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 NezhaEmbeddings(nn.Module):
"""Construct the embeddings from word and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.token_type_embeddings = n... | class_definition | 6,316 | 8,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,300 |
class NezhaSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
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 attention "
f"heads (... | class_definition | 8,636 | 15,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,301 |
class NezhaSelfOutput(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)
def ... | class_definition | 15,544 | 16,151 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,302 |
class NezhaAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = NezhaSelfAttention(config)
self.output = NezhaSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, inde... | class_definition | 16,154 | 18,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,303 |
class NezhaIntermediate(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.inter... | class_definition | 18,142 | 18,708 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,304 |
class NezhaOutput(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)
de... | class_definition | 18,711 | 19,320 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,305 |
class NezhaLayer(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 = NezhaAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config... | class_definition | 19,323 | 23,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,306 |
class NezhaEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([NezhaLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torc... | class_definition | 23,202 | 26,994 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,307 |
class NezhaPooler(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 hidd... | class_definition | 26,997 | 27,557 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,308 |
class NezhaPredictionHeadTransform(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.tra... | class_definition | 27,560 | 28,261 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,309 |
class NezhaLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = NezhaPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear... | class_definition | 28,264 | 29,098 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,310 |
class NezhaOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NezhaLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 29,101 | 29,417 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,311 |
class NezhaOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score | class_definition | 29,420 | 29,725 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,312 |
class NezhaPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NezhaLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.prediction... | class_definition | 29,728 | 30,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,313 |
class NezhaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NezhaConfig
load_tf_weights = load_tf_weights_in_nezha
base_model_prefix = "nezha"
supports_gradie... | class_definition | 30,196 | 31,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,314 |
class NezhaForPreTrainingOutput(ModelOutput):
"""
Output type of [`NezhaForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
... | class_definition | 31,364 | 33,316 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,315 |
class NezhaModel(NezhaPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/... | class_definition | 36,682 | 45,666 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,316 |
class NezhaForPreTraining(NezhaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
self.cls = NezhaPreTrainingHeads(config)
# Initialize weights and apply final processing
... | class_definition | 45,902 | 50,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,317 |
class NezhaForMaskedLM(NezhaPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `NezhaForMaskedLM` make sure `config.is_decoder=False` for "... | class_definition | 50,489 | 54,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,318 |
class NezhaForNextSentencePrediction(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
self.cls = NezhaOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_t... | class_definition | 54,840 | 58,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,319 |
class NezhaForSequenceClassification(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.nezha = NezhaModel(config)
classifier_dropout = (
config.classifier_dropout if config.cl... | class_definition | 58,864 | 62,770 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,320 |
class NezhaForMultipleChoice(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nezha = NezhaModel(config)
classifier_dropout = (
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
se... | class_definition | 63,003 | 66,531 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,321 |
class NezhaForTokenClassification(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nezha = NezhaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifie... | class_definition | 66,762 | 69,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,322 |
class NezhaForQuestionAnswering(NezhaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nezha = NezhaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Init... | class_definition | 69,807 | 73,923 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nezha/modeling_nezha.py | null | 10,323 |
class MMBTConfig:
"""
This is the configuration class to store the configuration of a [`MMBTModel`]. It is used to instantiate a MMBT
model according to the specified arguments, defining the model architecture.
Args:
config ([`PreTrainedConfig`]):
Config of the underlying Transforme... | class_definition | 749 | 1,596 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/configuration_mmbt.py | null | 10,324 |
class ModalEmbeddings(nn.Module):
"""Generic Modal Embeddings which takes in an encoder, and a transformer embedding."""
