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 OmDetTurboTaskEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.mlp = OmDetTurboMLPWithDropout(config)
self.res1 = OmDetTurboResidualLayer(config)
def forward(self, x):
mlp_out = self.mlp(x)
x = self.res1(x, mlp_out)
return x | class_definition | 43,485 | 43,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,400 |
class OmDetTurboDeformableTransformerDecoderLayer(nn.Module):
"""
A single layer of the Deformable Transformer Decoder.
"""
def __init__(self, config):
super().__init__()
# self attention
self.self_attn = OmDetTurboMultiheadAttention(
config,
hidden_size=... | class_definition | 43,798 | 48,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,401 |
class OmDetTurboPreTrainedModel(PreTrainedModel):
config_class = OmDetTurboConfig
base_model_prefix = "model"
main_input_name = "pixel_values"
def _init_weights(self, module):
def linear_init_(module_to_init):
bound = 1 / math.sqrt(module_to_init.weight.shape[0])
nn.init... | class_definition | 48,251 | 54,867 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,402 |
class OmDetTurboDecoder(OmDetTurboPreTrainedModel):
def __init__(self, config: OmDetTurboConfig):
self.config = config
super().__init__(config)
self.gradient_checkpointing = False
hidden_dim = config.decoder_hidden_dim
self.num_queries = config.num_queries
self.class... | class_definition | 59,202 | 74,316 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,403 |
class OmDetTurboForObjectDetection(OmDetTurboPreTrainedModel):
def __init__(self, config: OmDetTurboConfig):
super().__init__(config)
self.vision_backbone = OmDetTurboVisionBackbone(config)
self.language_backbone = OmDetTurboLanguageBackbone(config)
self.encoder = OmDetTurboHybridEnc... | class_definition | 74,577 | 81,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,404 |
class OmDetTurboConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OmDetTurboForObjectDetection`].
It is used to instantiate a OmDet-Turbo model according to the specified arguments, defining the model architecture
Instantiating a configuration with the defa... | class_definition | 891 | 14,445 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/configuration_omdet_turbo.py | null | 3,405 |
class EnglishNormalizer:
def __init__(self):
# List of (regular expression, replacement) pairs for abbreviations:
self._abbreviations = [
(re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
for x in [
("mrs", "misess"),
("mr", "mister"),
... | class_definition | 660 | 8,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/number_normalizer.py | null | 3,406 |
class ClvpTokenizer(PreTrainedTokenizer):
"""
Construct a CLVP tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (wit... | class_definition | 2,457 | 14,799 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/tokenization_clvp.py | null | 3,407 |
class ClvpFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a CLVP 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 | 993 | 10,946 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/feature_extraction_clvp.py | null | 3,408 |
class ClvpEncoderOutput(ModelOutput):
"""
Base class for CLVP encoder's outputs that contains a pooling of the last hidden states as well as a projection
output (a linear layer on top of the pooled output).
Args:
embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, retur... | class_definition | 6,066 | 8,026 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,409 |
class ClvpOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for speech-text similarity.
speech_ids (`torch.LongTensor`, *optional*):
speech_ids (or speech candidates) generated by... | class_definition | 8,040 | 10,538 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,410 |
class ClvpRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
ClvpRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 10,626 | 11,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,411 |
class ClvpRotaryPositionalEmbedding(nn.Module):
"""
Rotary Position Embedding Class for CLVP. It was proposed in the paper 'ROFORMER: ENHANCED TRANSFORMER WITH ROTARY
POSITION EMBEDDING', Please see https://arxiv.org/pdf/2104.09864v1.pdf .
"""
def __init__(self, config):
super().__init__()
... | class_definition | 11,347 | 12,707 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,412 |
class ClvpSelfAttention(nn.Module):
"""
Multi-headed attention to combine Absolute and Rotary Positional Embeddings into a single Attention module.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads ... | class_definition | 12,710 | 18,428 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,413 |
class ClvpGatedLinearUnit(nn.Module):
"""
`ClvpGatedLinearUnit` uses the second half of the `hidden_states` to act as a gate for the first half of the
`hidden_states` which controls the flow of data from the first of the tensor.
