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 FlaxRobertaPreLayerNormSelfOutput(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initialize... | class_definition | 16,097 | 16,833 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,700 |
class FlaxRobertaPreLayerNormAttention(nn.Module):
config: RobertaPreLayerNormConfig
causal: bool = False
dtype: jnp.dtype = jnp.float32
def setup(self):
self.self = FlaxRobertaPreLayerNormSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
self.output = FlaxRobertaPreLayer... | class_definition | 16,836 | 18,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,701 |
class FlaxRobertaPreLayerNormIntermediate(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.dense = nn.Dense(
self... | class_definition | 18,479 | 19,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,702 |
class FlaxRobertaPreLayerNormOutput(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_ra... | class_definition | 19,236 | 19,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,703 |
class FlaxRobertaPreLayerNormLayer(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxRobertaPreLayerNormAttention(self.config, causal=self.config.is_decoder, dtype=self.dtype)
self.intermediate ... | class_definition | 20,082 | 22,312 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,704 |
class FlaxRobertaPreLayerNormLayerCollection(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxRobertaPreLayerNormCheckpointLayer = re... | class_definition | 22,428 | 25,526 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,705 |
class FlaxRobertaPreLayerNormEncoder(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.layer = FlaxRobertaPreLayerNormLayerCollection(
self.config,
dtype... | class_definition | 25,634 | 26,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,706 |
class FlaxRobertaPreLayerNormPooler(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_ra... | class_definition | 27,023 | 27,572 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,707 |
class FlaxRobertaPreLayerNormLMHead(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
bias_init: Callable[..., np.ndarray] = jax.nn.initializers.zeros
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
... | class_definition | 27,691 | 29,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,708 |
class FlaxRobertaPreLayerNormClassificationHead(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(self.config.init... | class_definition | 29,169 | 30,441 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,709 |
class FlaxRobertaPreLayerNormPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RobertaPreLayerNormConfig
base_model_prefix = "roberta_prelayernorm"
module_cla... | class_definition | 30,629 | 38,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,710 |
class FlaxRobertaPreLayerNormModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
gradient_checkpointing: bool = False
def setup(self):
self.embeddings = FlaxRobertaPreLayerNormEmbeddings(self.conf... | class_definition | 38,669 | 41,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,711 |
class FlaxRobertaPreLayerNormModel(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormModule | class_definition | 41,913 | 42,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,712 |
class FlaxRobertaPreLayerNormForMaskedLMModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(
config=self.config,
add_pooling_la... | class_definition | 42,347 | 44,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,713 |
class FlaxRobertaPreLayerNormForMaskedLM(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormForMaskedLMModule | class_definition | 44,461 | 44,602 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,714 |
class FlaxRobertaPreLayerNormForSequenceClassificationModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(
config=self.config,
... | class_definition | 44,951 | 46,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,715 |
class FlaxRobertaPreLayerNormForSequenceClassification(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormForSequenceClassificationModule | class_definition | 46,941 | 47,110 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,716 |
class FlaxRobertaPreLayerNormForMultipleChoiceModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(
config=self.config,
dtype=se... | class_definition | 47,445 | 49,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,717 |
class FlaxRobertaPreLayerNormForMultipleChoice(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormForMultipleChoiceModule | class_definition | 50,025 | 50,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,718 |
class FlaxRobertaPreLayerNormForTokenClassificationModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(
config=self.config,
dty... | class_definition | 50,679 | 52,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,719 |
class FlaxRobertaPreLayerNormForTokenClassification(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormForTokenClassificationModule | class_definition | 52,936 | 53,099 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,720 |
class FlaxRobertaPreLayerNormForQuestionAnsweringModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(
config=self.config,
dtype... | class_definition | 53,431 | 55,213 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,721 |
class FlaxRobertaPreLayerNormForQuestionAnswering(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormForQuestionAnsweringModule | class_definition | 55,661 | 55,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,722 |
class FlaxRobertaPreLayerNormForCausalLMModule(nn.Module):
config: RobertaPreLayerNormConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self):
self.roberta_prelayernorm = FlaxRobertaPreLayerNormModule(
config=self.config,
add_pooling_la... | class_definition | 56,147 | 58,439 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,723 |
class FlaxRobertaPreLayerNormForCausalLM(FlaxRobertaPreLayerNormPreTrainedModel):
module_class = FlaxRobertaPreLayerNormForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length =... | class_definition | 58,800 | 60,373 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/roberta_prelayernorm/modeling_flax_roberta_prelayernorm.py | null | 8,724 |
class ByteRewriter:
"""
Byte rewriter class for MyT5 tokenizer.
