition | 22,202 | 22,579 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,527 |
class SeamlessM4TConformerFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.feature_projection_input_dim, eps=config.layer_norm_eps)
self.projection = nn.Linear(config.feature_projection_input_dim, config.hidden_size)
s... | class_definition | 22,582 | 23,263 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,528 |
class SeamlessM4TConformerFeedForward(nn.Module):
def __init__(self, config, act_fn=None, dropout=None):
super().__init__()
dropout = dropout if dropout is not None else config.speech_encoder_dropout
act_fn = act_fn if act_fn is not None else config.speech_encoder_hidden_act
self.in... | class_definition | 23,266 | 24,347 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,529 |
class SeamlessM4TConformerConvolutionModule(nn.Module):
"""Convolution block used in the conformer block"""
def __init__(self, config):
super().__init__()
if (config.conv_depthwise_kernel_size - 1) % 2 == 1:
raise ValueError("`config.conv_depthwise_kernel_size` should be a odd numbe... | class_definition | 24,350 | 26,766 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,530 |
class SeamlessM4TConformerSelfAttention(nn.Module):
"""Construct a SeamlessM4TConformerSelfAttention object.
Can be enhanced with rotary or relative position embeddings.
"""
def __init__(self, config, use_position_embeddings=True):
super().__init__()
self.head_size = config.hidden_size... | class_definition | 26,769 | 34,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,531 |
class SeamlessM4TConformerEncoderLayer(nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100."""
# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerEncoderLayer.__init__ with Wav2Vec2->SeamlessM4T, attention_dropout->speech_encoder_dropout, torc... | class_definition | 34,382 | 37,118 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,532 |
class SeamlessM4TConformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.position_embeddings_type == "relative":
self.embed_positions = SeamlessM4TConformerRelPositionalEmbedding(config)
elif config.position_embeddings_t... | class_definition | 37,121 | 41,291 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,533 |
class SeamlessM4TConformerAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
dropout = config.adaptor_dropout
self.kernel_size = config.adaptor_kernel_size
self.stride = config.adaptor_stride
# 1. residual convolut... | class_definition | 41,294 | 45,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,534 |
class SeamlessM4TConformerAdapter(nn.Module):
def __init__(self, config):
super().__init__()
self.layers = nn.ModuleList(SeamlessM4TConformerAdapterLayer(config) for _ in range(config.num_adapter_layers))
def forward(self, hidden_states, attention_mask):
# down project hidden_states if... | class_definition | 45,033 | 45,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,535 |
class SeamlessM4TScaledWordEmbedding(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, embe... | class_definition | 45,664 | 46,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,536 |
class SeamlessM4TSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = em... | class_definition | 46,254 | 49,860 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,537 |
class SeamlessM4TAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Copied from transformers.models.bart.modeling_bart.BartAttention.__init__ with Bart->SeamlessM4T
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = ... | class_definition | 49,863 | 56,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,538 |
class SeamlessM4TFeedForwardNetwork(nn.Module):
def __init__(self, config: SeamlessM4TConfig, ffn_dim: int):
super().__init__()
self.fc1 = nn.Linear(config.hidden_size, ffn_dim)
self.fc2 = nn.Linear(ffn_dim, config.hidden_size)
self.dropout = nn.Dropout(config.activation_dropout)
... | class_definition | 57,016 | 57,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,539 |
class SeamlessM4TEncoderLayer(nn.Module):
def __init__(self, config: SeamlessM4TConfig, encoder_ffn_dim=None, encoder_attention_heads=None):
super().__init__()
encoder_ffn_dim = config.encoder_ffn_dim if encoder_ffn_dim is None else encoder_ffn_dim
encoder_attention_heads = (
con... | class_definition | 57,955 | 60,260 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,540 |
class SeamlessM4TDecoderLayer(nn.Module):
def __init__(self, config: SeamlessM4TConfig, decoder_ffn_dim=None, decoder_attention_heads=None):
super().__init__()
decoder_ffn_dim = config.decoder_ffn_dim if decoder_ffn_dim is None else decoder_ffn_dim
decoder_attention_heads = (
con... | class_definition | 60,263 | 65,423 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,541 |
class SeamlessM4TPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SeamlessM4TConfig
base_model_prefix = "seamless_m4t"
supports_gradient_checkpointing = True
... | class_definition | 65,482 | 70,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,542 |
class SeamlessM4TSpeechEncoder(SeamlessM4TPreTrainedModel):
main_input_name = "input_features"
def __init__(self, config: SeamlessM4TConfig):
super().__init__(config)
self.feature_projection = SeamlessM4TConformerFeatureProjection(config)