"""
def __init__(self, config):
super().__init__()
sel... | class_definition | 18,431 | 19,081 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,414 |
class ClvpEncoderMLP(nn.Module):
"""
This MLP is used in CLVP speech or text encoder models.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.fc1 = ClvpGatedLinearUnit(config)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)... | class_definition | 19,084 | 19,722 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,415 |
class ClvpEncoderLayer(nn.Module):
def __init__(self, config: ClvpConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.self_attn = ClvpSelfAttention(config)
self.mlp = ClvpEncoderMLP(config)
self.input_rmsnorm = ClvpRMSNorm(self.e... | class_definition | 19,725 | 22,091 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,416 |
class ClvpDecoderMLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
... | class_definition | 22,181 | 22,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,417 |
class ClvpDecoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.input_layernorm = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
se... | class_definition | 22,882 | 24,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,418 |
class ClvpConditioningEncoder(nn.Module):
"""
This class processes the log-mel spectrograms(extracted by the Feature Extractor) and text tokens(produced by the
tokenizer) as inputs for the decoder model.
First each log-mel spectrogram is processed into a single vector which captures valuable characteri... | class_definition | 24,881 | 31,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,419 |
class ClvpPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ClvpConfig
base_model_prefix = "clvp"
supports_gradient_checkpointing = True
_skip_keys_device_plac... | class_definition | 31,149 | 33,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,420 |
class ClvpEncoder(ClvpPreTrainedModel):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`ClvpEncoderLayer`].
Args:
config: ClvpConfig
"""
def __init__(self, config: ClvpConfig):
super().__init__(config)
self.conf... | class_definition | 39,973 | 46,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,421 |
class ClvpDecoder(ClvpPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ClvpDecoderLayer`]
"""
def __init__(self, config):
super().__init__(config)
self.config = config
self.input_embeds_layer = nn.Embedding(self.confi... | class_definition | 46,813 | 54,421 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,422 |
class ClvpModel(ClvpPreTrainedModel):
def __init__(self, config: ClvpDecoderConfig):
super().__init__(config)
self.config = config
self.decoder = ClvpDecoder(self.config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
... | class_definition | 54,573 | 57,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,423 |
class ClvpForCausalLM(ClvpPreTrainedModel, GenerationMixin):
def __init__(self, config):
super().__init__(config)
self.config = config
self.model = ClvpModel(self.config)
self.final_norm = nn.LayerNorm(self.config.hidden_size)
self.lm_head = nn.Linear(self.config.hidden_siz... | class_definition | 57,446 | 67,600 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,424 |
class ClvpModelForConditionalGeneration(ClvpPreTrainedModel, GenerationMixin):
config_class = ClvpConfig
def __init__(self, config: ClvpConfig):
super().__init__(config)
if not isinstance(config.text_config, ClvpEncoderConfig):
raise TypeError(
"config.text_config i... | class_definition | 67,914 | 91,303 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/modeling_clvp.py | null | 3,425 |
class ClvpProcessor(ProcessorMixin):
r"""
Constructs a CLVP processor which wraps a CLVP Feature Extractor and a CLVP Tokenizer into a single processor.
[`ClvpProcessor`] offers all the functionalities of [`ClvpFeatureExtractor`] and [`ClvpTokenizer`]. See the
[`~ClvpProcessor.__call__`], [`~ClvpProces... | class_definition | 689 | 3,603 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/processing_clvp.py | null | 3,426 |
class ClvpEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP
text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults
will yield a similar configu... | class_definition | 861 | 7,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py | null | 3,427 |
class ClvpDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP
Decoder Model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yiel... | class_definition | 7,111 | 15,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py | null | 3,428 |
class ClvpConfig(PretrainedConfig):
r"""
[`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It
is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and
decoder model configs. Instanti... | class_definition | 15,150 | 20,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/clvp/configuration_clvp.py | null | 3,429 |
class M2M100ScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding... | class_definition | 3,335 | 3,822 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,430 |
class M2M100SinusoidalPositionalEmbedding(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 = embeddi... | class_definition | 3,825 | 7,426 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,431 |
class M2M100Attention(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,
c... | class_definition | 7,514 | 14,908 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,432 |
class M2M100FlashAttention2(M2M100Attention):
"""
M2M100 flash attention module. This module inherits from `M2M100Attention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal wit... | class_definition | 15,002 | 21,450 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,433 |
class M2M100SdpaAttention(M2M100Attention):
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_mask: Optional[... | class_definition | 21,542 | 27,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,434 |
class M2M100EncoderLayer(nn.Module):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_head... | class_definition | 27,437 | 30,575 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,435 |
class M2M100DecoderLayer(nn.Module):
def __init__(self, config: M2M100Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = M2M100_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_head... | class_definition | 30,829 | 36,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,436 |
class M2M100PreTrainedModel(PreTrainedModel):
config_class = M2M100Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["M2M100EncoderLayer", "M2M100DecoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
... | class_definition | 36,712 | 37,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,437 |
class M2M100Encoder(M2M100PreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`M2M100EncoderLayer`].
