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 TFIdeficsMainLayer(tf.keras.layers.Layer):
"""
Transformer decoder consisting of `config.num_hidden_layers` layers. Each layer is a [`IdeficsDecoderLayer`]
Args:
config: IdeficsConfig
"""
config_class = IdeficsConfig
def __init__(self, config: IdeficsConfig, add_pooling_year: bo... | class_definition | 51,910 | 69,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,500 |
class TFIdeficsModel(TFIdeficsPreTrainedModel):
def __init__(self, config: IdeficsConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFIdeficsMainLayer(config, name="model")
def call(
self,
input_ids: TFModelInputType | None = None,
att... | class_definition | 69,483 | 71,580 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,501 |
class TFIdeficsForVisionText2Text(TFPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
config_class = IdeficsConfig
def __init__(self, config, vision_model=None, **kwargs):
su... | class_definition | 71,583 | 80,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/modeling_tf_idefics.py | null | 7,502 |
class IdeficsVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield... | class_definition | 1,089 | 4,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/configuration_idefics.py | null | 7,503 |
class IdeficsPerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yi... | class_definition | 4,662 | 6,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/configuration_idefics.py | null | 7,504 |
class IdeficsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IdeficsModel`]. It is used to instantiate an
Idefics model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 6,838 | 15,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/idefics/configuration_idefics.py | null | 7,505 |
class TFConvBertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: ConvBertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.m... | class_definition | 1,875 | 5,324 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,506 |
class TFConvBertSelfAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of atte... | class_definition | 5,327 | 13,342 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,507 |
class TFConvBertSelfOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = keras.layer... | class_definition | 13,345 | 14,589 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,508 |
class TFConvBertAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFConvBertSelfAttention(config, name="self")
self.dense_output = TFConvBertSelfOutput(config, name="output")
def prune_heads(self, heads):
raise... | class_definition | 14,592 | 15,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,509 |
class GroupedLinearLayer(keras.layers.Layer):
def __init__(self, input_size, output_size, num_groups, kernel_initializer, **kwargs):
super().__init__(**kwargs)
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.kernel_initializer = k... | class_definition | 15,826 | 17,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,510 |
class TFConvBertIntermediate(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.num_groups == 1:
self.dense = keras.layers.Dense(
config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="den... | class_definition | 17,181 | 18,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,511 |
class TFConvBertOutput(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.num_groups == 1:
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
... | class_definition | 18,512 | 20,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,512 |
class TFConvBertLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.attention = TFConvBertAttention(config, name="attention")
self.intermediate = TFConvBertIntermediate(config, name="intermediate")
self.bert_output = TFConvBertOutput(conf... | class_definition | 20,113 | 21,606 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,513 |
class TFConvBertEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.layer = [TFConvBertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states,
attention_mask,
head... | class_definition | 21,609 | 23,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,514 |
class TFConvBertPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstan... | class_definition | 23,336 | 24,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,515 |
class TFConvBertMainLayer(keras.layers.Layer):
config_class = ConvBertConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFConvBertEmbeddings(config, name="embeddings")
if config.embedding_size != config.hidden_size:
self.embeddings_p... | class_definition | 24,682 | 29,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,516 |
class TFConvBertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvBertConfig
base_model_prefix = "convbert" | class_definition | 29,352 | 29,618 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,517 |
class TFConvBertModel(TFConvBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.convbert = TFConvBertMainLayer(config, name="convbert")
@unpack_inputs
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("... | class_definition | 35,501 | 37,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,518 |
class TFConvBertMaskedLMHead(keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_size = config.embedding_size
self.input_embeddings = input_embeddings
def build(self, input_shape):
s... | class_definition | 37,367 | 38,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,519 |
class TFConvBertGeneratorPredictions(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dense = keras.layers.Dense(config.embedding_size, name="dense")
... | class_definition | 38,747 | 39,836 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,520 |
class TFConvBertForMaskedLM(TFConvBertPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.convbert = TFConvBertMainLayer(config, name="convbert")
self.generator_predictions = TFCo... | class_definition | 39,949 | 43,972 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,521 |
class TFConvBertClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_ra... | class_definition | 43,975 | 45,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,522 |
class TFConvBertForSequenceClassification(TFConvBertPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.convbert = TFConvBertMainLayer(config, name="convbert")
... | class_definition | 45,682 | 48,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,523 |
class TFConvBertForMultipleChoice(TFConvBertPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.convbert = TFConvBertMainLayer(config, name="convbert")
self.sequence_summary = TFSequenceSummary(
... | class_definition | 49,002 | 53,372 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,524 |
class TFConvBertForTokenClassification(TFConvBertPreTrainedModel, TFTokenClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.convbert = TFConvBertMainLayer(config, name="convbert")
c... | class_definition | 53,609 | 56,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,525 |