self.encoder = SeamlessM4TConformerEncoder(conf... | class_definition | 71,085 | 73,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,543 |
class SeamlessM4TEncoder(SeamlessM4TPreTrainedModel):
def __init__(
self,
config: SeamlessM4TConfig,
embed_tokens: Optional[nn.Embedding] = None,
is_t2u_encoder: bool = False,
):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = conf... | class_definition | 74,188 | 81,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,544 |
class SeamlessM4TDecoder(SeamlessM4TPreTrainedModel):
def __init__(
self,
config: SeamlessM4TConfig,
embed_tokens: Optional[nn.Embedding] = None,
):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.paddi... | class_definition | 82,022 | 93,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,545 |
class SeamlessM4TTextToUnitModel(SeamlessM4TPreTrainedModel):
def __init__(
self,
config: SeamlessM4TConfig,
embed_tokens_decoder: Optional[nn.Embedding] = None,
):
super().__init__(config)
self.encoder = SeamlessM4TEncoder(config, is_t2u_encoder=True)
self.decod... | class_definition | 93,845 | 97,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,546 |
class SeamlessM4TTextToUnitForConditionalGeneration(SeamlessM4TPreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_missing = [
"vocoder",
"speech_encoder",
"text_encoder",
"text_decoder",
]
_tied_weights_keys = ["decoder.embed_tokens.weight", "lm_head.weight"]
def... | class_definition | 97,942 | 103,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,547 |
class HifiGanResidualBlock(nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
super().__init__()
self.leaky_relu_slope = leaky_relu_slope
self.convs1 = nn.ModuleList(
[
nn.Conv1d(
channels,
... | class_definition | 104,330 | 106,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,548 |
class SeamlessM4TVariancePredictor(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.unit_embed_dim
kernel_size = config.variance_predictor_kernel_size
var_pred_dropout = config.var_pred_dropout
self.conv1 = nn.Conv1d(
embed_dim,
... | class_definition | 106,462 | 107,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,549 |
class SeamlessM4THifiGan(nn.Module):
def __init__(self, config: SeamlessM4TConfig):
super().__init__()
model_in_dim = config.unit_embed_dim + config.lang_embed_dim + config.spkr_embed_dim
self.leaky_relu_slope = config.leaky_relu_slope
self.num_kernels = len(config.resblock_kernel_si... | class_definition | 107,859 | 111,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,550 |
class SeamlessM4TCodeHifiGan(PreTrainedModel):
config_class = SeamlessM4TConfig
main_input_name = "input_embeds"
_no_split_modules = []
def __init__(self, config):
super().__init__(config)
self.pad_token_id = config.t2u_pad_token_id
self.dur_predictor = SeamlessM4TVariancePredi... | class_definition | 111,432 | 118,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,551 |
class SeamlessM4TForTextToText(SeamlessM4TPreTrainedModel, GenerationMixin):
_keys_to_ignore_on_load_missing = ["speech_encoder", "t2u_model", "vocoder"]
main_input_name = "input_ids"
_tied_weights_keys = [
"lm_head.weight",
"text_encoder.embed_tokens.weight",
"text_decoder.embed_to... | class_definition | 118,535 | 131,655 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,552 |
class SeamlessM4TForSpeechToText(SeamlessM4TPreTrainedModel):
_keys_to_ignore_on_load_missing = ["text_decoder", "t2u_model", "vocoder"]
main_input_name = "input_features"
_tied_weights_keys = [
"lm_head.weight",
"text_decoder.embed_tokens.weight",
]
def __init__(self, config: Seam... | class_definition | 131,801 | 145,269 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,553 |
class SeamlessM4TForTextToSpeech(SeamlessM4TPreTrainedModel):
_keys_to_ignore_on_load_missing = ["speech_encoder"]
main_input_name = "input_ids"
_tied_weights_keys = [
"lm_head.weight",
"text_encoder.embed_tokens.weight",
"text_decoder.embed_tokens.weight",
]
def __init__(s... | class_definition | 145,415 | 160,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,554 |
class SeamlessM4TForSpeechToSpeech(SeamlessM4TPreTrainedModel):
_keys_to_ignore_on_load_missing = ["text_encoder"]
main_input_name = "input_features"
_tied_weights_keys = [
"lm_head.weight",
"text_decoder.embed_tokens.weight",
]
def __init__(self, config):
super().__init__(... | class_definition | 161,100 | 177,188 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,555 |
class SeamlessM4TModel(SeamlessM4TPreTrainedModel):
_tied_weights_keys = [
"lm_head.weight",
"text_encoder.embed_tokens.weight",
"text_decoder.embed_tokens.weight",
]
def __init__(self, config, current_modality="text"):
super().__init__(config)
self.shared = nn.Embe... | class_definition | 177,515 | 198,904 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py | null | 5,556 |
class SeamlessM4TTokenizer(PreTrainedTokenizer):
"""
Construct a SeamlessM4T tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<language code> <tokens> <eos>` for source language docum... | class_definition | 1,201 | 25,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/tokenization_seamless_m4t.py | null | 5,557 |
class SeamlessM4TProcessor(ProcessorMixin):
r"""
Constructs a SeamlessM4T processor which wraps a SeamlessM4T feature extractor and a SeamlessM4T tokenizer into a
single processor.