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M10... | class_definition | 45,044 | 53,896 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,438 |
class M2M100Decoder(M2M100PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`M2M100DecoderLayer`]
Args:
config: M2M100Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: M2M100Config, embed_token... | class_definition | 53,899 | 68,563 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,439 |
class M2M100Model(M2M100PreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: M2M100Config):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(conf... | class_definition | 68,712 | 74,082 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,440 |
class M2M100ForConditionalGeneration(M2M100PreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: M2M100Config):
super().__init__(config)
self.model = M2M... | class_definition | 74,220 | 79,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | null | 3,441 |
class M2M100Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`M2M100Model`]. It is used to instantiate an
M2M100 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 1,071 | 7,302 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py | null | 3,442 |
class M2M100OnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
... | class_definition | 7,305 | 13,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/configuration_m2m_100.py | null | 3,443 |
class M2M100Tokenizer(PreTrainedTokenizer):
"""
Construct an M2M100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information rega... | class_definition | 1,885 | 15,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | null | 3,444 |
class Olmo2RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Olmo2RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 1,693 | 2,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,445 |
class Olmo2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Olmo2Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head... | class_definition | 5,692 | 9,577 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,446 |
class Olmo2MLP(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)
self... | class_definition | 9,580 | 10,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,447 |
class Olmo2DecoderLayer(nn.Module):
def __init__(self, config: Olmo2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Olmo2Attention(config=config, layer_idx=layer_idx)
self.mlp = Olmo2MLP(config)
self.post_attention_layernorm... | class_definition | 10,251 | 12,311 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,448 |
class Olmo2RotaryEmbedding(nn.Module):
def __init__(self, config: Olmo2Config, 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", co... | class_definition | 12,314 | 15,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,449 |
class Olmo2PreTrainedModel(PreTrainedModel):
config_class = Olmo2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Olmo2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | class_definition | 16,531 | 17,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,450 |
class Olmo2Model(Olmo2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Olmo2DecoderLayer`]
Args:
config: Olmo2Config
"""
def __init__(self, config: Olmo2Config):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 22,258 | 33,483 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,451 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 33,486 | 33,548 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,452 |
class Olmo2ForCausalLM(Olmo2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Olmo2Model(config)
self.vocab_size = config.vocab_size
self.lm_head ... | class_definition | 33,551 | 38,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py | null | 3,453 |
class Olmo2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 870 | 8,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py | null | 3,454 |
class Olmo2Config(OlmoConfig):
r"""
This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | class_definition | 551 | 6,987 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py | null | 3,455 |
class Olmo2RMSNorm(LlamaRMSNorm):
pass | class_definition | 6,990 | 7,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py | null | 3,456 |
class Olmo2Attention(OlmoAttention):
def __init__(self, config: Olmo2Config, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx=layer_idx)
self.q_norm = Olmo2RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps)
self.k_norm = Olmo2RMSNorm(config.num_key_v... | class_definition | 7,208 | 10,076 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py | null | 3,457 |
class Olmo2DecoderLayer(OlmoDecoderLayer):
def __init__(self, config: Olmo2Config, layer_idx: int):
super().__init__(config, layer_idx=layer_idx)
self.post_attention_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_feedforward_layernorm = Olmo2RMSNorm(config.hi... | class_definition | 10,267 | 12,311 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py | null | 3,458 |
class Olmo2Model(OlmoModel):
def __init__(self, config: Olmo2Config):
super().__init__(config)
self.norm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layers = nn.ModuleList(
[Olmo2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)... | class_definition | 12,441 | 12,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py | null | 3,459 |
class Olmo2ForCausalLM(OlmoForCausalLM):
pass | class_definition | 12,860 | 12,909 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py | null | 3,460 |
class Emu3ImageProcessor(BaseImageProcessor):
r"""
Constructs a Emu3 image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample (`PILIm... | class_definition | 3,437 | 27,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py | null | 3,461 |
class Emu3TextKwargs(TextKwargs, total=False):
return_for_image_generation: bool | class_definition | 925 | 1,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py | null | 3,462 |
class Emu3ImagesKwargs(ImagesKwargs, total=False):
ratio: str
image_area: int | class_definition | 1,012 | 1,097 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py | null | 3,463 |
class Emu3ProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: Emu3TextKwargs
images_kwargs: Emu3ImagesKwargs
_defaults = {
"text_kwargs": {
"return_for_image_generation": False,
},
"images_kwargs": {
"ratio": "1:1",
"image_area": 518400,
... | class_definition | 1,100 | 1,435 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py | null | 3,464 |
class Emu3Processor(ProcessorMixin):
r"""
Constructs a Emu3 processor which wraps a Emu3 image processor and a GPT2 tokenizer into a single
processor.