class TFConvBertForQuestionAnswering(TFConvBertPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.convbert = TFConvBertMainLayer(config, name="convbert")
self.... | class_definition | 57,252 | 61,362 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_tf_convbert.py | null | 7,526 |
class ConvBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConvBertModel`]. It is used to instantiate an
ConvBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a ... | class_definition | 870 | 6,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/configuration_convbert.py | null | 7,527 |
class ConvBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 6,333 | 6,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/configuration_convbert.py | null | 7,528 |
class ConvBertTokenizer(PreTrainedTokenizer):
r"""
Construct a ConvBERT 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:
voc... | class_definition | 1,821 | 12,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/tokenization_convbert.py | null | 7,529 |
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 | 12,573 | 19,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/tokenization_convbert.py | null | 7,530 |
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 | 19,400 | 21,288 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/tokenization_convbert.py | null | 7,531 |
class ConvBertEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
self.position_... | class_definition | 7,754 | 10,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,532 |
class ConvBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvBertConfig
load_tf_weights = load_tf_weights_in_convbert
base_model_prefix = "convbert"
sup... | class_definition | 10,683 | 11,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,533 |
class SeparableConv1D(nn.Module):
"""This class implements separable convolution, i.e. a depthwise and a pointwise layer"""
def __init__(self, config, input_filters, output_filters, kernel_size, **kwargs):
super().__init__()
self.depthwise = nn.Conv1d(
input_filters,
inp... | class_definition | 11,852 | 12,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,534 |
class ConvBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the nu... | class_definition | 12,835 | 19,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,535 |
class ConvBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
d... | class_definition | 19,104 | 19,714 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,536 |
class ConvBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = ConvBertSelfAttention(config)
self.output = ConvBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
he... | class_definition | 19,717 | 21,529 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,537 |
class GroupedLinearLayer(nn.Module):
def __init__(self, input_size, output_size, num_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.group_in_dim = self.input_size // self.num_groups
self.group_... | class_definition | 21,532 | 22,474 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,538 |
class ConvBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
if config.num_groups == 1:
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
else:
self.dense = GroupedLinearLayer(
input_size=config.hidden_size, ... | class_definition | 22,477 | 23,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,539 |
class ConvBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
if config.num_groups == 1:
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
else:
self.dense = GroupedLinearLayer(
input_size=config.intermediate_size, ... | class_definition | 23,275 | 24,113 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,540 |
class ConvBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = ConvBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = ... | class_definition | 24,116 | 26,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,541 |
class ConvBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ConvBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states... | class_definition | 26,797 | 29,663 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,542 |
class ConvBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.... | class_definition | 29,666 | 30,370 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,543 |
class ConvBertModel(ConvBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = ConvBertEmbeddings(config)
if config.embedding_size != config.hidden_size:
self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
... | class_definition | 33,811 | 38,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,544 |
class ConvBertGeneratorPredictions(nn.Module):
"""Prediction module for the generator, made up of two dense layers."""
def __init__(self, config):
super().__init__()
self.activation = get_activation("gelu")
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)... | class_definition | 38,031 | 38,713 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,545 |
class ConvBertForMaskedLM(ConvBertPreTrainedModel):
_tied_weights_keys = ["generator.lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.convbert = ConvBertModel(config)
self.generator_predictions = ConvBertGeneratorPredictions(config)
self.generator_lm_... | class_definition | 38,826 | 42,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,546 |
class ConvBertClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifie... | class_definition | 42,062 | 42,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,547 |
class ConvBertForSequenceClassification(ConvBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.convbert = ConvBertModel(config)
self.classifier = ConvBertClassificationHead(config)
... | class_definition | 43,156 | 46,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,548 |
class ConvBertForMultipleChoice(ConvBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.convbert = ConvBertModel(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and a... | class_definition | 47,159 | 50,709 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,549 |
class ConvBertForTokenClassification(ConvBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.convbert = ConvBertModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is ... | class_definition | 50,946 | 53,804 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,550 |
class ConvBertForQuestionAnswering(ConvBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.convbert = ConvBertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weight... | class_definition | 54,099 | 58,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/modeling_convbert.py | null | 7,551 |
class ConvBertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" ConvBERT 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 fo... | class_definition | 1,147 | 7,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convbert/tokenization_convbert_fast.py | null | 7,552 |
class MoshiDepthConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MoshiDepthDecoder`]. It is used to instantiate a
Moshi depth decoder model according to the specified arguments, defining the Moshi depth decoder config.