This class is used to rewrite bytes using a hash tree. The hash tree is constructed from a set of rewriting rules.
Args:
rewriting_rules (`str` or `Dict[str, str]`):
A path to a json file containing the rewriting rules ... | class_definition | 941 | 4,597 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/myt5/tokenization_myt5.py | null | 8,725 |
class MyT5Tokenizer(PreTrainedTokenizer):
"""
Construct a MyT5 tokenizer.
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 (`str`): The file con... | class_definition | 4,600 | 15,524 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/myt5/tokenization_myt5.py | null | 8,726 |
class Wav2Vec2BertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Wav2Vec2BertModel`]. It is used to
instantiate an Wav2Vec2Bert model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults w... | class_definition | 813 | 18,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/configuration_wav2vec2_bert.py | null | 8,727 |
class Wav2Vec2BertProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {} | class_definition | 1,021 | 1,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/processing_wav2vec2_bert.py | null | 8,728 |
class Wav2Vec2BertProcessor(ProcessorMixin):
r"""
Constructs a Wav2Vec2-BERT processor which wraps a Wav2Vec2-BERT feature extractor and a Wav2Vec2 CTC tokenizer into a single
processor.
[`Wav2Vec2Processor`] offers all the functionalities of [`SeamlessM4TFeatureExtractor`] and [`PreTrainedTokenizer`].... | class_definition | 1,108 | 7,843 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/processing_wav2vec2_bert.py | null | 8,729 |
class Wav2Vec2BertRotaryPositionalEmbedding(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_attention_... | class_definition | 10,179 | 11,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,730 |
class Wav2Vec2BertRelPositionalEmbedding(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).exp... | class_definition | 11,955 | 14,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,731 |
class Wav2Vec2BertFeatureProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.layer_norm = nn.LayerNorm(config.feature_projection_input_dim, eps=config.layer_norm_eps)
self.projection = nn.Linear(config.feature_projection_input_dim, config.hidden_size)
self.drop... | class_definition | 14,351 | 15,039 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,732 |
class Wav2Vec2BertFeedForward(nn.Module):
def __init__(self, config, act_fn=None, hidden_size=None):
super().__init__()
act_fn = act_fn if act_fn is not None else config.hidden_act
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
self.intermediate_dropout ... | class_definition | 15,042 | 16,186 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,733 |
class Wav2Vec2BertConvolutionModule(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 for 'S... | class_definition | 16,189 | 18,900 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,734 |
class Wav2Vec2BertSelfAttention(nn.Module):
"""Construct an Wav2Vec2BertSelfAttention object.
Can be enhanced with rotary or relative position embeddings.