[`Emu3Processor`] offers all the functionalities of [`Emu3ImageProcessor`] and [`GPT2TokenizerFast`].
See the [`~Emu3Processor.__call__`] and [`~Emu... | class_definition | 1,438 | 10,384 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/processing_emu3.py | null | 3,465 |
class Emu3VQVAEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Emu3VQVAE`]. It is used to instantiate an VQ-VAE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a confi... | class_definition | 785 | 4,735 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py | null | 3,466 |
class Emu3TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Emu3TextModel`]. It is used to instantiate a
emu3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a simil... | class_definition | 4,738 | 14,024 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py | null | 3,467 |
class Emu3Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Emu3Model`]. It is used to instantiate a
emu3 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar config... | class_definition | 14,027 | 16,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/configuration_emu3.py | null | 3,468 |
class Emu3DecoderLayer(LlamaDecoderLayer):
def __init__(self, config: Emu3Config, layer_idx: int):
super().__init__(config, layer_idx)
self.dropout = nn.Dropout(config.attention_dropout)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tenso... | class_definition | 1,822 | 4,891 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,469 |
class Emu3VQVAEVectorQuantizer(nn.Module):
"""
A module for vector quantization using learned embedding vectors.
This module implements the quantization process similar to te one described in
the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
input vectors into dis... | class_definition | 4,894 | 6,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,470 |
class Emu3VQVAEEncoderConvDownsample(ChameleonVQVAEEncoderConvDownsample):
pass | class_definition | 6,564 | 6,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,471 |
class Emu3VQVAEEncoderConvUpsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
def forward(self, hidden_states):
hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="n... | class_definition | 6,650 | 7,056 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,472 |
class Emu3VQVAEConv3d(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
kernel_size: Tuple[int],
stride: Tuple[int],
):
super().__init__()
padding_sizes = [one_kernel - one_stride for one_kernel, one_stride in zip(kernel_size[1:], stride[... | class_definition | 7,059 | 7,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,473 |
class Emu3VQVAESpatialNorm(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
):
super().__init__()
self.norm_layer = nn.GroupNorm(
num_channels=out_channels,
num_groups=32,
eps=1e-6,
affine=True,
)... | class_definition | 7,896 | 8,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,474 |
class Emu3VQVAETemporalUpsample(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
):
super().__init__()
self.conv = Emu3VQVAEConv3d(
in_channel,
out_channel,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
... | class_definition | 8,909 | 9,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,475 |
class Emu3VQVAETemporalDownsample(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
):
super().__init__()
self.conv = Emu3VQVAEConv3d(
in_channel,
out_channel,
kernel_size=(4, 3, 3),
stride=(2, 1, 1),
... | class_definition | 9,752 | 10,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,476 |
class Emu3VQVAETemporalResnetBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels=None,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = in_channels if out_channels is None else out_channels
self.norm1 = nn.BatchNorm3d(i... | class_definition | 10,209 | 11,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,477 |
class Emu3VQVAEResnetBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
quant_channels: Optional[int] = None,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None e... | class_definition | 11,696 | 13,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,478 |
class Emu3VQVAEAttentionBlock(SiglipAttention):
pass | class_definition | 13,798 | 13,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,479 |
class Emu3VQVAEGroupNorm(nn.GroupNorm):
"""
Same as the torch GroupNorm with the only difference that this ones accepts
an optional kwarg `quant_states` which is not used. This class makes it easier to
use SpatialNorm or GroupNorm without conditionals
"""
def __init__(self, **kwargs):
s... | class_definition | 13,857 | 14,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,480 |
class Emu3VQVAEMiddleBlock(nn.Module):
def __init__(self, config, in_channels, quant_channels=None):
super().__init__()
self.block_1 = Emu3VQVAEResnetBlock(
in_channels=in_channels,
out_channels=in_channels,
quant_channels=quant_channels,
)
self.a... | class_definition | 14,341 | 15,843 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,481 |
class Emu3VQVAEDownBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
base_channels = config.base_channels
channel_multiplier = config.channel_multiplier
... | class_definition | 15,846 | 18,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,482 |
class Emu3VQVAEUpBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