def __init__(self, config, encoder, embeddings):
super().__init__()
self.config = config
self.encoder = encoder
self.proj_embeddings = nn.Linear(config.modal_... | class_definition | 1,086 | 3,192 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py | null | 10,325 |
class MMBTModel(nn.Module, ModuleUtilsMixin):
def __init__(self, config, transformer, encoder):
super().__init__()
self.config = config
self.transformer = transformer
self.modal_encoder = ModalEmbeddings(config, encoder, transformer.embeddings)
@add_start_docstrings_to_model_for... | class_definition | 9,790 | 14,625 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py | null | 10,326 |
class MMBTForClassification(nn.Module):
r"""
**labels**: (*optional*) `torch.LongTensor` of shape `(batch_size,)`:
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (... | class_definition | 14,837 | 18,912 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py | null | 10,327 |
class MegaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MegaModel`]. It is used to instantiate a Mega
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 873 | 12,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py | null | 10,328 |
class MegaOnnxConfig(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 | 12,142 | 12,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py | null | 10,329 |
class MegaLM(nn.Module):
"The base class for our Mega encoder - given input IDs, embed text and return encoder output"
def __init__(self, mega_args, depth, vocab_size):
super().__init__()
self.mega_args = mega_args
self.embedding_layer = nn.Embedding(vocab_size, self.mega_args.encoder_e... | class_definition | 1,828 | 3,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py | null | 10,330 |
class OriginalMegaForMaskedLM(nn.Module):
"A wrapper class for doing masked language modeling with Mega"
def __init__(self, mega_args, depth, vocab_size):
super().__init__()
self.mega = MegaLM(mega_args, depth, vocab_size)
self.mlm_head = nn.Linear(mega_args.encoder_embed_dim, vocab_siz... | class_definition | 4,013 | 4,954 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py | null | 10,331 |
class MegaEmbeddings(nn.Module):
"""
Mega's basic implementation does not incorporate token type embeddings, so this is a stripped-down version of
RoBERTa's embeddings which optionally includes token types
"""
def __init__(self, config: MegaConfig):
super().__init__()
self.word_embe... | class_definition | 1,617 | 4,281 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,332 |
class MegaSimpleRelativePositionalBias(nn.Module):
"""
Simple relative positional embeddings copied from the Mega repo; renamed variables for better readability
"""
def __init__(self, config: MegaConfig):
super().__init__()
self.config = config
self.max_positions = self.config.m... | class_definition | 4,284 | 5,473 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,333 |
class MegaRotaryRelativePositionalBias(nn.Module):
"""
Rotary relative bias for positional information; similar in concept to RoPE (i.e. RoFormer) but taken from the Mega
repo due to differences in implementation.
When initialized, produces a positional bias which ranges from position 0 to config.max_p... | class_definition | 5,476 | 8,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,334 |
class MegaDropout(nn.Module):
"""
A unified class for standard dropout functionality and featurewise dropout.
The original fairseq Mega repo used 2 classes for these, which included some unnecessary handling of training logic
and an unused `inplace` option. The original implementation used torch.nn.fun... | class_definition | 8,142 | 9,975 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,335 |
class MegaRMSNorm(nn.Module):
"""
RMSNorm used in Mega implementation. Differs from T5's RMSNorm by applying the weight prior to taking the square
root (as opposed to after in T5)
"""
def __init__(self, number_features, eps=1e-6, affine=True):
super().__init__()
self.num_features = ... | class_definition | 9,978 | 10,882 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,336 |
class MegaScaleNorm(nn.Module):
"""
Scale normalization introduced in MEGA which is similar to RMSNorm, but uses a single parameter for scalar
multiplication instead of a vector, and applies over a specified dimension
"""
def __init__(self, dim, eps=1e-6, affine=True):
super().__init__()
... | class_definition | 10,885 | 11,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,337 |
class MegaSequenceNorm(nn.Module):
"""
A wrapper class for various layer normalization options used in Mega. Used to handle differences in expectations on
input axis locations for different normalization methods.