"""
def __init__(self, config, is_adapter_attention=False):
super().__init__()
hidden_size = config.hidden_size if not is_adapter_at... | class_definition | 18,903 | 27,718 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,735 |
class Wav2Vec2BertEncoderLayer(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.LayerN... | class_definition | 27,721 | 30,312 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,736 |
class Wav2Vec2BertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
if config.position_embeddings_type == "relative":
self.embed_positions = Wav2Vec2BertRelPositionalEmbedding(config)
elif config.position_embeddings_type == "rotary":... | class_definition | 30,315 | 34,230 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,737 |
class Wav2Vec2BertAdapter(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_layer_... | class_definition | 34,233 | 36,167 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,738 |
class Wav2Vec2BertAdapterLayer(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.output_hidden_size
dropout = config.conformer_conv_dropout
self.kernel_size = config.adapter_kernel_size
self.stride = config.adapter_stride
# 1. residual co... | class_definition | 36,170 | 39,586 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,739 |
class Wav2Vec2BertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Wav2Vec2BertConfig
base_model_prefix = "wav2vec2_bert"
main_input_name = "input_features"
s... | class_definition | 39,804 | 43,442 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,740 |
class Wav2Vec2BertModel(Wav2Vec2BertPreTrainedModel):
def __init__(self, config: Wav2Vec2BertConfig):
super().__init__(config)
self.config = config
self.feature_projection = Wav2Vec2BertFeatureProjection(config)
# model only needs masking vector if mask prob is > 0.0
if conf... | class_definition | 46,195 | 51,663 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,741 |
class Wav2Vec2BertForCTC(Wav2Vec2BertPreTrainedModel):
# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForCTC.__init__ with Wav2Vec2Conformer->Wav2Vec2Bert,WAV2VEC2_CONFORMER->WAV2VEC2_BERT,wav2vec2_conformer->wav2vec2_bert
def __init__(self, config, target_lang... | class_definition | 51,844 | 56,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,742 |
class Wav2Vec2BertForSequenceClassification(Wav2Vec2BertPreTrainedModel):
# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification.__init__ with Wav2Vec2->Wav2Vec2Bert,wav2vec2->wav2vec2_bert
def __init__(self, config):
super().__init__(config)
if hasattr(c... | class_definition | 56,843 | 61,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,743 |
class Wav2Vec2BertForAudioFrameClassification(Wav2Vec2BertPreTrainedModel):
# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForAudioFrameClassification.__init__ with Wav2Vec2Conformer->Wav2Vec2Bert,WAV2VEC2_CONFORMER->WAV2VEC2_BERT,wav2vec2_conformer->wav2vec2_bert
... | class_definition | 61,650 | 65,914 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,744 |
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 | 65,992 | 66,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,745 |
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 | 66,942 | 68,372 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,746 |
class Wav2Vec2BertForXVector(Wav2Vec2BertPreTrainedModel):
# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerForXVector.__init__ with Wav2Vec2Conformer->Wav2Vec2Bert,WAV2VEC2_CONFORMER->WAV2VEC2_BERT,wav2vec2_conformer->wav2vec2_bert
def __init__(self, config):
... | class_definition | 68,559 | 74,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py | null | 8,747 |
class LevitForImageClassificationWithTeacherOutput(ModelOutput):
"""
Output type of [`LevitForImageClassificationWithTeacher`].
Args:
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Prediction scores as the average of the `cls_logits` and `distillation_logits`.
... | class_definition | 1,628 | 3,066 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,748 |
class LevitConvEmbeddings(nn.Module):
"""
LeViT Conv Embeddings with Batch Norm, used in the initial patch embedding layer.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, bn_weight_init=1
):
super().__init__()
self.conv... | class_definition | 3,069 | 3,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,749 |
class LevitPatchEmbeddings(nn.Module):
"""
LeViT patch embeddings, for final embeddings to be passed to transformer blocks. It consists of multiple
`LevitConvEmbeddings`.
"""
def __init__(self, config):
super().__init__()
self.embedding_layer_1 = LevitConvEmbeddings(
con... | class_definition | 3,752 | 5,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,750 |
class MLPLayerWithBN(nn.Module):
def __init__(self, input_dim, output_dim, bn_weight_init=1):
super().__init__()
self.linear = nn.Linear(in_features=input_dim, out_features=output_dim, bias=False)
self.batch_norm = nn.BatchNorm1d(output_dim)
def forward(self, hidden_state):
hidd... | class_definition | 5,669 | 6,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,751 |
class LevitSubsample(nn.Module):
def __init__(self, stride, resolution):
super().__init__()
self.stride = stride
self.resolution = resolution
def forward(self, hidden_state):
batch_size, _, channels = hidden_state.shape
hidden_state = hidden_state.view(batch_size, self.r... | class_definition | 6,148 | 6,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,752 |
class LevitAttention(nn.Module):
def __init__(self, hidden_sizes, key_dim, num_attention_heads, attention_ratio, resolution):
super().__init__()
self.num_attention_heads = num_attention_heads
self.scale = key_dim**-0.5
self.key_dim = key_dim
self.attention_ratio = attention_r... | class_definition | 6,627 | 9,611 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,753 |
class LevitAttentionSubsample(nn.Module):
def __init__(
self,
input_dim,
output_dim,
key_dim,
num_attention_heads,
attention_ratio,
stride,
resolution_in,
resolution_out,
):
super().__init__()
self.num_attention_heads = num_... | class_definition | 9,614 | 13,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,754 |
class LevitMLPLayer(nn.Module):
"""
MLP Layer with `2X` expansion in contrast to ViT with `4X`.