quant_channels = config.embed_dim
block_in = config.base_channels * config.channel_multiplier[... | class_definition | 18,475 | 20,955 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,483 |
class Emu3VQVAEEncoder(nn.Module):
def __init__(self, config):
super().__init__()
base_channels = config.base_channels
in_channels = config.in_channels
double_latent = config.double_latent
latent_channels = config.latent_channels
channel_multiplier = config.channel_m... | class_definition | 20,958 | 23,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,484 |
class Emu3VQVAEDecoder(nn.Module):
def __init__(self, config: Emu3VQVAEConfig):
super().__init__()
quant_channels = config.embed_dim
block_in = config.base_channels * config.channel_multiplier[-1]
self.time_res_stack = nn.ModuleList()
for _ in range(config.num_res_blocks):
... | class_definition | 23,568 | 26,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,485 |
class Emu3VQVAE(PreTrainedModel):
config_class = Emu3VQVAEConfig
base_model_prefix = "emuvideovq"
main_input_name = "pixel_values"
_no_split_modules = [
"Emu3VQVAETemporalResnetBlock",
"Emu3VQVAEAttentionBlock",
"Emu3VQVAEResnetBlock",
"Emu3VQVAEVectorQuantizer",
]
... | class_definition | 27,579 | 31,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,486 |
class Emu3ImageVocabularyMapping:
"""
A class for mapping discrete image tokens from VQGAN to BPE tokens.
"""
def __init__(self, vocab_map):
self.vocab_map = vocab_map
self.eol_token_id = vocab_map.get("<|extra_200|>")
self.image_token_id = vocab_map.get("<image>")
@cached_... | class_definition | 31,476 | 33,455 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,487 |
class Emu3PreTrainedModel(ChameleonPreTrainedModel, Emu3VQVAE):
_no_split_modules = [
"Emu3DecoderLayer",
]
_supports_flex_attn = True
def _init_weights(self, module):
std = self.config.get_text_config().initializer_range
if isinstance(module, Emu3VQVAE):
module.appl... | class_definition | 33,458 | 34,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,488 |
class Emu3TextModel(LlamaModel, Emu3PreTrainedModel):
def __init__(self, config: Emu3Config):
super().__init__(config)
self.layers = nn.ModuleList(
[Emu3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
) | class_definition | 43,704 | 43,980 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,489 |
class Emu3ForCausalLM(LlamaForCausalLM, Emu3PreTrainedModel, GenerationMixin):
config_class = Emu3TextConfig
def __init__(self, config):
super().__init__(config)
self.model = Emu3TextModel(config)
@add_start_docstrings_to_model_forward(EMU3_TEXT_INPUTS_DOCSTRING)
@replace_return_docstr... | class_definition | 43,983 | 46,005 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,490 |
class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["text_model.lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.text_model = Emu3ForCausalLM._from_config(config.text_config)
self.vqmodel = Emu3VQVAE(config.vq_config)... | class_definition | 46,008 | 53,429 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modular_emu3.py | null | 3,491 |
class Emu3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Emu3RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 2,429 | 3,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,492 |
class Emu3MLP(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=config.mlp_bias)
... | class_definition | 3,150 | 3,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,493 |
class Emu3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Emu3Config, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidde... | class_definition | 7,126 | 10,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,494 |
class Emu3DecoderLayer(nn.Module):
def __init__(self, config: Emu3Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Emu3Attention(config=config, layer_idx=layer_idx)
self.mlp = Emu3MLP(config)
self.input_layernorm = Emu3RMSNo... | class_definition | 10,694 | 14,081 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,495 |
class Emu3VQVAEVectorQuantizer(nn.Module):
"""
A module for vector quantization using learned embedding vectors.
This module implements the quantization process similar to te one described in
the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
input vectors into dis... | class_definition | 14,084 | 15,751 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,496 |
class Emu3VQVAEEncoderConvDownsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
def forward(self, hidden_states):
# no asymmetric padding in torch conv, must do it ourselves
... | class_definition | 15,754 | 16,232 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,497 |
class Emu3VQVAEEncoderConvUpsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
def forward(self, hidden_states):
hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="n... | class_definition | 16,235 | 16,641 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,498 |
class Emu3VQVAEConv3d(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
kernel_size: Tuple[int],
stride: Tuple[int],
):
super().__init__()
padding_sizes = [one_kernel - one_stride for one_kernel, one_stride in zip(kernel_size[1:], stride[... | class_definition | 16,644 | 17,478 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,499 |
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