Configuration objects inherit from [... | class_definition | 843 | 7,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/configuration_moshi.py | null | 7,553 |
class MoshiConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MoshiModel`]. It is used to instantiate a
Moshi model according to the specified arguments, defining the audio encoder, Moshi depth decoder and Moshi decoder
configs. Instantiating a configuration... | class_definition | 7,247 | 16,001 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/configuration_moshi.py | null | 7,554 |
class MoshiConditionalGenerationGenerateOutput(ModelOutput):
"""
Outputs of [`MoshiForConditionalConditionalGeneration.generate`].
Args:
audio_sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, 1, sequence_length)`, *optional*):
The generated audio waveforms.
... | class_definition | 1,987 | 5,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,555 |
class MoshiCausalLMOutputWithPast(ModelOutput):
"""
`MoshiForCausalLM` outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
logits (`torch.FloatTensor` of shape `(batch_size... | class_definition | 5,906 | 8,359 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,556 |
class MoshiConditionalGenerationOutputWithPast(ModelOutput):
"""
`MoshiForConditionalGeneration` outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `text_labels` is provided):
Text language modeling loss (for next-token prediction).
logits (`torc... | class_definition | 8,373 | 12,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,557 |
class MoshiUnconditionalInput(ModelOutput):
"""
Args:
input_ids (`torch.Tensor `of shape `(batch_size, sequence_length), *optional*):
The sequence used as a text prompt for the generation.
user_audio_codes (`torch.Tensor `of shape `(batch_size, num_codebooks, sequence_length), *optio... | class_definition | 12,273 | 13,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,558 |
class MoshiRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim)) # Ignore copy
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
# Ignore copy
... | class_definition | 13,696 | 14,247 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,559 |
class MoshiFlexibleLinear(nn.Module):
def __init__(self, input_size, output_size, num_layers):
super().__init__()
# Stack the weights for N layers into a single tensor (num_layers, output_size, input_size)
self.weight = nn.Parameter(torch.randn(num_layers, output_size, input_size))
def ... | class_definition | 14,294 | 16,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,560 |
class MoshiLinear(nn.Module):
def __init__(self, input_dim, output_dim, num_codebooks, use_flexible_linear=False):
super().__init__()
self.use_flexible_linear = use_flexible_linear
if not use_flexible_linear:
self.linear = nn.Linear(input_dim, output_dim, bias=False)
el... | class_definition | 16,572 | 17,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,561 |
class MoshiRotaryEmbedding(nn.Module):
def __init__(self, config: MoshiConfig, 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 | 17,268 | 20,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,562 |
class MoshiGatingMLP(nn.Module):
def __init__(self, config, use_flexible_linear=False):
super().__init__()
self.activation_fn = ACT2FN[config.hidden_act]
ffn_dim = config.ffn_dim
hidden_size = config.hidden_size
num_layers = config.num_codebooks if use_flexible_linear else 1... | class_definition | 22,334 | 23,570 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,563 |
class MoshiAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: MoshiConfig, layer_idx: Optional[int] = None, use_flexible_linear=False, use_rope=True):
super().__init__()
self.config = config
self.layer_idx = layer_idx
... | class_definition | 24,247 | 29,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,564 |
class MoshiFlashAttention2(MoshiAttention):
"""
Moshi flash attention module. This module inherits from `MoshiAttention` 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 with pa... | class_definition | 29,921 | 35,879 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,565 |
class MoshiSdpaAttention(MoshiAttention):
"""
Moshi attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`MoshiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