"""
def __init__(self, input_dim, hidden_dim):
super().__init__()
self.linear_up = MLPLayerWithBN(input_dim, hidden_dim)
self.activation = nn.Hardswish()
self.linear_down = ML... | class_definition | 13,303 | 13,883 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,755 |
class LevitResidualLayer(nn.Module):
"""
Residual Block for LeViT
"""
def __init__(self, module, drop_rate):
super().__init__()
self.module = module
self.drop_rate = drop_rate
def forward(self, hidden_state):
if self.training and self.drop_rate > 0:
rnd ... | class_definition | 13,886 | 14,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,756 |
class LevitStage(nn.Module):
"""
LeViT Stage consisting of `LevitMLPLayer` and `LevitAttention` layers.
"""
def __init__(
self,
config,
idx,
hidden_sizes,
key_dim,
depths,
num_attention_heads,
attention_ratio,
mlp_ratio,
do... | class_definition | 14,572 | 16,921 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,757 |
class LevitEncoder(nn.Module):
"""
LeViT Encoder consisting of multiple `LevitStage` stages.
"""
def __init__(self, config):
super().__init__()
self.config = config
resolution = self.config.image_size // self.config.patch_size
self.stages = []
self.config.down_op... | class_definition | 16,924 | 18,570 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,758 |
class LevitClassificationLayer(nn.Module):
"""
LeViT Classification Layer
"""
def __init__(self, input_dim, output_dim):
super().__init__()
self.batch_norm = nn.BatchNorm1d(input_dim)
self.linear = nn.Linear(input_dim, output_dim)
def forward(self, hidden_state):
hi... | class_definition | 18,573 | 19,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,759 |
class LevitPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LevitConfig
base_model_prefix = "levit"
main_input_name = "pixel_values"
_no_split_modules = ["Lev... | class_definition | 19,003 | 19,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,760 |
class LevitModel(LevitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.patch_embeddings = LevitPatchEmbeddings(config)
self.encoder = LevitEncoder(config)
# Initialize weights and apply final processing
self.post_init()
... | class_definition | 21,314 | 23,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,761 |
class LevitForImageClassification(LevitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.num_labels = config.num_labels
self.levit = LevitModel(config)
# Classifier head
self.classifier = (
LevitClassificatio... | class_definition | 23,489 | 26,812 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,762 |
class LevitForImageClassificationWithTeacher(LevitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.num_labels = config.num_labels
self.levit = LevitModel(config)
# Classifier head
self.classifier = (
LevitCl... | class_definition | 27,247 | 29,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/modeling_levit.py | null | 8,763 |
class LevitImageProcessor(BaseImageProcessor):
r"""
Constructs a LeViT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the
`do_resize` parameter in the ... | class_definition | 1,356 | 16,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/image_processing_levit.py | null | 8,764 |
class LevitConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LevitModel`]. It is used to instantiate a LeViT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar co... | class_definition | 933 | 5,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/configuration_levit.py | null | 8,765 |
class LevitOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
... | class_definition | 5,317 | 5,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/configuration_levit.py | null | 8,766 |
class LevitFeatureExtractor(LevitImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use LevitImageProcessor instead.",
FutureWarning,
... | class_definition | 837 | 1,203 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/levit/feature_extraction_levit.py | null | 8,767 |
class PatchTSMixerGatedAttention(nn.Module):
"""
Module that applies gated attention to input data.