"""
def __init__(self, norm_type, embedding_dim, eps=1e-5, affine=True, export=False):... | class_definition | 11,694 | 13,095 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,338 |
class MegaMultiDimensionDampedEma(nn.Module):
"""
Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
variable names and moving away from the stateful representation of incremental decoding state. See
"https://arxiv.org/abs/2209.10655" for more d... | class_definition | 13,197 | 24,287 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,339 |
class MegaGatedCrossAttention(nn.Module):
"""
Gated Structured State Attention for use in encoder-decoder model. See Mega paper for more details. Only
modifications from original implementation are variable names, removing the unnecessary `before_attn_fn` and
`static_kv` arguments, and the stateful repr... | class_definition | 24,290 | 37,442 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,340 |
class MegaMovingAverageGatedAttention(nn.Module):
"""
Pure PyTorch implementation of Mega block; see https://arxiv.org/abs/2209.10655 and original fairseq implementation
at https://github.com/facebookresearch/mega (copyright Meta Research, licensed under MIT License)
Differences from original implement... | class_definition | 37,445 | 55,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,341 |
class MegaNormalizedFeedForwardNetwork(nn.Module):
"""
Normalized feed-forward network used in Mega blocks. Left as-is from original Mega repo aside from retrieving args
from Hugging Face config
"""
def __init__(self, config: MegaConfig):
super().__init__()
self.config = config
... | class_definition | 55,109 | 56,624 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,342 |
class MegaBlock(nn.Module):
def __init__(self, config: MegaConfig):
super().__init__()
self.seq_len_dim = 1
self.mega_layer = MegaMovingAverageGatedAttention(config)
self.nffn = MegaNormalizedFeedForwardNetwork(config) if config.use_normalized_ffn else None
self.is_decoder = ... | class_definition | 56,627 | 65,241 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,343 |
class MegaPooler(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 hidde... | class_definition | 65,336 | 65,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,344 |
class MegaPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MegaConfig
base_model_prefix = "mega"
supports_gradient_checkpointing = False
_no_split_modules = [... | class_definition | 65,898 | 68,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,345 |
class MegaModel(MegaPreTrainedModel):
"""
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 after self-attention, following the architecture described in *Mega: Moving Average
Equipped Gated Attention*_ by Xuezhe Ma, Ch... | class_definition | 72,205 | 82,382 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,346 |
class MegaForCausalLM(MegaPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: MegaConfig):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `MegaForCausalLM` as a standalone, add `is_decoder=True.`")
sel... | class_definition | 82,513 | 89,950 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,347 |
class MegaForMaskedLM(MegaPreTrainedModel):
_tied_weights_keys = ["mlm_head.weight"]
def __init__(self, config: MegaConfig):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `MegaForMaskedLM`, set `config.is_decoder=False` for "
... | class_definition | 90,055 | 94,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,348 |
class MegaForSequenceClassification(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.mega = MegaModel(config, add_pooling_layer=False)
self.classifier = MegaClassificationHead(config)
... | class_definition | 94,293 | 97,884 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,349 |
class MegaForMultipleChoice(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mega = MegaModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply final... | class_definition | 98,115 | 101,383 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,350 |
class MegaForTokenClassification(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mega = MegaModel(config, add_pooling_layer=False)
classifier_dropout = (
config.classifier_dropout if config.classifier_dr... | class_definition | 101,612 | 104,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,351 |
class MegaClassificationHead(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.classifier_dr... | class_definition | 104,408 | 105,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,352 |
class MegaForQuestionAnswering(MegaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mega = MegaModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initiali... | class_definition | 105,465 | 109,518 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py | null | 10,353 |
class JukeboxPriorConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JukeboxPrior`]. It is used to instantiate a
`JukeboxPrior` according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults ... | class_definition | 3,712 | 15,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py | null | 10,354 |
class JukeboxVQVAEConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JukeboxVQVAE`]. It is used to instantiate a
`JukeboxVQVAE` according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a... | class_definition | 15,236 | 21,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py | null | 10,355 |
class JukeboxConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`JukeboxModel`].
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information. Insta... | class_definition | 21,292 | 26,748 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/configuration_jukebox.py | null | 10,356 |
class JukeboxTokenizer(PreTrainedTokenizer):
"""
Constructs a Jukebox tokenizer. Jukebox can be conditioned on 3 different inputs :
- Artists, unique ids are associated to each artist from the provided dictionary.
