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 BertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(... | class_definition | 36,335 | 36,798 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,100 |
class BertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BertConfig
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"
supports_gradient_c... | class_definition | 36,801 | 37,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,101 |
class BertForPreTrainingOutput(ModelOutput):
"""
Output type of [`BertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
... | class_definition | 37,991 | 39,941 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,102 |
class BertModel(BertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/17... | class_definition | 43,655 | 54,373 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,103 |
class BertForPreTraining(BertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.bert = BertModel(config)
self.cls = BertPreTrainingHeads(config)
# Initialize weights an... | class_definition | 54,607 | 59,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,104 |
class BertLMHeadModel(BertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `BertLMHeadModel` a... | class_definition | 59,351 | 65,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,105 |
class BertForMaskedLM(BertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `BertForMaskedLM` make sure... | class_definition | 65,206 | 69,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,106 |
class BertForNextSentencePrediction(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = BertModel(config)
self.cls = BertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_mod... | class_definition | 69,737 | 73,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,107 |
class BertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classi... | class_definition | 73,858 | 77,970 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,108 |
class BertForMultipleChoice(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = BertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.d... | class_definition | 78,201 | 81,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,109 |
class BertForTokenClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = BertModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dr... | class_definition | 82,062 | 85,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,110 |
class BertForQuestionAnswering(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = BertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initiali... | class_definition | 85,312 | 89,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/modeling_bert.py | null | 7,111 |
class BertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" BERT 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 superclass for more i... | class_definition | 1,042 | 7,651 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/tokenization_bert_fast.py | null | 7,112 |
class BertTokenizer(PreTrainedTokenizer):
r"""
Construct a BERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file ... | class_definition | 1,545 | 12,202 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/tokenization_bert.py | null | 7,113 |
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 | 12,205 | 18,953 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/tokenization_bert.py | null | 7,114 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 18,956 | 20,844 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bert/tokenization_bert.py | null | 7,115 |
class MusicgenDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`MusicgenDecoder`]. It is used to instantiate a
MusicGen decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults wi... | class_definition | 846 | 6,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/configuration_musicgen.py | null | 7,116 |
class MusicgenConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MusicgenModel`]. It is used to instantiate a
MusicGen model according to the specified arguments, defining the text encoder, audio encoder and MusicGen decoder
configs.
Configuration objec... | class_definition | 6,402 | 10,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/configuration_musicgen.py | null | 7,117 |
class MusicgenUnconditionalInput(ModelOutput):
"""
Args:
encoder_outputs (`Tuple[torch.FloatTensor]` of length 1, with tensor shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the text encoder model.
attention_mask (`to... | class_definition | 2,250 | 3,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,118 |
class MusicgenSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int):
super().__init__()
self.embedding_dim = embedding_dim
self.make_weights(num_positions, embedding... | class_definition | 4,129 | 6,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,119 |
class MusicgenAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | class_definition | 6,396 | 13,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,120 |
class MusicgenFlashAttention2(MusicgenAttention):
"""
Musicgen flash attention module. This module inherits from `MusicgenAttention` 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 ... | class_definition | 13,890 | 20,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,121 |
class MusicgenSdpaAttention(MusicgenAttention):
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: Optio... | class_definition | 20,353 | 27,165 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,122 |
class MusicgenDecoderLayer(nn.Module):
def __init__(self, config: MusicgenDecoderConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = MUSICGEN_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.num_a... | class_definition | 27,320 | 33,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,123 |
class MusicgenPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MusicgenDecoderConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_sp... | class_definition | 33,355 | 34,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,124 |
class MusicgenDecoder(MusicgenPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MusicgenDecoderLayer`]
"""
def __init__(self, config: MusicgenDecoderConfig):
super().__init__(config)
self.dropout = config.dropout
self.la... | class_definition | 47,370 | 57,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,125 |
class MusicgenModel(MusicgenPreTrainedModel):
def __init__(self, config: MusicgenDecoderConfig):
super().__init__(config)
self.decoder = MusicgenDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.d... | class_definition | 57,714 | 60,590 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,126 |
class MusicgenForCausalLM(MusicgenPreTrainedModel, GenerationMixin):
def __init__(self, config: MusicgenDecoderConfig):
super().__init__(config)
self.model = MusicgenModel(config)
self.num_codebooks = config.num_codebooks
self.lm_heads = nn.ModuleList(
[nn.Linear(config... | class_definition | 60,721 | 83,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,127 |
class MusicgenForConditionalGeneration(PreTrainedModel, GenerationMixin):
config_class = MusicgenConfig
base_model_prefix = "encoder_decoder"
main_input_name = "input_ids"
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports_sdpa = True
def __init__(
self,
... | class_definition | 83,303 | 135,846 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/modeling_musicgen.py | null | 7,128 |
class MusicgenProcessor(ProcessorMixin):
r"""
Constructs a MusicGen processor which wraps an EnCodec feature extractor and a T5 tokenizer into a single processor
class.
