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 RealmEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([RealmLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torc... | class_definition | 25,214 | 29,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,500 |
class RealmPooler(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 | 29,009 | 29,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,501 |
class RealmEmbedderOutput(ModelOutput):
"""
Outputs of [`RealmEmbedder`] models.
Args:
projected_score (`torch.FloatTensor` of shape `(batch_size, config.retriever_proj_size)`):
Projected score.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden... | class_definition | 29,583 | 30,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,502 |
class RealmScorerOutput(ModelOutput):
"""
Outputs of [`RealmScorer`] models.
Args:
relevance_score (`torch.FloatTensor` of shape `(batch_size, config.num_candidates)`):
The relevance score of document candidates (before softmax).
query_score (`torch.FloatTensor` of shape `(batch... | class_definition | 30,879 | 31,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,503 |
class RealmReaderOutput(ModelOutput):
"""
Outputs of [`RealmReader`] models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `start_positions`, `end_positions`, `has_answers` are provided):
Total loss.
retriever_loss (`torch.FloatTensor` of shape `(1,)... | class_definition | 31,627 | 34,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,504 |
class RealmForOpenQAOutput(ModelOutput):
"""
Outputs of [`RealmForOpenQA`] models.
Args:
reader_output (`dict`):
Reader output.
predicted_answer_ids (`torch.LongTensor` of shape `(answer_sequence_length)`):
Predicted answer ids.
"""
reader_output: dict = No... | class_definition | 34,513 | 34,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,505 |
class RealmPredictionHeadTransform(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 | 34,888 | 35,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,506 |
class RealmLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = RealmPredictionHeadTransform(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 | 35,562 | 36,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,507 |
class RealmOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RealmLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 36,399 | 36,685 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,508 |
class RealmScorerProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = RealmLMPredictionHead(config)
self.dense = nn.Linear(config.hidden_size, config.retriever_proj_size)
self.LayerNorm = nn.LayerNorm(config.retriever_proj_size, eps=config.layer_no... | class_definition | 36,688 | 37,187 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,509 |
class RealmReaderProjection(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense_intermediate = nn.Linear(config.hidden_size, config.span_hidden_size * 2)
self.dense_output = nn.Linear(config.span_hidden_size, 1)
self.layer_normalization ... | class_definition | 37,190 | 40,210 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,510 |
class RealmPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = RealmConfig
load_tf_weights = load_tf_weights_in_realm
base_model_prefix = "realm"
def _init_weig... | class_definition | 43,478 | 45,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,511 |
class RealmBertModel(RealmPreTrainedModel):
"""
Same as the original BertModel but remove docstrings.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = RealmEmbeddings(config)
self.encoder = RealmEnco... | class_definition | 45,077 | 51,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,512 |
class RealmEmbedder(RealmPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.realm = RealmBertModel(self.config)
self.cls = RealmScorerProjection(self.config)
self.post_init()
def get_input_embed... | class_definition | 51,511 | 54,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,513 |
class RealmScorer(RealmPreTrainedModel):
r"""
Args:
query_embedder ([`RealmEmbedder`]):
Embedder for input sequences. If not specified, it will use the same embedder as candidate sequences.
"""
def __init__(self, config, query_embedder=None):
super().__init__(config)
... | class_definition | 54,474 | 61,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,514 |
class RealmKnowledgeAugEncoder(RealmPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
self.realm = RealmBertModel(self.config)
self.cls = RealmOnlyMLMHead(self.config)
self.post_init()
def get_input_embed... | class_definition | 61,285 | 67,782 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,515 |
class RealmReader(RealmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.realm = RealmBertModel(config)
self.cls = RealmOnlyMLMHead(config)
self.qa_outputs = RealmReaderProjection(config)
self.post_init()... | class_definition | 67,854 | 76,079 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,516 |
class RealmForOpenQA(RealmPreTrainedModel):
def __init__(self, config, retriever=None):
super().__init__(config)
self.embedder = RealmEmbedder(config)
self.reader = RealmReader(config)
self.register_buffer(
"block_emb",
torch.zeros(()).new_empty(
... | class_definition | 77,902 | 83,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py | null | 10,517 |
class GPTSanJapaneseDenseActDense(nn.Module):
"""
FFN Layer for Switch Transformer and Extra layers
GPTSAN can mix Switch Transformer layers and normal Transformer layers This class is used as Expert in Switch
Transformer layers and as FFN in regular Transformer layers. RELU is used in the Switch Trans... | class_definition | 3,903 | 5,317 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,518 |
class GPTSanJapaneseTop1Router(nn.Module):
"""
Router using tokens choose top-1 experts assignment.