Args:
in_size (`int`): The input size.
out_size (`int`): The output size.
"""
def __init__(self, in_size: int, out_size: int):
super().__init__()
self.attn_layer = nn... | class_definition | 3,172 | 3,722 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,768 |
class PatchTSMixerBatchNorm(nn.Module):
"""
Compute batch normalization over the sequence length (time) dimension.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.batchnorm = nn.BatchNorm1d(config.d_model, eps=config.norm_eps)
def forward(self, inputs: t... | class_definition | 3,832 | 4,607 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,769 |
class PatchTSMixerPositionalEncoding(nn.Module):
"""
Class for positional encoding
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
# positional encoding: [num_patches x d_model]
if config.use_positional_encoding:
self.position_enc = self._init_... | class_definition | 4,610 | 6,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,770 |
class PatchTSMixerNormLayer(nn.Module):
"""Normalization block
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.norm_mlp = config.norm_mlp
if "batch" in config.norm_mlp.lower():... | class_definition | 6,399 | 7,924 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,771 |
class PatchTSMixerMLP(nn.Module):
def __init__(self, in_features, out_features, config):
super().__init__()
num_hidden = in_features * config.expansion_factor
self.fc1 = nn.Linear(in_features, num_hidden)
self.dropout1 = nn.Dropout(config.dropout)
self.fc2 = nn.Linear(num_hid... | class_definition | 7,927 | 8,777 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,772 |
class PatchTSMixerChannelFeatureMixerBlock(nn.Module):
"""This module mixes the features in the channel dimension.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.norm = PatchTSMixerNor... | class_definition | 8,780 | 10,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,773 |
class PatchTSMixerAttention(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 | 10,229 | 17,635 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,774 |
class PatchMixerBlock(nn.Module):
"""This module mixes the patch dimension.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.norm = PatchTSMixerNormLayer(config)
self.self_attn ... | class_definition | 17,638 | 19,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,775 |
class FeatureMixerBlock(nn.Module):
"""This module mixes the hidden feature dimension.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.norm = PatchTSMixerNormLayer(config)
sel... | class_definition | 19,760 | 20,905 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,776 |
class PatchTSMixerLayer(nn.Module):
"""
The `PatchTSMixer` layer that does all three kinds of mixing.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.patch_mixer = PatchMixerBlock(... | class_definition | 20,908 | 22,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,777 |
class PatchTSMixerBlock(nn.Module):
"""The main computing framework of the `PatchTSMixer` model.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
num_layers = config.num_layers
self.... | class_definition | 22,003 | 23,225 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,778 |
class PatchTSMixerForPredictionHead(nn.Module):
"""Prediction Head for Forecasting
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig, distribution_output=None):
super().__init__()
self.prediction_channel_indices =... | class_definition | 23,228 | 25,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,779 |
class PatchTSMixerLinearHead(nn.Module):
"""Linear head for Classification and Regression.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig, distribution_output=None):
super().__init__()
self.head_aggregation = c... | class_definition | 25,440 | 28,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,780 |
class PatchTSMixerPreTrainedModel(PreTrainedModel):
# Weight initialization
config_class = PatchTSMixerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = False
def _init_weights(self, module):
"""Initialize weights"""
if isinstan... | class_definition | 28,251 | 29,295 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,781 |
class PatchTSMixerPretrainHead(nn.Module):
"""Pretraining head.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.dropout_layer = nn.Dropout(config.head_dropout)
self.base_pt_bloc... | class_definition | 29,298 | 30,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,782 |
class PatchTSMixerPatchify(nn.Module):
"""
A class to patchify the time series sequence into different patches
Returns:
`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
se... | class_definition | 35,903 | 37,958 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,783 |
class PatchTSMixerMasking(nn.Module):
"""
Class to perform random or forecast masking.