# Adapted from... | class_definition | 36,012 | 40,806 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,566 |
class MoshiDecoderLayer(nn.Module):
def __init__(self, config: MoshiConfig, layer_idx: int, use_flexible_linear: bool, use_rope=True):
super().__init__()
self.hidden_size = config.hidden_size
self.use_flexible_linear = use_flexible_linear
self.self_attn = MOSHI_ATTENTION_CLASSES[con... | class_definition | 40,949 | 44,664 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,567 |
class MoshiPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MoshiConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules =... | class_definition | 44,667 | 46,077 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,568 |
class MoshiDepthDecoder(MoshiPreTrainedModel, GenerationMixin):
"""
Transformer depth decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoshiTransformerLayer`]
Args:
config: MoshiConfig
"""
config_class = MoshiDepthConfig
def __init__(self, config: MoshiDepthC... | class_definition | 56,635 | 76,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,569 |
class MoshiModel(MoshiPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoshiDecoderLayer`]
Args:
config: MoshiConfig
"""
def __init__(self, config: MoshiConfig):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 76,646 | 90,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,570 |
class MoshiForCausalLM(MoshiPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["model.embed_tokens.weight", "lm_head.weight"]
# Copied from transformers.models.gemma.modeling_gemma.GemmaForCausalLM.__init__ with Gemma->Moshi
def __init__(self, config):
super().__init__(config)
self.mo... | class_definition | 91,037 | 97,053 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,571 |
class MoshiForConditionalGeneration(MoshiPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["decoder.model.embed_tokens.weight", "decoder.lm_head.weight"]
config_class = MoshiConfig
main_input_name = "input_ids"
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports... | class_definition | 97,235 | 137,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/moshi/modeling_moshi.py | null | 7,572 |
class HieraConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`HieraModel`]. It is used to instantiate a Hiera
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults wi... | class_definition | 882 | 9,290 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/configuration_hiera.py | null | 7,573 |
class HieraEncoderOutput(ModelOutput):
"""
Hiera encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 1,765 | 3,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,574 |
class HieraModelOutput(ModelOutput):
"""
Hiera model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the mo... | class_definition | 3,863 | 6,631 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,575 |
class HieraForImageClassificationOutput(ImageClassifierOutput):
"""
Hiera image classification outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, `optional`):
Loss value for the training task.
logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
P... | class_definition | 6,645 | 8,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,576 |
class HieraForPreTrainingOutput(ModelOutput):
"""
Class for HieraForPreTraining's outputs, with potential hidden states and attentions.
Args:
loss (`torch.FloatTensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length... | class_definition | 8,569 | 11,028 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,577 |
class HieraPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config, is... | class_definition | 11,031 | 15,214 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,578 |
class HieraEmbeddings(nn.Module):
"""
Construct position and patch embeddings.
"""
def __init__(self, config: HieraConfig, is_mae: bool = False) -> None:
super().__init__()
self.patch_stride = config.patch_stride
tokens_spatial_shape = [i // s for i, s in zip(config.image_size, ... | class_definition | 15,217 | 18,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,579 |
class HieraMaskUnitAttention(nn.Module):
"""
Computes either Mask Unit or Global Attention. Also is able to perform query pooling.
Note: this assumes the tokens have already been flattened and unrolled into mask units.