This router uses the same mechanism as in Switch Transformer (https://arxiv.org/abs/2101.03961) and V-MoE
(https://arxiv.org/abs/2106.05974): tokens choose their top experts. Items are sorted by router_p... | class_definition | 5,320 | 10,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,519 |
class GPTSanJapaneseSparseMLP(nn.Module):
r"""
Implementation of the Switch Transformers Sparse MLP module.
"""
def __init__(self, config: GPTSanJapaneseConfig, expert_class: nn.Module = GPTSanJapaneseDenseActDense):
super().__init__()
# Step 1: Get the correct router according to its c... | class_definition | 10,418 | 12,529 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,520 |
class GPTSanJapaneseLayerSparseFF(nn.Module):
r"""
Switch Transformers Feed Forward layer module. This is a wrapper around the Mixture of Experts module.
Parameters:
config : ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model.
Initializing with a ... | class_definition | 12,532 | 14,094 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,521 |
class GPTSanJapaneseLayerDenseFF(nn.Module):
r"""
Extra Transformers Feed Forward layer module.
Parameters:
config : ([`GPTSanJapaneseConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the ... | class_definition | 14,097 | 15,286 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,522 |
class GPTSanJapaneseAttention(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 | 15,289 | 22,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,523 |
class GPTSanJapaneseLayerSelfAttention(nn.Module):
"""
Self Attention and Normalization Unit
"""
def __init__(self, config, has_relative_attention_bias=False):
super().__init__()
self.self_attn = GPTSanJapaneseAttention(
embed_dim=config.d_model,
num_heads=config... | class_definition | 22,702 | 26,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,524 |
class GPTSanJapaneseBlock(nn.Module):
"""
Self Attention and FFN Unit
"""
def __init__(self, config, ext_layer=False):
super().__init__()
self.self_attn = GPTSanJapaneseLayerSelfAttention(config)
self.feed_forward = GPTSanJapaneseLayerDenseFF(config) if ext_layer else GPTSanJapa... | class_definition | 26,937 | 30,886 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,525 |
class GPTSanJapanesePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTSanJapaneseConfig
base_model_prefix = "gptsan_japanese"
supports_gradient_checkpointing = ... | class_definition | 30,889 | 36,913 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,526 |
class GPTSanJapaneseModel(GPTSanJapanesePreTrainedModel):
def __init__(self, config: GPTSanJapaneseConfig):
super().__init__(config)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.d_model)
self.config = copy.deepcopy(config)
self.embed_tokens = nn.Embe... | class_definition | 42,090 | 54,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,527 |
class GPTSanJapaneseForConditionalGeneration(GPTSanJapanesePreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: GPTSanJapaneseConfig):
super().__init__(config)
self.model = GPTSanJapaneseModel(config)
self.register_buffer("final_logits_bias", torch.zeros(... | class_definition | 54,234 | 64,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/modeling_gptsan_japanese.py | null | 10,528 |
class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
- Return token_type_ids for Prefix-LM model
The bagofword token represents a ... | class_definition | 1,919 | 15,764 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py | null | 10,529 |
class SubWordJapaneseTokenizer:
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
https://github.com/tanreinama/Japanese-BPEEncoder_V2 This tokenizer class is under MIT Lisence accor... | class_definition | 15,767 | 22,618 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/tokenization_gptsan_japanese.py | null | 10,530 |
class GPTSanJapaneseConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTSanJapaneseModel`]. It is used to instantiate
a GPTSANJapanese model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defau... | class_definition | 764 | 7,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/gptsan_japanese/configuration_gptsan_japanese.py | null | 10,531 |
class XGLMTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" XGLM tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from [`RobertaTokenizer`]
and [`XLNetTokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
... | class_definition | 1,140 | 7,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm_fast.py | null | 10,532 |
class FlaxXGLMAttention(nn.Module):
config: XGLMConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_hea... | class_definition | 5,758 | 13,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,533 |
class FlaxXGLMDecoderLayer(nn.Module):
config: XGLMConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxXGLMAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.... | class_definition | 13,169 | 16,871 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,534 |
class FlaxXGLMDecoderLayerCollection(nn.Module):
config: XGLMConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxXGLMDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_layers)
]
s... | class_definition | 16,874 | 19,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,535 |
class FlaxXGLMModule(nn.Module):
config: XGLMConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.d_model
self.padding_idx = self.config.pad_token_id
self.max... | class_definition | 19,570 | 22,734 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,536 |
class FlaxXGLMPreTrainedModel(FlaxPreTrainedModel):