[`SeamlessM4TProcessor`] offers all the functionalities of [`SeamlessM4TFeatureExtractor`] and
[`SeamlessM4TTokenizerF... | class_definition | 706 | 5,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/processing_seamless_m4t.py | null | 5,558 |
class SeamlessM4TFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a SeamlessM4T feature extractor.
This feature extractor inherits from [`SequenceFeatureExtractor`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.
... | class_definition | 1,101 | 13,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py | null | 5,559 |
class SeamlessM4TTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" SeamlessM4T tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
This tokenizer inherits from [`PreTrainedToken... | class_definition | 1,305 | 19,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/tokenization_seamless_m4t_fast.py | null | 5,560 |
class SeamlessM4TConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~SeamlessM4TModel`]. It is used to instantiate an
SeamlessM4T model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults wil... | class_definition | 788 | 23,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/seamless_m4t/configuration_seamless_m4t.py | null | 5,561 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 3,767 | 10,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/herbert/tokenization_herbert.py | null | 5,562 |
class HerbertTokenizer(PreTrainedTokenizer):
"""
Construct a BPE tokenizer for HerBERT.
Peculiarities:
- uses BERT's pre-tokenizer: BaseTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a
punctuation character will be treated separately.
- Such pretokenized inpu... | class_definition | 10,518 | 25,033 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/herbert/tokenization_herbert.py | null | 5,563 |
class HerbertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library).
Peculiarities:
- uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
a punctuation ch... | class_definition | 1,015 | 5,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/herbert/tokenization_herbert_fast.py | null | 5,564 |
class UMT5Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`UMT5Model`]. It is used to instantiate a UMT5
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 844 | 6,483 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/configuration_umt5.py | null | 5,565 |
class UMT5OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = {
"input_ids": {0: "batch", 1: "encoder_sequence"},
"attention_mask": {0: ... | class_definition | 6,486 | 7,694 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/configuration_umt5.py | null | 5,566 |
class UMT5LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the UMT5 style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
... | class_definition | 1,821 | 2,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,567 |
class UMT5DenseActDense(nn.Module):
def __init__(self, config: UMT5Config):
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 = A... | class_definition | 3,002 | 3,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,568 |
class UMT5DenseGatedActDense(nn.Module):
def __init__(self, config: UMT5Config):
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=Fals... | class_definition | 3,952 | 5,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,569 |
class UMT5LayerFF(nn.Module):
def __init__(self, config: UMT5Config):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = UMT5DenseGatedActDense(config)
else:
self.DenseReluDense = UMT5DenseActDense(config)
self.layer_norm = UMT5LayerNorm(config.... | class_definition | 5,319 | 5,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,570 |
class UMT5Attention(nn.Module):
"""
T5's attention using relative_attention_bias.