- Genres, unique ids are associated to each genre from the provided dictionary.
... | class_definition | 1,299 | 17,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py | null | 10,357 |
class JukeboxConv1D(nn.Module):
def __init__(self, input_width, output_width):
super().__init__()
self.input_width = input_width
self.output_width = output_width
weight = torch.empty(input_width, output_width)
bias = torch.zeros(output_width)
self.weight = nn.Paramete... | class_definition | 9,576 | 10,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,358 |
class JukeboxResConv1DBlock(nn.Module):
def __init__(self, config, conv_width, depth=1, res_scale=1.0):
super().__init__()
hidden_dim = config.res_convolution_multiplier * conv_width
dilation = config.res_dilation_growth_rate**depth
padding = dilation
self.res_scale = res_sc... | class_definition | 10,337 | 11,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,359 |
class JukeboxResnet1D(nn.Module):
def __init__(self, config, conv_width, n_depth, reverse_dilation=False):
super().__init__()
self.dilation_cycle = config.res_dilation_cycle
res_scale = 1.0 if not config.conv_res_scale else 1.0 / math.sqrt(n_depth)
blocks = []
for depth in r... | class_definition | 11,196 | 11,990 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,360 |
class JukeboxEncoderConvBlock(nn.Module):
def __init__(self, config, embed_dim, hidden_dim, depth, down_t, stride_t):
super().__init__()
blocks = []
filter_t = stride_t * 2
pad_t = stride_t // 2
if down_t > 0:
for i in range(down_t):
blocks.append(... | class_definition | 11,993 | 12,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,361 |
class JukeboxEncoder(nn.Module):
def __init__(self, config, width, depth, levels, downs_t, strides_t):
super().__init__()
self.levels = levels
self.level_blocks = nn.ModuleList()
iterator = zip(list(range(self.levels)), downs_t, strides_t)
for i, down_t, stride_t in iterator... | class_definition | 12,817 | 13,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,362 |
class JukeboxDecoderConvBock(nn.Module):
def __init__(self, config, embed_dim, hidden_dim, depth, down_t, stride_t, reverse_dilation=True):
self.embed_dim = embed_dim
self.hidden_dim = hidden_dim
super().__init__()
blocks = []
if down_t > 0:
filter_t = stride_t * ... | class_definition | 13,694 | 14,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,363 |
class JukeboxDecoder(nn.Module):
def __init__(self, config, hidden_dim, depth, levels, downs_t, strides_t):
super().__init__()
self.levels = levels
self.level_blocks = nn.ModuleList()
for level, down_t, stride_t in zip(list(range(self.levels)), downs_t, strides_t):
self.l... | class_definition | 14,730 | 15,730 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,364 |
class JukeboxBottleneckBlock(nn.Module):
def __init__(self, config: JukeboxVQVAEConfig):
super().__init__()
self.nb_discrete_codes = config.nb_discrete_codes
self.codebook_width = config.embed_dim
self.mu = config.lmu
self.threshold = 1.0
self.init = False
sel... | class_definition | 15,733 | 22,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,365 |
class JukeboxBottleneck(nn.Module):
def __init__(self, config, levels):
super().__init__()
self.levels = levels
self.level_blocks = nn.ModuleList()
for level in range(self.levels):
self.level_blocks.append(JukeboxBottleneckBlock(config))
def encode(self, raw_audio):
... | class_definition | 22,586 | 24,333 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,366 |
class JukeboxVQVAE(PreTrainedModel):
config_class = JukeboxVQVAEConfig
base_model_prefix = "vqvae"
def _init_weights(self, module):
if isinstance(module, nn.Embedding): # embed_tokens
module.weight.data.normal_(mean=0.0, std=0.02 * self.config.init_scale)
elif isinstance(module... | class_definition | 25,491 | 33,751 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,367 |
class JukeboxMLP(nn.Module):
def __init__(self, config):
# a single channel is always used in original code
super().__init__()
embed_dim = config.hidden_size
hidden_dim = int(config.mlp_multiplier * embed_dim)
self.c_fc = JukeboxConv1D(embed_dim, hidden_dim)
self.c_p... | class_definition | 33,754 | 34,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,368 |
class JukeboxLayerNorm(FusedLayerNorm):
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super().__init__(normalized_shape, eps=eps, elementwise_affine=elementwise_affine)