"""
def __init__(
self,
hidden_size: int,
hidden_size_o... | class_definition | 18,527 | 20,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,580 |
class HieraDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torc... | class_definition | 22,197 | 22,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,581 |
class HieraMlp(nn.Module):
def __init__(self, config, dim: int) -> None:
super().__init__()
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(dim, int(dim * config.mlp_ratio))
self.fc2 = nn.Linear(int(dim * config.mlp_ratio), dim)
def forward(self, hidden_state... | class_definition | 22,679 | 23,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,582 |
class HieraLayer(nn.Module):
def __init__(
self,
config,
hidden_size: int,
hidden_size_output: int,
num_heads: int,
drop_path: float = 0.0,
query_stride: int = 1,
window_size: int = 0,
use_mask_unit_attn: bool = False,
) -> None:
su... | class_definition | 23,218 | 25,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,583 |
class HieraStage(nn.Module):
def __init__(
self,
config,
depth: int,
hidden_size: int,
hidden_size_output: int,
num_heads: int,
drop_path: List[float],
query_stride: List[int],
window_size: int,
use_mask_unit_attn: bool,
stage_n... | class_definition | 25,610 | 27,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,584 |
class HieraEncoder(nn.Module):
def __init__(self, config: HieraConfig) -> None:
super().__init__()
total_depth = sum(config.depths)
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, total_depth)]
# query strides rule
cumul... | class_definition | 29,126 | 36,077 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,585 |
class HieraPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = HieraConfig
base_model_prefix = "hiera"
main_input_name = "pixel_values"
supports_gradient_checkpo... | class_definition | 38,989 | 40,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,586 |
class HieraPooler(nn.Module):
def __init__(self, config: HieraConfig):
super().__init__()
num_features = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
self.layernorm = nn.LayerNorm(num_features, eps=config.layer_norm_eps)
self.pooler = nn.AdaptiveAvg... | class_definition | 42,026 | 42,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,587 |
class HieraModel(HieraPreTrainedModel):
def __init__(self, config: HieraConfig, add_pooling_layer: bool = True, is_mae: bool = False):
super().__init__(config)
self.num_features = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
self.embeddings = HieraEmbeddin... | class_definition | 43,085 | 48,184 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,588 |
class HieraDecoder(nn.Module):
def __init__(self, config: HieraConfig):
super().__init__()
num_features = int(config.embed_dim * config.embed_dim_multiplier ** (len(config.depths) - 1))
tokens_spatial_shape = [i // s for i, s in zip(config.image_size, config.patch_stride)]
self.token... | class_definition | 48,187 | 52,557 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,589 |
class HieraMultiScaleHead(nn.Module):
def __init__(self, config: HieraConfig):
super().__init__()
self.mask_unit_spatial_shape_final = [
i // s ** (config.num_query_pool) for i, s in zip(config.masked_unit_size, config.query_stride)
]
self.stage_dimensions = [
... | class_definition | 52,560 | 55,177 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,590 |
class HieraForPreTraining(HieraPreTrainedModel):
def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
# Encoder
self.hiera = HieraModel(config, add_pooling_layer=False, is_mae=True)
self.encoder_norm = nn.LayerNorm(self.hiera.num_features, eps=config.layer_norm_e... | class_definition | 55,554 | 61,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,591 |
class HieraForImageClassification(HieraPreTrainedModel):
def __init__(self, config: HieraConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.hiera = HieraModel(config, add_pooling_layer=True, is_mae=False)
# Classifier head
self.classifier = (... | class_definition | 61,702 | 65,761 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,592 |
class HieraBackbone(HieraPreTrainedModel, BackboneMixin):
def __init__(self, config: HieraConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embed_dim] + [
int(config.embed_dim * config.embed_dim_multiplier**i) for i in range(len(config.... | class_definition | 65,906 | 69,661 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/hiera/modeling_hiera.py | null | 7,593 |
class MobileNetV2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MobileNetV2Model`]. It is used to instantiate a
MobileNetV2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 914 | 6,159 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py | null | 7,594 |
class MobileNetV2OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict([("pixel_values", {0: "batch"})])
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
if self.ta... | class_definition | 6,162 | 6,766 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/configuration_mobilenet_v2.py | null | 7,595 |
class MobileNetV2ImageProcessor(BaseImageProcessor):
r"""
Constructs a MobileNetV2 image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in... | class_definition | 1,448 | 17,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/image_processing_mobilenet_v2.py | null | 7,596 |
class MobileNetV2ConvLayer(nn.Module):
def __init__(
self,
config: MobileNetV2Config,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
groups: int = 1,
bias: bool = False,
dilation: int = 1,
use_normalization: boo... | class_definition | 12,797 | 15,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,597 |
class MobileNetV2InvertedResidual(nn.Module):
def __init__(
self, config: MobileNetV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1
) -> None:
super().__init__()
expanded_channels = make_divisible(
int(round(in_channels * config.expand_ratio)), c... | class_definition | 15,166 | 16,663 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,598 |
class MobileNetV2Stem(nn.Module):
def __init__(self, config: MobileNetV2Config, in_channels: int, expanded_channels: int, out_channels: int) -> None:
super().__init__()
# The very first layer is a regular 3x3 convolution with stride 2 that expands to 32 channels.
# All other expansion layer... | class_definition | 16,666 | 18,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py | null | 7,599 |
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