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 TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnsweringLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFXLNetMainLayer(config, name="transformer")
self.qa_outputs = keras.layers.Dense(
... | class_definition | 73,008 | 77,602 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/modeling_tf_xlnet.py | null | 8,900 |
class XLNetConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`XLNetModel`] or a [`TFXLNetModel`]. It is used to
instantiate a XLNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults wi... | class_definition | 903 | 10,924 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/configuration_xlnet.py | null | 8,901 |
class XLNetTokenizer(PreTrainedTokenizer):
"""
Construct an XLNet tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regard... | class_definition | 1,167 | 15,700 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/tokenization_xlnet.py | null | 8,902 |
class XLNetTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" XLNet tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTokenizer... | class_definition | 1,340 | 9,363 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xlnet/tokenization_xlnet_fast.py | null | 8,903 |
class MPNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MPNetModel`] or a [`TFMPNetModel`]. It is used to
instantiate a MPNet model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | class_definition | 848 | 5,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/configuration_mpnet.py | null | 8,904 |
class MPNetTokenizer(PreTrainedTokenizer):
"""
This tokenizer inherits from [`BertTokenizer`] which contains most of the methods. Users should refer to the
superclass for more information regarding methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
do_lower_cas... | class_definition | 1,605 | 13,653 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | null | 8,905 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 13,728 | 20,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | null | 8,906 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 20,555 | 22,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | null | 8,907 |
class TFMPNetPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MPNetConfig
base_model_prefix = "mpnet" | class_definition | 1,864 | 2,121 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,908 |
class TFMPNetEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.padding_idx = 1
self.config = config
self.hidden_size = config.hidden_size
self.max_position... | class_definition | 2,124 | 5,559 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,909 |
class TFMPNetPooler(keras.layers.Layer):
def __init__(self, config: MPNetConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
... | class_definition | 5,648 | 6,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,910 |
class TFMPNetSelfAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attenti... | class_definition | 6,622 | 10,519 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,911 |
class TFMPNetAttention(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.attn = TFMPNetSelfAttention(config, name="attn")
self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
self.dropout = keras.l... | class_definition | 10,522 | 11,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,912 |
class TFMPNetIntermediate(keras.layers.Layer):
def __init__(self, config: MPNetConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if ... | class_definition | 11,992 | 13,016 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,913 |
class TFMPNetOutput(keras.layers.Layer):
def __init__(self, config: MPNetConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm =... | class_definition | 13,105 | 14,436 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,914 |
class TFMPNetLayer(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.attention = TFMPNetAttention(config, name="attention")
self.intermediate = TFMPNetIntermediate(config, name="intermediate")
self.out = TFMPNetOutput(config, name="output")
... | class_definition | 14,439 | 16,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,915 |
class TFMPNetEncoder(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.n_heads = config.num_attention_heads
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states... | class_definition | 16,028 | 20,368 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,916 |
class TFMPNetMainLayer(keras.layers.Layer):
config_class = MPNetConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.num_hidden_layers = config.num_hidden_layers
self.initializer_range = config.initializer_range
self.output_a... | class_definition | 20,391 | 25,934 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,917 |
class TFMPNetModel(TFMPNetPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mpnet = TFMPNetMainLayer(config, name="mpnet")
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequenc... | class_definition | 31,410 | 33,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,918 |
class TFMPNetLMHead(keras.layers.Layer):
"""MPNet head for masked and permuted language modeling"""
def __init__(self, config, input_embeddings, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.dense = keras.layers.Dense(
... | class_definition | 33,132 | 35,558 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,919 |
class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"pooler"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mpnet = TFMPNetMainLayer(config, name="mpnet")
self.lm_head = TF... | class_definition | 35,665 | 38,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,920 |
class TFMPNetClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initia... | class_definition | 38,919 | 40,327 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,921 |
class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassificationLoss):
_keys_to_ignore_on_load_missing = [r"pooler"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mpnet = TFMPNe... | class_definition | 40,551 | 43,611 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,922 |
class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mpnet = TFMPNetMainLayer(config, name="mpnet")
self.dropout = keras.layers.Dropout(config.hidden_dropout_prob)
... | class_definition | 43,844 | 47,752 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,923 |
class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificationLoss):
_keys_to_ignore_on_load_missing = [r"pooler"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mpnet = TFMPNetMainL... | class_definition | 47,992 | 51,126 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,924 |
class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLoss):
_keys_to_ignore_on_load_missing = [r"pooler"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mpnet = TFMPNetMainLayer... | class_definition | 51,415 | 55,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_tf_mpnet.py | null | 8,925 |
class MPNetTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" MPNet tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more... | class_definition | 1,137 | 9,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.py | null | 8,926 |
class MPNetPreTrainedModel(PreTrainedModel):
config_class = MPNetConfig
base_model_prefix = "mpnet"
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initializa... | class_definition | 1,558 | 2,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,927 |
class MPNetEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.padding_idx = 1
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