config_class = XGLMConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: XGLMConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
... | class_definition | 22,737 | 28,950 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,537 |
class FlaxXGLMModel(FlaxXGLMPreTrainedModel):
module_class = FlaxXGLMModule | class_definition | 29,106 | 29,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,538 |
class FlaxXGLMForCausalLMModule(nn.Module):
config: XGLMConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.model = FlaxXGLMModule(self.config, self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
... | class_definition | 29,338 | 31,244 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,539 |
class FlaxXGLMForCausalLM(FlaxXGLMPreTrainedModel):
module_class = FlaxXGLMForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = s... | class_definition | 31,445 | 32,966 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_flax_xglm.py | null | 10,540 |
class TFXGLMAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super()... | class_definition | 5,294 | 12,868 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_tf_xglm.py | null | 10,541 |
class TFXGLMDecoderLayer(keras.layers.Layer):
def __init__(self, config: XGLMConfig, **kwargs: Any) -> None:
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFXGLMAttention(
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
... | class_definition | 12,871 | 19,845 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_tf_xglm.py | null | 10,542 |
class TFXGLMMainLayer(keras.layers.Layer):
config_class = XGLMConfig
def __init__(
self, config: XGLMConfig, embed_tokens: Optional[TFSharedEmbeddings] = None, *inputs, **kwargs: Any
) -> None:
super().__init__(*inputs, **kwargs)
self.config = config
self.padding_idx = conf... | class_definition | 19,868 | 29,235 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_tf_xglm.py | null | 10,543 |
class TFXGLMPreTrainedModel(TFPreTrainedModel):
config_class = XGLMConfig
base_model_prefix = "model" | class_definition | 29,238 | 29,347 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_tf_xglm.py | null | 10,544 |
class TFXGLMModel(TFXGLMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_layers* layers. Each layer is a [`TFXGLMDecoderLayer`]
Args:
config: XGLMConfig
embed_tokens: [TFSharedEmbeddings]: output embedding
"""
def __init__(
self, config: XGLMConfig, embed... | class_definition | 36,589 | 39,233 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_tf_xglm.py | null | 10,545 |
class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
base_model_prefix = "model"
_keys_to_ignore_on_load_missing = [
r"model.embed_positions.weights",
r"lm_head.weight",
]
_keys_to_ignore_on_save = [
r"model.embed_positions.weights",
]
def __init... | class_definition | 39,434 | 45,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_tf_xglm.py | null | 10,546 |
class XGLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XGLMModel`]. It is used to instantiate an XGLM
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 776 | 5,845 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/configuration_xglm.py | null | 10,547 |
class XGLMTokenizer(PreTrainedTokenizer):
"""
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. 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 supercl... | class_definition | 1,014 | 12,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/tokenization_xglm.py | null | 10,548 |
class XGLMScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_d... | class_definition | 6,824 | 7,309 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,549 |
class XGLMSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = embedding... | class_definition | 7,312 | 9,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,550 |
class XGLMAttention(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,
):
super().__init__()
se... | class_definition | 9,781 | 17,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,551 |
class XGLMDecoderLayer(nn.Module):
def __init__(self, config: XGLMConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = XGLMAttention(
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
... | class_definition | 17,148 | 23,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,552 |
class XGLMPreTrainedModel(PreTrainedModel):
config_class = XGLMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["XGLMDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
... | class_definition | 23,006 | 23,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,553 |
class XGLMModel(XGLMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_layers* layers. Each layer is a [`XGLMDecoderLayer`]
Args:
config: XGLMConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: XGLMConfig, embed_tokens: Optional[nn.E... | class_definition | 23,824 | 32,861 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,554 |
class XGLMForCausalLM(XGLMPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = XGLMModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=F... | class_definition | 33,062 | 37,636 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py | null | 10,555 |
class BitLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool, device=None, dtype=None):
super().__init__()
self.dtype = dtype
self.in_features = in_features
self.out_features = out_features
self.register_buffer(
"weight",
... | class_definition | 4,273 | 6,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/bitnet.py | null | 10,556 |
class HfDeepSpeedConfig(DeepSpeedConfig):
"""
This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.