self.width = np.prod(normalized_shape)
self.max_numel = 65535 * self.width
def forward(self... | class_definition | 34,484 | 35,038 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,369 |
class JukeboxAttention(nn.Module):
def __init__(self, config, n_ctx, attn_func="dense_attn"):
super().__init__()
self.embed_dim = config.hidden_size
self.n_heads = config.n_heads
self.dropout = config.attn_dropout
hidden_dim = int(config.attention_multiplier * self.embed_dim)... | class_definition | 35,041 | 52,588 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,370 |
class JukeboxBlock(nn.Module):
def __init__(self, config, n_ctx, attn_func="dense_attn"):
super().__init__()
self.width = config.hidden_size
self.attn = JukeboxAttention(config, n_ctx, attn_func=attn_func)
self.layer_norm_0 = JukeboxLayerNorm(config.hidden_size)
self.mlp = J... | class_definition | 52,591 | 53,701 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,371 |
class JukeboxLayerStack(nn.Module):
def __init__(self, config, n_ctx):
super().__init__()
self.n_ctx = n_ctx
self.width = config.hidden_size
self.num_layers = config.num_layers
self.blocks = config.blocks
self.attention_pattern = config.attention_pattern
if se... | class_definition | 53,704 | 56,026 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,372 |
class JukeboxPositionalEmbedding(nn.Module):
def __init__(self, embed_dim, width):
super().__init__()
self.pos_emb = nn.Parameter(torch.empty((embed_dim, width)))
def forward(self):
pos_emb = self.pos_emb
return pos_emb | class_definition | 56,029 | 56,289 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,373 |
class JukeboxConditionalAutoregressive(nn.Module):
def __init__(
self,
config,
n_ctx=None,
embed_dim=None,
audio_conditioning=False,
metadata_conditioning=False,
is_encoder=False,
):
"""
Autoregressive model on either lyric tokens or music ... | class_definition | 56,292 | 70,204 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,374 |
class JukeboxMusicTokenConditioner(nn.Module):
"""
The `JukeboxMusicTokenConditioner` takes music tokens as an input (coresponding to the codes of the VQVAE's
codebook) and upsamples it using a single layer of decoder convolution block (the same is used in the VQVAE).
"""
def __init__(self, config,... | class_definition | 70,207 | 72,068 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,375 |
class JukeboxRangeEmbedding(nn.Module):
"""
The `JukeboxRangeEmbedding` interpolate the given [pos_start, pos_end] to obtain an equivalent of time positional
embedding of length `n_ctx`.
Binning process : For each pos in position tensor, find its bin [start,end) mapped to [0,1,...,bins-1] [start,end)
... | class_definition | 72,071 | 74,141 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,376 |
class JukeboxLabelConditioner(nn.Module):
def __init__(self, config, include_time_signal):
super().__init__()
embed_dim = config.hidden_size
timing_dims = config.timing_dims
sampling_rate = config.sampling_rate
nb_genres, nb_artists = config.metadata_dims
music_token... | class_definition | 74,144 | 76,481 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,377 |
class JukeboxPrior(PreTrainedModel):
"""
The JukeboxPrior class, which is a wrapper around the various conditioning and the transformer. JukeboxPrior can be
seen as language models trained on music. They model the next `music token` prediction task. If a (lyric) `encoderù
is defined, it also models the ... | class_definition | 76,484 | 99,923 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,378 |
class JukeboxPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = JukeboxConfig
base_model_prefix = "jukebox"
supports_gradient_checkpointing = False
def _init_w... | class_definition | 99,926 | 100,483 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,379 |
class JukeboxModel(JukeboxPreTrainedModel):
_no_split_modules = ["JukeboxBlock"]
def __init__(self, config):
super().__init__(config)
vqvae_config = config.vqvae_config
self.vqvae = JukeboxVQVAE(vqvae_config)
self.set_shared_params(config)
self.priors = nn.ModuleList(
... | class_definition | 101,510 | 119,470 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py | null | 10,380 |
class TapexTruncationStrategy(ExplicitEnum):
"""
Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE.