self.position_embeddings = nn.Embedding(
config.max_position_embe... | class_definition | 2,479 | 4,759 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,928 |
class MPNetSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numbe... | class_definition | 4,762 | 7,464 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,929 |
class MPNetAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.attn = MPNetSelfAttention(config)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.pruned_heads = set()... | class_definition | 7,467 | 9,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,930 |
class MPNetIntermediate(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 | 9,196 | 9,762 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,931 |
class MPNetOutput(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 | 9,829 | 10,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,932 |
class MPNetLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.attention = MPNetAttention(config)
self.intermediate = MPNetIntermediate(config)
self.output = MPNetOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
... | class_definition | 10,441 | 11,453 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,933 |
class MPNetEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.n_heads = config.num_attention_heads
self.layer = nn.ModuleList([MPNetLayer(config) for _ in range(config.num_hidden_layers)])
self.relative_attention_bias = nn.Embedding(c... | class_definition | 11,456 | 14,866 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,934 |
class MPNetPooler(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 | 14,933 | 15,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,935 |
class MPNetModel(MPNetPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = MPNetEmbeddings(config)
self.encoder = MPNetEncoder(config)
self.pooler = MPNetPooler(config) if add_pooling_layer else... | class_definition | 18,805 | 22,714 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,936 |
class MPNetForMaskedLM(MPNetPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder"]
def __init__(self, config):
super().__init__(config)
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.lm_head = MPNetLMHead(config)
# Initialize weights and apply final processi... | class_definition | 22,717 | 25,688 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,937 |
class MPNetLMHead(nn.Module):
"""MPNet Head for masked and permuted language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | class_definition | 25,691 | 26,604 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,938 |
class MPNetForSequenceClassification(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.classifier = MPNetClassificationHead(config)
# Initialize weigh... | class_definition | 26,828 | 30,479 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,939 |
class MPNetForMultipleChoice(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mpnet = MPNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply f... | class_definition | 30,712 | 34,061 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,940 |
class MPNetForTokenClassification(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = ... | class_definition | 34,292 | 36,926 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,941 |
class MPNetClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Li... | class_definition | 36,929 | 37,569 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,942 |
class MPNetForQuestionAnswering(MPNetPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.mpnet = MPNetModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Init... | class_definition | 37,858 | 42,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mpnet/modeling_mpnet.py | null | 8,943 |
class PhobertTokenizer(PreTrainedTokenizer):
"""
Construct a PhoBERT tokenizer. Based on Byte-Pair-Encoding.
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:
... | class_definition | 1,383 | 13,090 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phobert/tokenization_phobert.py | null | 8,944 |
class ErnieConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ErnieModel`] or a [`TFErnieModel`]. It is used to
instantiate a ERNIE model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults w... | class_definition | 960 | 7,098 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/configuration_ernie.py | null | 8,945 |
class ErnieOnnxConfig(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 | 7,101 | 7,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/configuration_ernie.py | null | 8,946 |
class ErnieEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embedd... | class_definition | 1,811 | 5,567 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,947 |
class ErnieSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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}) ... | class_definition | 5,658 | 13,002 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,948 |
class ErnieSelfOutput(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 | 13,090 | 13,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,949 |
class ErnieAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = ERNIE_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = ErnieSelfOutput(con... | class_definition | 13,866 | 15,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,950 |
class ErnieIntermediate(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 | 16,081 | 16,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,951 |
class ErnieOutput(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 | 16,731 | 17,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,952 |
class ErnieLayer(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 = ErnieAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config... | class_definition | 17,423 | 21,335 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,953 |
class ErnieEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([ErnieLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torc... | class_definition | 21,420 | 25,212 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,954 |
class ErniePooler(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 | 25,296 | 25,856 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,955 |
class ErniePredictionHeadTransform(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 | 25,957 | 26,658 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,956 |
class ErnieLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = ErniePredictionHeadTransform(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 | 26,752 | 27,586 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,957 |
class ErnieOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = ErnieLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 27,675 | 27,991 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,958 |
class ErnieOnlyNSPHead(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 | 28,080 | 28,385 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,959 |
class ErniePreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = ErnieLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.prediction... | class_definition | 28,479 | 28,944 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,960 |
class ErniePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ErnieConfig
base_model_prefix = "ernie"
supports_gradient_checkpointing = True
def _init_weights(... | class_definition | 28,947 | 30,054 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,961 |
class ErnieForPreTrainingOutput(ModelOutput):
"""
Output type of [`ErnieForPreTraining`].