[`MusicgenProcessor`] offers all the functionalities of [`EncodecFeatureExtractor`] and [`TTokenizer`]. See
[`~MusicgenProcessor._... | class_definition | 788 | 5,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/musicgen/processing_musicgen.py | null | 7,129 |
class PLBartConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PLBartModel`]. It is used to instantiate an
PLBART model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simila... | class_definition | 931 | 7,549 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/configuration_plbart.py | null | 7,130 |
class PLBartOnnxConfig(OnnxConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("attention_mask", {0: "batch", 1: "sequence"}),
]
)
@property... | class_definition | 7,552 | 8,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/configuration_plbart.py | null | 7,131 |
class PLBartLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# PLBart is set up so that if padding_idx is specified then offset the embedding ids by 2
# and... | class_definition | 2,888 | 3,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,132 |
class PLBartScaledWordEmbedding(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 | 3,894 | 4,381 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,133 |
class PLBartAttention(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 | 4,469 | 11,863 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,134 |
class PLBartEncoderLayer(nn.Module):
def __init__(self, config: PLBartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = PLBART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_head... | class_definition | 11,968 | 15,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,135 |
class PLBartDecoderLayer(nn.Module):
def __init__(self, config: PLBartConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = PLBART_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_head... | class_definition | 15,383 | 21,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,136 |
class PLBartClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_d... | class_definition | 21,427 | 22,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,137 |
class PLBartPreTrainedModel(PreTrainedModel):
config_class = PLBartConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PLBartDecoderLayer", "PLBartEncoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(mod... | class_definition | 22,218 | 22,908 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,138 |
class PLBartEncoder(PLBartPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`PLBartEncoderLayer`].
Args:
config: PLBartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: PLBar... | class_definition | 31,352 | 40,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,139 |
class PLBartDecoder(PLBartPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PLBartDecoderLayer`]
Args:
config: PLBartConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: PLBartConfig, embed_token... | class_definition | 40,201 | 54,796 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,140 |
class PLBartModel(PLBartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: PLBartConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(conf... | class_definition | 54,944 | 60,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,141 |
class PLBartForConditionalGeneration(PLBartPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
def __init__(self, config: PLBartConfig... | class_definition | 60,472 | 66,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,142 |
class PLBartForSequenceClassification(PLBartPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: PLBartConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = PLBartModel(config)
self.classification_h... | class_definition | 66,724 | 72,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,143 |
class PLBartDecoderWrapper(PLBartPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
... | class_definition | 72,400 | 72,847 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,144 |
class PLBartForCausalLM(PLBartPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = PLBartDeco... | class_definition | 72,978 | 82,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/modeling_plbart.py | null | 7,145 |
class PLBartTokenizer(PreTrainedTokenizer):
"""
Construct an PLBART tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and... | class_definition | 1,459 | 18,859 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/plbart/tokenization_plbart.py | null | 7,146 |
class Wav2Vec2ConformerForPreTrainingOutput(ModelOutput):
"""
Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.
