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 WhisperEncoder(WhisperPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`WhisperEncoderLayer`].
Args:
config: WhisperConfig
"""
def __init__(self, config: WhisperConfig):
super().__init__(config)
... | class_definition | 46,151 | 53,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,900 |
class WhisperDecoder(WhisperPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`WhisperDecoderLayer`]
Args:
config: WhisperConfig
"""
main_input_name = "input_ids"
def __init__(self, config: WhisperConfig):
super().__init__... | class_definition | 53,288 | 73,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,901 |
class WhisperModel(WhisperPreTrainedModel):
def __init__(self, config: WhisperConfig):
super().__init__(config)
self.encoder = WhisperEncoder(config)
self.decoder = WhisperDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_emb... | class_definition | 73,260 | 80,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,902 |
class WhisperForConditionalGeneration(WhisperGenerationMixin, WhisperPreTrainedModel):
base_model_prefix = "model"
_tied_weights_keys = ["proj_out.weight"]
def __init__(self, config: WhisperConfig):
super().__init__(config)
self.model = WhisperModel(config)
self.proj_out = nn.Linear... | class_definition | 81,075 | 91,343 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,903 |
class WhisperDecoderWrapper(WhisperPreTrainedModel):
"""
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 | 91,346 | 92,002 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,904 |
class WhisperForCausalLM(WhisperPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["proj_out.weight"]
main_input_name = "input_ids"
def __init__(self, config):
super().__init__(config)
config.is_encoder_decoder = False
self.model = WhisperDecoderWrapper(config)
self.p... | class_definition | 92,191 | 102,515 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,905 |
class WhisperForAudioClassification(WhisperPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.encoder = WhisperEncoder(config)
num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings
if config.use_weighted_layer_sum:
se... | class_definition | 102,751 | 108,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,906 |
class TFWhisperPositionalEmbedding(keras.layers.Layer):
def __init__(
self,
num_positions: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
embedding_initializer=None,
**kwargs,
):
super().__init__(**kwargs)
self.num_positions = num_posi... | class_definition | 4,873 | 5,928 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,907 |
class TFWhisperAttention(keras.layers.Layer):
"""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,
**kwargs,
):
... | class_definition | 5,931 | 13,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,908 |
class TFWhisperEncoderLayer(keras.layers.Layer):
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFWhisperAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, ... | class_definition | 13,849 | 17,538 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,909 |
class TFWhisperDecoderLayer(keras.layers.Layer):
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFWhisperAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | class_definition | 17,669 | 24,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,910 |
class TFWhisperPreTrainedModel(TFPreTrainedModel):
config_class = WhisperConfig
base_model_prefix = "model"
main_input_name = "input_features"
def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor) -> int:
"""
Computes the output length of the convolutional layers
... | class_definition | 24,506 | 25,790 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,911 |
class TFWhisperEncoder(keras.layers.Layer):
config_class = WhisperConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFWhisperEncoderLayer`].
Args:
config: WhisperConfig
embed_tokens (TFWhisperEmbedding): output embedding
... | class_definition | 32,214 | 39,533 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,912 |
class TFWhisperDecoder(keras.layers.Layer):
config_class = WhisperConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFWhisperDecoderLayer`]
Args:
config: WhisperConfig
"""
def __init__(self, config: WhisperConfig, **kwargs):
super().... | class_definition | 39,556 | 52,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,913 |
class TFWhisperMainLayer(keras.layers.Layer):
config_class = WhisperConfig
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.encoder = TFWhisperEncoder(config, name="encoder")
self.decoder = TFWhisperDecoder(config, name="d... | class_definition | 52,453 | 57,714 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,914 |
class TFWhisperModel(TFWhisperPreTrainedModel):
def __init__(self, config: WhisperConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = TFWhisperMainLayer(config, name="model")
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embedd... | class_definition | 57,864 | 62,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,915 |
class TFWhisperForConditionalGeneration(TFWhisperPreTrainedModel, TFCausalLanguageModelingLoss):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"encoder.version",
r"decoder.version",
r"proj_out.weight",
]
_keys_to_ignore_on_save = [
r"proj_out.weight",
... | class_definition | 62,886 | 84,764 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py | null | 9,916 |
class WhisperTokenizer(PreTrainedTokenizer):
"""
Construct a Whisper tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`):
... | class_definition | 5,272 | 37,861 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py | null | 9,917 |
class WhisperConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a
Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a simi... | class_definition | 2,270 | 14,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py | null | 9,918 |
class WhisperOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
]
)
if self.use_past:
... | class_definition | 14,906 | 16,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py | null | 9,919 |
class WhisperGenerationMixin(GenerationMixin):
def _extract_token_timestamps(
self, generate_outputs, alignment_heads, time_precision=0.02, num_frames=None, num_input_ids=None
):
"""
Calculates token-level timestamps using the encoder-decoder cross-attentions and dynamic time-warping (DT... | class_definition | 8,013 | 102,444 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py | null | 9,920 |
class WhisperFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Whisper feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information ... | class_definition | 1,073 | 14,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py | null | 9,921 |
class FlaxWhisperAttention(nn.Module):
config: WhisperConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_di... | class_definition | 12,188 | 18,992 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,922 |
class FlaxWhisperEncoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=sel... | class_definition | 19,097 | 21,387 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,923 |
class FlaxWhisperEncoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxWhisperEncoderCheckpointLayer = remat(FlaxWhisperEncoder... | class_definition | 21,390 | 23,758 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,924 |
class FlaxWhisperDecoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=sel... | class_definition | 23,863 | 27,421 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,925 |
class FlaxWhisperDecoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxWhisperDecoderCheckpointLayer = remat(FlaxWhisperDecoder... | class_definition | 27,424 | 30,458 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,926 |
class FlaxWhisperEncoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.conv1 = nn.Conv(
self.config.d_model,
kernel_size=(3,),
padding=1,
kernel_init=jax.nn.init... | class_definition | 30,461 | 33,973 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,927 |
class FlaxWhisperDecoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.d_model, dtype=self.dtype)
self.embed_positions = nn.Embed(self.config... | class_definition | 33,976 | 36,549 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,928 |
class FlaxWhisperModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
... | class_definition | 36,552 | 38,768 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,929 |
class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel):
config_class = WhisperConfig
base_model_prefix: str = "model"
main_input_name = "input_features"
module_class: nn.Module = None
def __init__(
self,
config: WhisperConfig,
input_shape: Tuple[int] = None,
seed: int... | class_definition | 38,771 | 52,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,930 |
class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxWhisperModule | class_definition | 53,150 | 53,331 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,931 |
class FlaxWhisperForConditionalGenerationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.model = FlaxWhisperModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_c... | class_definition | 53,445 | 55,901 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,932 |
class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_clas... | class_definition | 56,003 | 66,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,933 |
class FlaxWhisperForAudioClassificationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_... | class_definition | 67,406 | 69,747 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,934 |
class FlaxWhisperForAudioClassification(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForAudioClassificationModule
dtype: jnp.dtype = jnp.float32
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input... | class_definition | 69,860 | 72,224 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py | null | 9,935 |
class BasicTextNormalizer:
def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
self.split_letters = split_letters
def __call__(self, s: str):
s = s.lower()
s = re.su... | class_definition | 2,060 | 2,762 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py | null | 9,936 |
class EnglishNumberNormalizer:
"""
Convert any spelled-out numbers into arabic numbers, while handling:
- remove any commas
- keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
- spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
- spell out `one` and `... | class_definition | 2,765 | 18,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py | null | 9,937 |
class EnglishSpellingNormalizer:
"""
Applies British-American spelling mappings as listed in [1].
[1] https://www.tysto.com/uk-us-spelling-list.html
"""
def __init__(self, english_spelling_mapping):
self.mapping = english_spelling_mapping
def __call__(self, s: str):
return " "... | class_definition | 18,991 | 19,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py | null | 9,938 |
class EnglishTextNormalizer:
def __init__(self, english_spelling_mapping):
self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
self.replacers = {
# common contractions
r"\bwon't\b": "will not",
r"\bcan't\b": "can not",
r"\blet's\b": "let us",
... | class_definition | 19,371 | 22,821 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py | null | 9,939 |
class LlavaOnevisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaOnevisionForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
... | class_definition | 802 | 7,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py | null | 9,940 |
class LlavaOnevisionProcessorKwargs(ProcessingKwargs, total=False):