Parameters:
config (`PatchTSMixerConfig`): model config
Returns:
x_mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`)
Masked patched input
mask ... | class_definition | 38,066 | 40,564 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,784 |
class PatchTSMixerStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
... | class_definition | 40,674 | 42,420 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,785 |
class PatchTSMixerMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(c... | class_definition | 42,531 | 44,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,786 |
class PatchTSMixerNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "scaling... | class_definition | 45,048 | 46,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,787 |
class PatchTSMixerEncoderOutput(ModelOutput):
"""
Base class for `PatchTSMixerEncoderOutput`, with potential hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, d_model)`):
Hidden-state at the output of the last layer of the mode... | class_definition | 46,269 | 46,841 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,788 |
class PatchTSMixerEncoder(PatchTSMixerPreTrainedModel):
"""
Encoder for PatchTSMixer which inputs patched time-series and outputs patched embeddings.
Args:
config (`PatchTSMixerConfig`):
Configuration.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__... | class_definition | 46,844 | 49,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,789 |
class PatchTSMixerModelOutput(ModelOutput):
"""
Base class for model's outputs, with potential hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, d_model)`):
Hidden-state at the output of the last layer of the model.
hi... | class_definition | 49,853 | 51,367 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,790 |
class PatchTSMixerModel(PatchTSMixerPreTrainedModel):
def __init__(self, config: PatchTSMixerConfig, mask_input: bool = False):
super().__init__(config)
self.use_return_dict = config.use_return_dict
self.encoder = PatchTSMixerEncoder(config)
self.patching = PatchTSMixerPatchify(conf... | class_definition | 51,488 | 54,795 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,791 |
class PatchTSMixerForPreTrainingOutput(ModelOutput):
"""
Output type of [`PatchTSMixerForPreTrainingOutput`].
Args:
prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, patch_length)`):
Prediction output from the pretrain head.
hidden_s... | class_definition | 54,809 | 55,752 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,792 |
class PatchTSMixerForPretraining(PatchTSMixerPreTrainedModel):
r"""
`PatchTSMixer` for mask pretraining.
Args:
config (`PatchTSMixerConfig`):
Configuration.
Returns:
`None`.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__(config)
... | class_definition | 55,755 | 59,242 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,793 |
class PatchTSMixerForPredictionOutput(ModelOutput):
"""
Output type of [`PatchTSMixerForPredictionOutput`].
Args:
prediction_outputs (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_input_channels)`):
Prediction output from the forecast head.
last_hidden_state ... | class_definition | 59,256 | 60,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,794 |
class SamplePatchTSMixerPredictionOutput(ModelOutput):
"""
Base class for time series model's predictions outputs that contains the sampled values from the chosen
distribution.
Args:
sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, number_channels)`):
... | class_definition | 60,558 | 60,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,795 |
class SamplePatchTSMixerRegressionOutput(ModelOutput):
"""
Base class for time series model's predictions outputs that contains the sampled values from the chosen
distribution.
Args:
sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, num_targets)`
Sampled values ... | class_definition | 60,991 | 61,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,796 |
class PatchTSMixerForPrediction(PatchTSMixerPreTrainedModel):
r"""
`PatchTSMixer` for forecasting application.
Args:
config (`PatchTSMixerConfig`):
Configuration.
Returns:
`None`.
"""
def __init__(self, config: PatchTSMixerConfig):
super().__init__(config)
... | class_definition | 62,950 | 72,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,797 |
class PatchTSMixerForTimeSeriesClassificationOutput(ModelOutput):
"""
Output type of [`PatchTSMixerForTimeSeriesClassificationOutput`].
Args:
prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
Prediction output from the classfication head.
last_hidden_... | class_definition | 72,133 | 73,117 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,798 |
class PatchTSMixerForTimeSeriesClassification(PatchTSMixerPreTrainedModel):
r"""
`PatchTSMixer` for classification application.
Args:
config (`PatchTSMixerConfig`):
Configuration.
Returns:
`None`.
"""
def __init__(self, config: PatchTSMixerConfig):
super().... | class_definition | 73,120 | 77,315 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py | null | 8,799 |
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