A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
things like the Trainer object is ... | class_definition | 2,067 | 3,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py | null | 10,557 |
class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
"""
The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
same lifespan as the latter.
"""
def __init__(self, config_file_or_dict):
super().__init__(config_file_or_dict)
... | class_definition | 3,209 | 11,401 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py | null | 10,558 |
class HiggsLinear(torch.nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
num_bits: int,
bias=True,
dtype: torch.dtype = None,
device: torch.device = None,
group_size: int = 256,
hadamard_size: int = 1024,
):
supe... | class_definition | 24,705 | 26,819 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/higgs.py | null | 10,559 |
class PeftAdapterMixin:
"""
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
library: https://huggingface.co/docs/peft/index
... | class_definition | 1,222 | 27,463 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py | null | 10,560 |
class FbgemmFp8Linear(torch.nn.Module):
def __init__(self, in_features, out_features, bias, weight_dtype=torch.float32):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.register_buffer("weight", torch.zeros((out_features, in_features), dtype=t... | class_definition | 979 | 3,030 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/fbgemm_fp8.py | null | 10,561 |
class GGUFTokenizerSkeleton:
def __init__(self, dict_):
for k, v in dict_.items():
setattr(self, k, v)
if not hasattr(self, "merges"):
if not hasattr(self, "tokens") or not hasattr(self, "scores"):
raise ValueError(
"tokens and scores need... | class_definition | 9,815 | 11,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,562 |
class GGUFLlamaConverter(LlamaConverter):
def __init__(self, tokenizer_dict):
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.original_tokenizer = self.proto
self.additional_kwargs = {}
self.is_llama_3_tokenizer = getattr(self.proto, "tokenizer_type", "llama") != "llama"
... | class_definition | 11,660 | 16,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,563 |
class GGUFQwen2Converter(Qwen2Converter):
def __init__(self, tokenizer_dict):
self.original_tokenizer = GGUFTokenizerSkeleton(tokenizer_dict)
self.additional_kwargs = {}
def converted(self) -> Tokenizer:
vocab = {word: i for i, word in enumerate(self.original_tokenizer.tokens)}
... | class_definition | 16,275 | 17,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,564 |
class GGUFPhi3Converter(LlamaConverter):
def __init__(self, tokenizer_dict):
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.original_tokenizer = self.proto
self.additional_kwargs = {}
def vocab(self, proto):
return list(zip(proto.tokens, proto.scores))
def merges(s... | class_definition | 17,019 | 20,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,565 |
class GGUFGPTConverter(GPT2Converter):
def __init__(self, tokenizer_dict):
self.original_tokenizer = GGUFTokenizerSkeleton(tokenizer_dict)
self.additional_kwargs = {}
def converted(self) -> Tokenizer:
vocab = {word: i for i, word in enumerate(self.original_tokenizer.tokens)}
mer... | class_definition | 20,181 | 20,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,566 |
class GGUFT5Converter(T5Converter):
def __init__(self, tokenizer_dict):
# set dummy data to avoid unnecessary merges calculation
tokenizer_dict["merges"] = ["dummy text"]
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.token2id = {k: v for v, k in enumerate(self.proto.tokens... | class_definition | 20,618 | 22,825 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,567 |
class GGUFGemmaConverter(GemmaConverter):
def __init__(self, tokenizer_dict):
# set dummy data to avoid unnecessary merges calculation
tokenizer_dict["merges"] = ["dummy text"]
self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
self.original_tokenizer = self.proto
self.addit... | class_definition | 22,828 | 24,981 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py | null | 10,568 |
class TorchExportableModuleWithStaticCache(torch.nn.Module):
"""
A wrapper module designed to make a `PreTrainedModel` exportable with `torch.export`,
specifically for use with static caching. This module ensures that the exported model
is compatible with further lowering and execution in `ExecuTorch`.