# see processing_utils.ProcessingKwargs documentation for usage.
_defaults = {
"text_kwargs": {
"padding": False,
},
"image_kwargs": {},
"video_kwargs": {},
} | class_definition | 1,161 | 1,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py | null | 9,941 |
class LlavaOnevisionProcessor(ProcessorMixin):
r"""
Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
[`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`Ll... | class_definition | 1,446 | 15,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py | null | 9,942 |
class LlavaOnevisionImageProcessor(BaseImageProcessor):
r"""
Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (... | class_definition | 4,853 | 34,194 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py | null | 9,943 |
class LlavaOnevisionVideoProcessor(BaseImageProcessor):
r"""
Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (... | class_definition | 1,996 | 16,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py | null | 9,944 |
class LlavaOnevisionCausalLMOutputWithPast(ModelOutput):
"""
Base class for LlavaOnevision causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token predicti... | class_definition | 6,173 | 9,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py | null | 9,945 |
class LlavaOnevisionMultiModalProjector(nn.Module):
def __init__(self, config: LlavaOnevisionConfig):
super().__init__()
self.linear_1 = nn.Linear(
config.vision_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
)
self.act = ACT2FN[... | class_definition | 9,259 | 9,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py | null | 9,946 |
class LlavaOnevisionPreTrainedModel(PreTrainedModel):
config_class = LlavaOnevisionConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlavaOnevisionVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_su... | class_definition | 11,075 | 12,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py | null | 9,947 |
class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin):
def __init__(self, config: LlavaOnevisionConfig):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = LlavaOnevisionMultiModalProje... | class_definition | 19,036 | 41,606 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py | null | 9,948 |
class Qwen2AudioCausalLMOutputWithPast(ModelOutput):
"""
Base class for Qwen2Audio causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
... | class_definition | 1,622 | 4,066 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,949 |
class Qwen2AudioAttention(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 | 4,170 | 9,993 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,950 |
class Qwen2AudioFlashAttention2(Qwen2AudioAttention):
"""
Qwen2Audio flash attention module. This module inherits from `Qwen2AudioAttention` 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 attent... | class_definition | 10,103 | 16,521 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,951 |
class Qwen2AudioSdpaAttention(Qwen2AudioAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: O... | class_definition | 16,629 | 22,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,952 |
class Qwen2AudioEncoderLayer(nn.Module):
def __init__(self, config: Qwen2AudioConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = QWEN2AUDIO_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_at... | class_definition | 22,318 | 25,468 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,953 |
class Qwen2AudioPreTrainedModel(PreTrainedModel):
config_class = Qwen2AudioConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Qwen2AudioAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True... | class_definition | 26,510 | 27,575 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,954 |
class Qwen2AudioEncoder(Qwen2AudioPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`Qwen2AudioEncoderLayer`].
Args:
config: Qwen2AudioEncoderConfig
"""
# Ignore copy
config_class = Qwen2AudioEncoderConfig
ma... | class_definition | 28,723 | 36,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,955 |
class Qwen2AudioMultiModalProjector(nn.Module):
def __init__(self, config: Qwen2AudioConfig):
super().__init__()
self.linear = nn.Linear(config.audio_config.d_model, config.text_config.hidden_size, bias=True)
def forward(self, audio_features):
hidden_states = self.linear(audio_features)... | class_definition | 36,871 | 37,220 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,956 |
class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel, GenerationMixin):
def __init__(self, config: Qwen2AudioConfig):
super().__init__(config)
self.audio_tower = AutoModel.from_config(config.audio_config)
self.multi_modal_projector = Qwen2AudioMultiModalProjector(config)
... | class_definition | 42,290 | 69,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py | null | 9,957 |
class Qwen2AudioProcessor(ProcessorMixin):
r"""
Constructs a Qwen2Audio processor which wraps a Qwen2Audio feature extractor and a Qwen2Audio tokenizer into a single processor.
[`Qwen2AudioProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`Qwen2TokenizerFast`]. See the
[`~Q... | class_definition | 895 | 11,922 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/processing_qwen2_audio.py | null | 9,958 |
class Qwen2AudioEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2AudioEncoder`]. It is used to instantiate a
Qwen2-Audio audio encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the... | class_definition | 888 | 5,305 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py | null | 9,959 |
class Qwen2AudioConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2AudioForConditionalGeneration`]. It is used to instantiate an
Qwen2-Audio model according to the specified arguments, defining the model architecture. Instantiating a configuration
with ... | class_definition | 5,308 | 8,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py | null | 9,960 |
class RemBertEmbeddings(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.input_embedding_size, padding_idx=config.pad_token_id
... | class_definition | 5,221 | 7,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,961 |
class RemBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hi... | class_definition | 7,647 | 8,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,962 |
class RemBertSelfAttention(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 num... | class_definition | 8,212 | 13,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,963 |
class RemBertSelfOutput(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)
de... | class_definition | 13,500 | 14,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,964 |
class RemBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = RemBertSelfAttention(config)
self.output = RemBertSelfOutput(config)
self.pruned_heads = set()
# Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads
def pru... | class_definition | 14,112 | 16,265 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,965 |
class RemBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.int... | class_definition | 16,357 | 16,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,966 |
class RemBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 17,011 | 17,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,967 |
class RemBertLayer(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 = RemBertAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = co... | class_definition | 17,625 | 21,672 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,968 |
class RemBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.embedding_hidden_mapping_in = nn.Linear(config.input_embedding_size, config.hidden_size)