Args:
loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
Total loss as ... | class_definition | 2,148 | 5,011 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,147 |
class Wav2Vec2ConformerNoLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_di... | class_definition | 11,974 | 12,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,148 |
class Wav2Vec2ConformerLayerNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,... | class_definition | 12,836 | 13,824 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,149 |
class Wav2Vec2ConformerGroupNormConvLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1
self.out_conv_dim = config.conv_dim[layer_id]
self.conv = nn.Conv1d(
self.in_conv_dim,... | class_definition | 13,948 | 14,854 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,150 |
class Wav2Vec2ConformerPositionalConvEmbedding(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_embeddings ... | class_definition | 14,983 | 16,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,151 |
class Wav2Vec2ConformerRotaryPositionalEmbedding(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.num_atten... | class_definition | 16,797 | 18,377 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,152 |
class Wav2Vec2ConformerRelPositionalEmbedding(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(0.0... | class_definition | 18,380 | 20,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,153 |
class Wav2Vec2ConformerSamePadLayer(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 | 20,896 | 21,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,154 |
class Wav2Vec2ConformerFeatureEncoder(nn.Module):
"""Construct the features from raw audio waveform"""
def __init__(self, config):
super().__init__()
if config.feat_extract_norm == "group":
conv_layers = [Wav2Vec2ConformerGroupNormConvLayer(config, layer_id=0)] + [
... | class_definition | 21,390 | 23,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,155 |
class Wav2Vec2ConformerFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps)
self.projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
self.dropout = nn.Dropout(config.fea... | class_definition | 23,299 | 23,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,156 |
class Wav2Vec2ConformerFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.intermediate_dropout = nn.Dropout(config.activation_dropout)
self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
... | class_definition | 24,077 | 25,056 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,157 |
class Wav2Vec2ConformerConvolutionModule(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 number f... | class_definition | 25,059 | 27,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,158 |
class Wav2Vec2ConformerSelfAttention(nn.Module):
"""Construct an Wav2Vec2ConformerSelfAttention object.
Can be enhanced with rotary or relative position embeddings.
"""
def __init__(self, config):
super().__init__()
self.head_size = config.hidden_size // config.num_attention_heads
... | class_definition | 27,227 | 34,331 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,159 |
class Wav2Vec2ConformerEncoderLayer(nn.Module):
"""Conformer block based on https://arxiv.org/abs/2005.08100."""
def __init__(self, config):
super().__init__()
embed_dim = config.hidden_size
dropout = config.attention_dropout
# Feed-forward 1
self.ffn1_layer_norm = nn.L... | class_definition | 34,334 | 36,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,160 |
class Wav2Vec2ConformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.position_embeddings_type == "relative":
self.embed_positions = Wav2Vec2ConformerRelPositionalEmbedding(config)
elif config.position_embeddings_type ==... | class_definition | 36,749 | 40,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,161 |
class Wav2Vec2ConformerGumbelVectorQuantizer(nn.Module):
"""
Vector quantization using gumbel softmax. See `[CATEGORICAL REPARAMETERIZATION WITH
GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information.
"""
def __init__(self, config):
super().__init__()
self.num_gr... | class_definition | 40,937 | 44,363 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,162 |
class Wav2Vec2ConformerAdapter(nn.Module):
def __init__(self, config):
super().__init__()
# feature dim might need to be down-projected
if config.output_hidden_size != config.hidden_size:
self.proj = nn.Linear(config.hidden_size, config.output_hidden_size)
self.proj_... | class_definition | 44,476 | 45,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,163 |
class Wav2Vec2ConformerAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
config.output_hidden_size,
2 * config.output_hidden_size,
config.adapter_kernel_size,
stride=config.adapter_stride,
paddin... | class_definition | 45,815 | 46,331 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,164 |
class Wav2Vec2ConformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2ConformerConfig
base_model_prefix = "wav2vec2_conformer"
main_input_name = "input_... | class_definition | 46,334 | 50,939 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,165 |
class Wav2Vec2ConformerModel(Wav2Vec2ConformerPreTrainedModel):
def __init__(self, config: Wav2Vec2ConformerConfig):
super().__init__(config)
self.config = config
self.feature_extractor = Wav2Vec2ConformerFeatureEncoder(config)
self.feature_projection = Wav2Vec2ConformerFeatureProjec... | class_definition | 54,474 | 60,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,166 |
class Wav2Vec2ConformerForPreTraining(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
def __init__(self, config: Wav2Vec2ConformerConfig):