... | class_definition | 869 | 7,587 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/executorch.py | null | 10,569 |
class TensorBoardCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard).
Args:
tb_writer (`SummaryWriter`, *optional*):
The writer to use. Will instantiate one if not set.
"""
def __init__(self, tb_write... | class_definition | 25,925 | 29,225 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,570 |
class WandbLogModel(str, Enum):
"""Enum of possible log model values in W&B."""
CHECKPOINT = "checkpoint"
END = "end"
FALSE = "false"
@property
def is_enabled(self) -> bool:
"""Check if the value corresponds to a state where the `WANDB_LOG_MODEL` setting is enabled."""
return s... | class_definition | 29,816 | 31,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,571 |
class WandbCallback(TrainerCallback):
"""
A [`TrainerCallback`] that logs metrics, media, model checkpoints to [Weight and Biases](https://www.wandb.com/).
"""
def __init__(self):
has_wandb = is_wandb_available()
if not has_wandb:
raise RuntimeError("WandbCallback requires w... | class_definition | 31,044 | 42,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,572 |
class CometCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.com/site/).
"""
def __init__(self):
if _is_comet_installed is False or _is_comet_recent_enough is False:
raise RuntimeError(
f"CometCallback requires c... | class_definition | 42,994 | 49,725 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,573 |
class AzureMLCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/).
"""
def __init__(self, azureml_run=None):
if not is_azureml_available():
raise RuntimeError("AzureMLCallback requires azureml to be installed. R... | class_definition | 49,728 | 50,613 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,574 |
class MLflowCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting
environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`.
"""
def __init__(self):
if not is_mlflow_available():
raise RuntimeErro... | class_definition | 50,616 | 60,582 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,575 |
class DagsHubCallback(MLflowCallback):
"""
A [`TrainerCallback`] that logs to [DagsHub](https://dagshub.com/). Extends [`MLflowCallback`]
"""
def __init__(self):
super().__init__()
if not is_dagshub_available():
raise ImportError("DagsHubCallback requires dagshub to be insta... | class_definition | 60,585 | 62,364 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,576 |
class NeptuneMissingConfiguration(Exception):
def __init__(self):
super().__init__(
"""
------ Unsupported ---- We were not able to create new runs. You provided a custom Neptune run to
`NeptuneCallback` with the `run` argument. For the integration to work fully, provide your `ap... | class_definition | 62,367 | 62,812 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,577 |
class NeptuneCallback(TrainerCallback):
"""TrainerCallback that sends the logs to [Neptune](https://app.neptune.ai).
Args:
api_token (`str`, *optional*): Neptune API token obtained upon registration.
You can leave this argument out if you have saved your token to the `NEPTUNE_API_TOKEN` env... | class_definition | 62,815 | 74,985 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,578 |
class CodeCarbonCallback(TrainerCallback):
"""
A [`TrainerCallback`] that tracks the CO2 emission of training.
"""
def __init__(self):
if not is_codecarbon_available():
raise RuntimeError(
"CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install co... | class_definition | 74,988 | 76,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,579 |
class ClearMLCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/).
Environment:
- **CLEARML_PROJECT** (`str`, *optional*, defaults to `HuggingFace Transformers`):
ClearML project name.
- **CLEARML_TASK** (`str`, *optional*, defaults to `Tr... | class_definition | 76,493 | 89,037 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,580 |
class FlyteCallback(TrainerCallback):
"""A [`TrainerCallback`] that sends the logs to [Flyte](https://flyte.org/).
NOTE: This callback only works within a Flyte task.
Args:
save_log_history (`bool`, *optional*, defaults to `True`):
When set to True, the training logs are saved as a Flyt... | class_definition | 89,040 | 91,521 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,581 |
class DVCLiveCallback(TrainerCallback):
"""
A [`TrainerCallback`] that sends the logs to [DVCLive](https://www.dvc.org/doc/dvclive).