self.layer = nn.ModuleList([RemBertLayer(config) for _ in range(config.num_hidden_layers... | class_definition | 21,675 | 25,601 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,969 |
class RemBertPredictionHeadTransform(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.t... | class_definition | 25,704 | 26,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,970 |
class RemBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.output_embedding_size)
self.decoder = nn.Linear(config.output_embedding_size, config.vocab_size)
self.activation = ACT2FN[config.hidden_act]
... | class_definition | 26,410 | 27,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,971 |
class RemBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RemBertLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 27,220 | 27,540 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,972 |
class RemBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RemBertConfig
load_tf_weights = load_tf_weights_in_rembert
base_model_prefix = "rembert"
support... | class_definition | 27,543 | 28,705 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,973 |
class RemBertModel(RemBertPreTrainedModel):
"""
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/... | class_definition | 32,138 | 40,955 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,974 |
class RemBertForMaskedLM(RemBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `RemBertForMaskedLM` make sure `config.is_decoder... | class_definition | 41,066 | 45,324 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,975 |
class RemBertForCausalLM(RemBertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `RemBertForCausalLM` as a standalone, add `is... | class_definition | 45,461 | 51,933 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,976 |
class RemBertForSequenceClassification(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.rembert = RemBertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(c... | class_definition | 52,161 | 56,007 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,977 |
class RemBertForMultipleChoice(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.rembert = RemBertModel(config)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights... | class_definition | 56,244 | 59,760 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,978 |
class RemBertForTokenClassification(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.rembert = RemBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.c... | class_definition | 59,995 | 62,721 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,979 |
class RemBertForQuestionAnswering(RemBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.rembert = RemBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
... | class_definition | 63,014 | 67,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_rembert.py | null | 9,980 |
class RemBertTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" RemBert tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
tokenizer inherits from [`PreTrainedTokeni... | class_definition | 1,211 | 9,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | null | 9,981 |
class RemBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`RemBertModel`]. It is used to instantiate an
RemBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | class_definition | 899 | 6,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py | null | 9,982 |
class RemBertOnnxConfig(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 | 6,662 | 7,239 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/configuration_rembert.py | null | 9,983 |
class RemBertTokenizer(PreTrainedTokenizer):
"""
Construct a RemBERT 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 reg... | class_definition | 992 | 10,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/tokenization_rembert.py | null | 9,984 |
class TFRemBertEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.input_embedding_size = config.input_embedding_size
... | class_definition | 1,895 | 5,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,985 |
class TFRemBertSelfAttention(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **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 ... | class_definition | 5,317 | 12,143 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,986 |
class TFRemBertSelfOutput(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Lay... | class_definition | 12,238 | 13,571 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,987 |
class TFRemBertAttention(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFRemBertSelfAttention(config, name="self")
self.dense_output = TFRemBertSelfOutput(config, name="output")
def prune_heads(self, heads):
... | class_definition | 13,665 | 15,509 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,988 |
class TFRemBertIntermediate(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | class_definition | 15,606 | 16,634 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,989 |
class TFRemBertOutput(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNo... | class_definition | 16,725 | 18,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,990 |
class TFRemBertLayer(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFRemBertAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
i... | class_definition | 18,150 | 22,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,991 |
class TFRemBertEncoder(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embedding_hidden_mapping_in = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config... | class_definition | 22,900 | 26,529 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,992 |
class TFRemBertPooler(keras.layers.Layer):
def __init__(self, config: RemBertConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
... | class_definition | 26,620 | 27,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,993 |
class TFRemBertLMPredictionHead(keras.layers.Layer):
def __init__(self, config: RemBertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.initializer_range = config.initializer_range
self.output_embedding_size = config.outpu... | class_definition | 27,598 | 30,393 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,994 |
class TFRemBertMLMHead(keras.layers.Layer):
def __init__(self, config: RemBertConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFRemBertLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Tensor... | class_definition | 30,485 | 31,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,995 |
class TFRemBertMainLayer(keras.layers.Layer):
config_class = RemBertConfig
def __init__(self, config: RemBertConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.is_decoder = config.is_decoder
self.embeddings = TFRemBertEmbedd... | class_definition | 31,220 | 41,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,996 |
class TFRemBertPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RemBertConfig
base_model_prefix = "rembert" | class_definition | 41,058 | 41,321 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,997 |
class TFRemBertModel(TFRemBertPreTrainedModel):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.rembert = TFRemBertMainLayer(config, name="rembert")
@unpack_inputs
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING... | class_definition | 47,306 | 51,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,998 |
class TFRemBertForMaskedLM(TFRemBertPreTrainedModel, TFMaskedLanguageModelingLoss):
def __init__(self, config: RemBertConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.warning(
"If you want to use `TFRemBertForMaskedLM` ... | class_definition | 51,303 | 54,826 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/rembert/modeling_tf_rembert.py | null | 9,999 |
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