super().__init__(config... | class_definition | 60,688 | 71,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,167 |
class Wav2Vec2ConformerForCTC(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
def __init__(self, config, target_lang: Optional[str] = None):
super().__init__(config)
... | class_definition | 71,830 | 77,056 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,168 |
class Wav2Vec2ConformerForSequenceClassification(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer
def __init__(self, config):
super().__init__(config)
... | class_definition | 77,296 | 82,399 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,169 |
class Wav2Vec2ConformerForAudioFrameClassification(Wav2Vec2ConformerPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForAudioFrameClassification.__init__ with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,WAV_2_VEC_2->WAV2VEC2_CONFORMER
def __init__(self, config)... | class_definition | 82,588 | 87,196 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,170 |
class AMSoftmaxLoss(nn.Module):
def __init__(self, input_dim, num_labels, scale=30.0, margin=0.4):
super(AMSoftmaxLoss, self).__init__()
self.scale = scale
self.margin = margin
self.num_labels = num_labels
self.weight = nn.Parameter(torch.randn(input_dim, num_labels), require... | class_definition | 87,274 | 88,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,171 |
class TDNNLayer(nn.Module):
def __init__(self, config, layer_id=0):
super().__init__()
self.in_conv_dim = config.tdnn_dim[layer_id - 1] if layer_id > 0 else config.tdnn_dim[layer_id]
self.out_conv_dim = config.tdnn_dim[layer_id]
self.kernel_size = config.tdnn_kernel[layer_id]
... | class_definition | 88,224 | 89,654 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,172 |
class Wav2Vec2ConformerForXVector(Wav2Vec2ConformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2_conformer = Wav2Vec2ConformerModel(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighte... | class_definition | 89,851 | 96,142 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py | null | 7,173 |
class Wav2Vec2ConformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Wav2Vec2ConformerModel`]. It is used to
instantiate an Wav2Vec2Conformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with... | class_definition | 852 | 20,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_conformer/configuration_wav2vec2_conformer.py | null | 7,174 |
class AriaTextConfig(LlamaConfig):
r"""
This class handles the configuration for the text component of the Aria model.
Instantiating a configuration with the defaults will yield a similar configuration to that of the model of the Aria
[rhymes-ai/Aria](https://huggingface.co/rhymes-ai/Aria) architecture.... | class_definition | 3,691 | 12,034 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,175 |
class AriaConfig(PretrainedConfig):
r"""
This class handles the configuration for both vision and text components of the Aria model,
as well as additional parameters for image token handling and projector mapping.
Instantiating a configuration with the defaults will yield a similar configuration to that... | class_definition | 12,037 | 15,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,176 |
class AriaTextRMSNorm(LlamaRMSNorm):
pass | class_definition | 15,613 | 15,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,177 |
class AriaProjectorMLP(nn.Module):
"""
Feed-Forward Network module for the Aria Projector.
Args:
in_features (`int`):
Input embedding dimension.
hidden_features (`int`):
Hidden dimension of the feed-forward network.
output_dim (`int`):
Output dime... | class_definition | 15,661 | 16,468 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,178 |
class AriaCrossAttention(nn.Module):
"""
Aria Cross-Attention module.
Args:
config (`AriaConfig`):
The configuration to use.
"""
def __init__(self, config: AriaConfig, dropout_rate: float = 0):
super().__init__()
hidden_size = config.vision_config.hidden_size
... | class_definition | 16,471 | 18,477 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,179 |
class AriaProjector(nn.Module):
"""
Aria Projector module.
This module projects vision features into the language model's embedding space, enabling interaction between vision and language components.
Args:
config (`AriaConfig`):
Configuration object for the model.
"""
def ... | class_definition | 18,480 | 20,932 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,180 |
class AriaImageProcessor(BaseImageProcessor):
"""
A vision processor for the Aria model that handles image preprocessing.
Initialize the AriaImageProcessor.
Args:
image_mean (`list`, *optional*, defaults to [0.5, 0.5, 0.5]):
Mean values for normalization.
image_std (`list`, ... | class_definition | 21,536 | 40,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,181 |
class AriaProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {
"max_image_size": 980,
"split_image": False,
},
"return_tensors": TensorType.PYTORCH,
} | class_definition | 40,116 | 40,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,182 |
class AriaProcessor(ProcessorMixin):
"""
AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer.
Args:
image_processor (`AriaImageProcessor`, *optional*):
The AriaImageProcessor to use for image preprocessing.
tokeniz... | class_definition | 40,419 | 45,935 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,183 |
class AriaSharedExpertsMLP(LlamaMLP):
"""
Shared Expert MLP for shared experts.
Unlike routed experts, shared experts process all tokens without routing.
This class reconfigures the intermediate size in comparison to the LlamaMLP.