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 | 30,163 | 32,115 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,962 |
class ErnieModel(ErniePreTrainedModel):
"""
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 | 36,226 | 45,813 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,963 |
class ErnieForPreTraining(ErniePreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
# Copied from transformers.models.bert.modeling_bert.BertForPreTraining.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)... | class_definition | 46,049 | 51,077 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,964 |
class ErnieForCausalLM(ErniePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
# Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->ErnieForCausalLM,Bert->Ernie,bert->ernie
def __init__(se... | class_definition | 51,210 | 57,731 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,965 |
class ErnieForMaskedLM(ErniePreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"]
# Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
... | class_definition | 57,838 | 62,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,966 |
class ErnieForNextSentencePrediction(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForNextSentencePrediction.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.ernie = ErnieModel(config)
self.cls = ErnieOnlyN... | class_definition | 62,882 | 67,004 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,967 |
class ErnieForSequenceClassification(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = conf... | class_definition | 67,228 | 71,283 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,968 |
class ErnieForMultipleChoice(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.ernie = ErnieModel(config)
classifier_dropout = (
co... | class_definition | 71,516 | 75,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,969 |
class ErnieForTokenClassification(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie = ErnieModel... | class_definition | 75,597 | 78,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,970 |
class ErnieForQuestionAnswering(ErniePreTrainedModel):
# Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->Ernie,bert->ernie
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ernie = ErnieModel(con... | class_definition | 78,791 | 83,047 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ernie/modeling_ernie.py | null | 8,971 |
class InstructBlipForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`InstructBlipForConditionalGeneration`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Language modeling loss f... | class_definition | 1,878 | 3,317 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,972 |
class InstructBlipVisionEmbeddings(nn.Module):
def __init__(self, config: InstructBlipVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embeddi... | class_definition | 3,418 | 6,844 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,973 |
class InstructBlipAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim =... | class_definition | 6,944 | 10,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,974 |
class InstructBlipMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden... | class_definition | 10,249 | 10,827 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,975 |
class InstructBlipEncoderLayer(nn.Module):
def __init__(self, config: InstructBlipConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = InstructBlipAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp... | class_definition | 10,924 | 12,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,976 |
class InstructBlipPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = InstructBlipConfig
base_model_prefix = "blip"
supports_gradient_checkpointing = True
_no_s... | class_definition | 12,797 | 14,500 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,977 |
class InstructBlipEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`InstructBlipEncoderLayer`].
Args:
config (`InstructBlipConfig`):
The corresponding vision configuration for the `InstructBlipEncoder`.