"""
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_rel... | class_definition | 5,998 | 16,183 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,571 |
class UMT5LayerSelfAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = UMT5Attention(config, has_relative_attention_bias=True, layer_idx=layer_idx)
self.layer_norm = UMT5LayerNorm(config.d_model, eps=config.layer_norm_eps... | class_definition | 16,186 | 17,263 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,572 |
class UMT5LayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = UMT5Attention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = UMT5LayerNorm(config.d_model, eps=config.layer_norm... | class_definition | 17,266 | 18,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,573 |
class UMT5Block(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(UMT5LayerSelfAttention(config, layer_idx=layer_idx))
if self.is_decoder:
... | class_definition | 18,443 | 21,465 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,574 |
class UMT5ClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config: UMT5Config):
super().__init__()
self.dense = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(p=config.classifier_dropout)
self.out_proj = n... | class_definition | 21,552 | 22,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,575 |
class UMT5PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = UMT5Config
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_supports_cache... | class_definition | 22,273 | 28,654 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,576 |
class UMT5Stack(UMT5PreTrainedModel):
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([UMT5Block(config, layer_idx=i) for i in range(config.num_layers)])
se... | class_definition | 28,657 | 44,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,577 |
class UMT5Model(UMT5PreTrainedModel):
r"""
Examples:
```python
>>> from transformers import UMT5Model, AutoTokenizer
>>> model = UMT5Model.from_pretrained("google/umt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small")
>>> noisy_text = "UN Offizier sagt, dass weiter... | class_definition | 54,456 | 61,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,578 |
class UMT5ForConditionalGeneration(UMT5PreTrainedModel, GenerationMixin):
r"""
Examples:
```python
>>> from transformers import UMT5ForConditionalGeneration, AutoTokenizer
>>> model = UMT5ForConditionalGeneration.from_pretrained("google/umt5-small")
>>> tokenizer = AutoTokenizer.from_pretraine... | class_definition | 62,099 | 71,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,579 |
class UMT5EncoderModel(UMT5PreTrainedModel):
r"""
Examples:
```python
>>> from transformers import UMT5EncoderModel, AutoTokenizer
>>> model = UMT5EncoderModel.from_pretrained("google/umt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/umt5-small")
>>> article = "UN Offizier... | class_definition | 71,893 | 75,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,580 |
class UMT5ForSequenceClassification(UMT5PreTrainedModel):
_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.T5Fo... | class_definition | 76,160 | 82,372 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,581 |
class UMT5ForTokenClassification(UMT5PreTrainedModel):
_keys_to_ignore_on_load_unexpected = ["decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight"]
_tied_weights_keys = ["transformer.encoder.embed_tokens.weight"]
# Copied from transformers.models.t5.modeling_t5.T5ForTokenClassification._... | class_definition | 82,609 | 85,417 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,582 |
class UMT5ForQuestionAnswering(UMT5PreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
self.model_dim = config.d_model
self.shared = nn.Embedding(config.vocab_size, config.d_model)
... | class_definition | 85,702 | 94,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/umt5/modeling_umt5.py | null | 5,583 |
class Cohere2RotaryEmbedding(nn.Module):
def __init__(self, config: Cohere2Config, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type"... | class_definition | 2,264 | 5,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,584 |
class Cohere2LayerNorm(nn.Module):
def __init__(self, hidden_size=None, eps=1e-5, bias=False):
"""The hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dim"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.varianc... | class_definition | 5,527 | 6,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,585 |
class Cohere2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Cohere2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "... | class_definition | 9,728 | 13,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,586 |
class Cohere2MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
se... | class_definition | 13,587 | 14,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,587 |
class Cohere2DecoderLayer(nn.Module):
def __init__(self, config: Cohere2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Cohere2Attention(config, layer_idx)
self.mlp = Cohere2MLP(config)
self.input_layernorm = Cohere2LayerNorm... | class_definition | 14,260 | 18,448 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,588 |
class Cohere2PreTrainedModel(PreTrainedModel):
config_class = Cohere2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Cohere2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | class_definition | 19,478 | 20,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,589 |
class Cohere2Model(Cohere2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Cohere2DecoderLayer`]
Args:
config: Cohere2Config
"""
def __init__(self, config: Cohere2Config):
super().__init__(config)
self.padding_idx =... | class_definition | 25,217 | 35,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,590 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 35,251 | 35,313 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,591 |
class Cohere2ForCausalLM(Cohere2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config: Cohere2Config):
super().__init__(config)
self.model = Cohere2Model(config)
self.vocab_size = config.vocab_size
... | class_definition | 35,316 | 44,439 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modeling_cohere2.py | null | 5,592 |
class Cohere2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can b... | class_definition | 1,504 | 11,533 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,593 |
class Cohere2RotaryEmbedding(CohereRotaryEmbedding):
pass | class_definition | 11,536 | 11,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,594 |
class Cohere2LayerNorm(CohereLayerNorm):
pass | class_definition | 11,600 | 11,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,595 |
class Cohere2Attention(CohereAttention, nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Cohere2Config, layer_idx: Optional[int] = None):
nn.Module.__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim ... | class_definition | 11,652 | 15,527 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,596 |
class Cohere2DecoderLayer(CohereDecoderLayer):
def __init__(self, config: Cohere2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = Cohere2Attention(config, layer_idx)
self.config = config
self.is_sliding = (layer_idx + 1) % self.config.sliding_window_patte... | class_definition | 15,530 | 19,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,597 |
class Cohere2PreTrainedModel(CoherePreTrainedModel):
config_class = Cohere2Config | class_definition | 19,554 | 19,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,598 |
class Cohere2Model(Gemma2Model):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Cohere2DecoderLayer`]
Args:
config: Cohere2Config
"""
def __init__(self, config: Cohere2Config):
super().__init__(config)
self.norm = Cohere2LayerNorm(... | class_definition | 19,642 | 24,603 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere2/modular_cohere2.py | null | 5,599 |
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