Args:
config (`AriaTextConfig`): Configuration object for t... | class_definition | 45,938 | 46,459 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,184 |
class AriaGroupedExpertsGemm(nn.Module):
"""
Grouped GEMM (General Matrix Multiplication) module for efficient expert computation.
This module utilizes the grouped_gemm library (https://github.com/fanshiqing/grouped_gemm)
for optimized performance. If the grouped_gemm library is not installed, it gracef... | class_definition | 46,462 | 47,953 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,185 |
class AriaGroupedExpertsMLP(nn.Module):
"""
Grouped MLP module for Mixture of Experts.
Args:
config (`AriaTextConfig`):
Configuration object for the model.
"""
def __init__(self, config: AriaTextConfig) -> None:
super().__init__()
self.config = config
se... | class_definition | 47,956 | 49,151 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,186 |
class AriaTextMoELayer(nn.Module):
"""
Aria Text Mixture of Experts (MoE) Layer.
This layer applies a gating mechanism to route input tokens to different experts.
Args:
config (`AriaTextConfig`):
Configuration object for the text component of the model.
"""
def __init__(se... | class_definition | 49,331 | 52,222 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,187 |
class AriaTextDecoderLayer(LlamaDecoderLayer):
"""
Aria Text Decoder Layer.
This class defines a single decoder layer in the language model, incorporating self-attention and Mixture of Experts (MoE) feed-forward network.
Args:
config (`AriaTextConfig`):
Configuration object for the... | class_definition | 52,225 | 52,781 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,188 |
class AriaTextPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models.
"""
config_class = AriaConfig
base_model_prefix = "model"
_no_split_modules = ["AriaTextDecoderLayer", "AriaGroupedExperts... | class_definition | 52,784 | 54,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,189 |
class AriaPreTrainedModel(LlamaPreTrainedModel):
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
e... | class_definition | 54,106 | 54,736 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,190 |
class AriaTextModel(LlamaModel):
def __init__(self, config: AriaTextConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[AriaTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
self... | class_definition | 54,739 | 55,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,191 |
class AriaTextForCausalLM(AriaTextPreTrainedModel, LlamaForCausalLM):
"""
Aria model for causal language modeling tasks.
This class extends `LlamaForCausalLM` to incorporate the Mixture of Experts (MoE) approach,
allowing for more efficient and scalable language modeling.
Args:
config (`Ar... | class_definition | 55,074 | 55,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,192 |
class AriaCausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
pass | class_definition | 55,882 | 55,953 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,193 |
class AriaForConditionalGeneration(AriaPreTrainedModel, GenerationMixin):
config_class = AriaConfig
_supports_flash_attn_2 = False
_supports_sdpa = False
_tied_weights_keys = ["language_model.lm_head.weight"]
def __init__(self, config: AriaConfig):
super().__init__(config)
self.vis... | class_definition | 58,593 | 69,365 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modular_aria.py | null | 7,194 |
class AriaTextRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
AriaTextRMSNorm 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 | 2,549 | 3,275 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,195 |
class AriaProjectorMLP(nn.Module):
"""
Feed-Forward Network module for the Aria Projector.
Args:
in_features (`int`):
Input embedding dimension.
hidden_features (`int`):
Hidden dimension of the feed-forward network.
output_dim (`int`):
Output dime... | class_definition | 3,278 | 4,085 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,196 |
class AriaCrossAttention(nn.Module):
"""
Aria Cross-Attention module.
Args:
config (`AriaConfig`):
The configuration to use.
"""
def __init__(self, config: AriaConfig, dropout_rate: float = 0):
super().__init__()
hidden_size = config.vision_config.hidden_size
... | class_definition | 4,088 | 6,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,197 |
class AriaProjector(nn.Module):
"""
Aria Projector module.
This module projects vision features into the language model's embedding space, enabling interaction between vision and language components.
Args:
config (`AriaConfig`):
Configuration object for the model.
"""
def ... | class_definition | 6,097 | 8,549 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,198 |
class AriaSharedExpertsMLP(nn.Module):
"""
Shared Expert MLP for shared experts.
Unlike routed experts, shared experts process all tokens without routing.
This class reconfigures the intermediate size in comparison to the LlamaMLP.
Args:
config (`AriaTextConfig`): Configuration object for ... | class_definition | 8,552 | 9,625 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/aria/modeling_aria.py | null | 7,199 |
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