Use the environment variables below in `setup` to configure the integration. To customize this callback beyond
those environment variables, see [here](https://dvc.org/doc... | class_definition | 91,524 | 96,229 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/integration_utils.py | null | 10,582 |
class UserCommands(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
login_parser = parser.add_parser("login", help="Log in using the same credentials as on huggingface.co")
login_parser.set_defaults(func=lambda args: LoginCommand(args))
whoami_p... | class_definition | 844 | 2,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,583 |
class ANSI:
"""
Helper for en.wikipedia.org/wiki/ANSI_escape_code
"""
_bold = "\u001b[1m"
_red = "\u001b[31m"
_gray = "\u001b[90m"
_reset = "\u001b[0m"
@classmethod
def bold(cls, s):
return f"{cls._bold}{s}{cls._reset}"
@classmethod
def red(cls, s):
return ... | class_definition | 2,572 | 3,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,584 |
class BaseUserCommand:
def __init__(self, args):
self.args = args | class_definition | 3,614 | 3,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,585 |
class LoginCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"ERROR! `huggingface-cli login` uses an outdated login mechanism "
"that is not compatible with the Hugging Face Hub backend anymore. "
"Please use `huggingface-cli login instead.... | class_definition | 3,694 | 4,039 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,586 |
class WhoamiCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
"`huggingface-cli whoami` instead."
)
)
token = HfFolder.get_token()
... | class_definition | 4,042 | 4,707 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,587 |
class LogoutCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"ERROR! `transformers-cli logout` uses an outdated logout mechanism "
"that is not compatible with the Hugging Face Hub backend anymore. "
"Please use `huggingface-cli logout ins... | class_definition | 4,710 | 5,060 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,588 |
class RepoCreateCommand(BaseUserCommand):
def run(self):
print(
ANSI.red(
"WARNING! Managing repositories through transformers-cli is deprecated. "
"Please use `huggingface-cli` instead."
)
)
token = HfFolder.get_token()
if toke... | class_definition | 5,063 | 7,090 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/user.py | null | 10,589 |
class RunCommand(BaseTransformersCLICommand):
def __init__(self, nlp: Pipeline, reader: PipelineDataFormat):
self._nlp = nlp
self._reader = reader
@staticmethod
def register_subcommand(parser: ArgumentParser):
run_parser = parser.add_parser("run", help="Run a pipeline through the CL... | class_definition | 1,848 | 4,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/run.py | null | 10,590 |
class ModelPatterns:
"""
Holds the basic information about a new model for the add-new-model-like command.
Args:
model_name (`str`): The model name.
checkpoint (`str`): The checkpoint to use for doc examples.
model_type (`str`, *optional*):
The model type, the identifier... | class_definition | 1,356 | 4,741 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py | null | 10,591 |
class AddNewModelLikeCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
add_new_model_like_parser = parser.add_parser("add-new-model-like")
add_new_model_like_parser.add_argument(
"--config_file", type=str, help="A file with all the in... | class_definition | 60,387 | 62,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/add_new_model_like.py | null | 10,592 |
class EnvironmentCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
download_parser.add_argument(
"--accelerate-config_file",
... | class_definition | 1,136 | 5,755 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/env.py | null | 10,593 |
class ServeModelInfoResult(BaseModel):
"""
Expose model information
"""
infos: dict | class_definition | 1,721 | 1,821 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py | null | 10,594 |
class ServeTokenizeResult(BaseModel):
"""
Tokenize result model
"""
tokens: List[str]
tokens_ids: Optional[List[int]] | class_definition | 1,824 | 1,962 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py | null | 10,595 |
class ServeDeTokenizeResult(BaseModel):
"""
DeTokenize result model
"""
text: str | class_definition | 1,965 | 2,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py | null | 10,596 |
class ServeForwardResult(BaseModel):
"""
Forward result model
"""
output: Any | class_definition | 2,066 | 2,160 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py | null | 10,597 |
class ServeCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
Args:
parser: Root parser to register command-specific arguments
"""
... | class_definition | 2,163 | 8,026 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/serving.py | null | 10,598 |
class DownloadCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("download")
download_parser.add_argument(
"--cache-dir", type=str, default=None, help="Path to location to store the models"
)... | class_definition | 816 | 2,394 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/commands/download.py | null | 10,599 |
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