"""
DROP_ROWS_TO_FIT = "drop_rows_to_fit" | class_definition | 1,321 | 1,544 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py | null | 10,381 |
class IndexedRowTableLinearize:
"""
FORMAT: col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...
"""
def process_table(self, table_content: Dict):
"""
Given a table, TableLinearize aims at converting it into a flatten sequence with special symbols.
"""
assert ... | class_definition | 7,152 | 8,674 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py | null | 10,382 |
class TapexTokenizer(PreTrainedTokenizer):
r"""
Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).
This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences
to be used by TAPEX models. The format that the TAPEX tokenizer c... | class_definition | 8,677 | 64,346 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/tapex/tokenization_tapex.py | null | 10,383 |
class ViTHybridConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViTHybridModel`]. It is used to instantiate a ViT
Hybrid model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield... | class_definition | 872 | 8,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/configuration_vit_hybrid.py | null | 10,384 |
class ViTHybridImageProcessor(BaseImageProcessor):
r"""
Constructs a ViT Hybrid 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 th... | class_definition | 1,423 | 16,218 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/image_processing_vit_hybrid.py | null | 10,385 |
class ViTHybridEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: ViTHybridConfig, use_mask_token: bool = False) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.randn(1, 1, c... | class_definition | 1,790 | 5,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,386 |
class ViTHybridPatchEmbeddings(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... | class_definition | 5,745 | 8,380 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,387 |
class ViTHybridSelfAttention(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
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... | class_definition | 8,383 | 11,235 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,388 |
class ViTHybridSdpaSelfAttention(ViTHybridSelfAttention):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, out... | class_definition | 11,238 | 12,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,389 |
class ViTHybridSelfOutput(nn.Module):
"""
The residual connection is defined in ViTHybridLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.dense =... | class_definition | 12,496 | 13,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,390 |
class ViTHybridAttention(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.attention = ViTHybridSelfAttention(config)
self.output = ViTHybridSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
... | class_definition | 13,160 | 14,861 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,391 |
class ViTHybridSdpaAttention(ViTHybridAttention):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__(config)
self.attention = ViTHybridSdpaSelfAttention(config) | class_definition | 14,864 | 15,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,392 |
class ViTHybridIntermediate(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
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]
... | class_definition | 15,066 | 15,662 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,393 |
class ViTHybridOutput(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tenso... | class_definition | 15,665 | 16,204 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,394 |
class ViTHybridLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VIT_... | class_definition | 16,313 | 18,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,395 |
class ViTHybridEncoder(nn.Module):
def __init__(self, config: ViTHybridConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([ViTHybridLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
... | class_definition | 18,220 | 20,159 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,396 |
class ViTHybridPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ViTHybridConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
supports_gradient_c... | class_definition | 20,162 | 21,878 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,397 |
class ViTHybridModel(ViTHybridPreTrainedModel):
def __init__(self, config: ViTHybridConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
super().__init__(config)
self.config = config
self.embeddings = ViTHybridEmbeddings(config, use_mask_token=use_mask_token)
self.... | class_definition | 23,795 | 28,068 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,398 |
class ViTHybridPooler(nn.Module):
def __init__(self, config: ViTHybridConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state ... | class_definition | 28,071 | 28,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/vit_hybrid/modeling_vit_hybrid.py | null | 10,399 |
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