... | class_definition | 20,015 | 23,794 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,978 |
class InstructBlipVisionModel(InstructBlipPreTrainedModel):
main_input_name = "pixel_values"
config_class = InstructBlipVisionConfig
def __init__(self, config: InstructBlipVisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embed... | class_definition | 23,910 | 26,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,979 |
class InstructBlipQFormerMultiHeadAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
... | class_definition | 26,452 | 33,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,980 |
class InstructBlipQFormerSelfOutput(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_p... | class_definition | 33,207 | 33,828 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,981 |
class InstructBlipQFormerAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = InstructBlipQFormerMultiHeadAttention(config, is_cross_attention)
self.output = InstructBlipQFormerSelfOutput(config)
self.pruned_heads = set()
... | class_definition | 33,935 | 36,088 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,982 |
class InstructBlipQFormerIntermediate(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:
... | class_definition | 36,192 | 36,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,983 |
class InstructBlipQFormerOutput(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... | class_definition | 36,870 | 37,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,984 |
class InstructBlipQFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = InstructBlipQFormerAttention(config)
self.layer_idx = layer_idx
i... | class_definition | 37,496 | 41,490 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,985 |
class InstructBlipQFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[InstructBlipQFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointin... | class_definition | 41,595 | 45,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,986 |
class InstructBlipQFormerEmbeddings(nn.Module):
"""Construct the embeddings from word and position 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.position_embe... | class_definition | 45,104 | 47,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,987 |
class InstructBlipQFormerModel(InstructBlipPreTrainedModel):
"""
Querying Transformer (Q-Former), used in InstructBLIP. Slightly modified from BLIP-2 as it also takes the
instruction as input.
"""
def __init__(self, config: InstructBlipQFormerConfig):
super().__init__(config)
self.c... | class_definition | 47,106 | 56,931 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,988 |
class InstructBlipForConditionalGeneration(InstructBlipPreTrainedModel, GenerationMixin):
config_class = InstructBlipConfig
main_input_name = "pixel_values"
def __init__(self, config: InstructBlipConfig):
super().__init__(config)
self.vision_model = InstructBlipVisionModel(config.vision_co... | class_definition | 57,431 | 75,246 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/modeling_instructblip.py | null | 8,989 |
class InstructBlipProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"stride": 0,
"return_overflowing_tokens": False,
"return_special_tokens_mask": False,
"return... | class_definition | 1,150 | 1,650 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/processing_instructblip.py | null | 8,990 |
class InstructBlipProcessor(ProcessorMixin):
r"""
Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the
docstring of ... | class_definition | 1,653 | 10,402 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/processing_instructblip.py | null | 8,991 |
class InstructBlipVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVisionModel`]. It is used to
instantiate a InstructBLIP vision encoder according to the specified arguments, defining the model architecture.
Instantiating a configuration... | class_definition | 910 | 4,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/configuration_instructblip.py | null | 8,992 |
class InstructBlipQFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipQFormerModel`]. It is used to
instantiate a InstructBLIP Querying Transformer (Q-Former) model according to the specified arguments, defining the
model architecture. Inst... | class_definition | 4,852 | 10,623 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/configuration_instructblip.py | null | 8,993 |
class InstructBlipConfig(PretrainedConfig):
r"""
[`InstructBlipConfig`] is the configuration class to store the configuration of a
[`InstructBlipForConditionalGeneration`]. It is used to instantiate a InstructBLIP model according to the specified
arguments, defining the vision model, Q-Former model and ... | class_definition | 10,626 | 15,670 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblip/configuration_instructblip.py | null | 8,994 |
class BrosSpadeOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
initial_token_logits (`torch.FloatTensor` of shape `(batch_size,... | class_definition | 5,486 | 7,322 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 8,995 |
class BrosPositionalEmbedding1D(nn.Module):
# Reference: https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/mem_transformer.py#L15
def __init__(self, config):
super(BrosPositionalEmbedding1D, self).__init__()
self.dim_bbox_sinusoid_emb_1d = config.dim_bbox_sinusoid_emb_1d
... | class_definition | 7,325 | 8,177 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 8,996 |
class BrosPositionalEmbedding2D(nn.Module):
def __init__(self, config):
super(BrosPositionalEmbedding2D, self).__init__()
self.dim_bbox = config.dim_bbox
self.x_pos_emb = BrosPositionalEmbedding1D(config)
self.y_pos_emb = BrosPositionalEmbedding1D(config)
def forward(self, bbox... | class_definition | 8,180 | 8,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 8,997 |
class BrosBboxEmbeddings(nn.Module):
def __init__(self, config):
super(BrosBboxEmbeddings, self).__init__()
self.bbox_sinusoid_emb = BrosPositionalEmbedding2D(config)
self.bbox_projection = nn.Linear(config.dim_bbox_sinusoid_emb_2d, config.dim_bbox_projection, bias=False)
def forward(se... | class_definition | 8,832 | 9,422 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 8,998 |
class BrosTextEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_em... | class_definition | 9,425 | 12,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/bros/modeling_bros.py | null | 8,999 |
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