text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "efficientformer", None) is not None:
with tf.name_scope(self.efficientformer.name):
self.efficientformer.build(None) | 10,482 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerForImageClassification(TFEfficientFormerPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config: EfficientFormerConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = TFEfficientFormerMainLayer(config, name="effi... | 10,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def ... | 10,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
outputs = self.efficientformer(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.classi... | 10,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "efficientformer", None) is not None:
with tf.name_scope(self.efficientformer.name):
self.efficientformer.build(None)
if getattr(self, "classifier", None) i... | 10,483 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerForImageClassificationWithTeacherOutput(ModelOutput):
"""
Args:
Output type of [`EfficientFormerForImageClassificationWithTeacher`].
logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
Prediction scores as the average of the cls_logits and distillation... | 10,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus
the initial embedding outputs.
attentions (`tuple(tf.Tensor)`, *optional*, ret... | 10,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
logits: tf.Tensor = None
cls_logits: tf.Tensor = None
distillation_logits: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None | 10,484 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class TFEfficientFormerForImageClassificationWithTeacher(TFEfficientFormerPreTrainedModel):
def __init__(self, config: EfficientFormerConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.efficientformer = TFEfficientFormerMainLayer(config, name="efficientformer... | 10,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(EFFICIENTFORMER_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=TFEfficientFormerForImageClassificationWithTeacherOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLAS... | 10,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
outputs = self.efficientformer(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
cls_logits = self.cl... | 10,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "efficientformer", None) is not None:
with tf.name_scope(self.efficientformer.name):
self.efficientformer.build(None)
if getattr(self, "classifier", None) i... | 10,485 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_tf_efficientformer.py |
class ScaNNSearcher:
"""Note that ScaNNSearcher cannot currently be used within the model. In future versions, it might however be included."""
def __init__(
self,
db,
num_neighbors,
dimensions_per_block=2,
num_leaves=1000,
num_leaves_to_search=100,
train... | 10,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
def search_batched(self, question_projection):
retrieved_block_ids, _ = self.searcher.search_batched(question_projection.detach().cpu())
return retrieved_block_ids.astype("int64") | 10,486 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
class RealmRetriever:
"""The retriever of REALM outputting the retrieved evidence block and whether the block has answers as well as answer
positions."
Parameters:
block_records (`np.ndarray`):
A numpy array which cantains evidence texts.
tokenizer ([`RealmTokeni... | 10,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
text = []
text_pair = []
for retrieved_block in retrieved_blocks:
text.append(question)
text_pair.append(retrieved_block.decode())
concat_inputs = self.tokenizer(
text, text_pair, padding=True, truncation=True, return_special_tokens_mask=True, max_length=max_... | 10,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *init_inputs, **kwargs):
if os.path.isdir(pretrained_model_name_or_path):
block_records_path = os.path.join(pretrained_model_name_or_path, _REALM_BLOCK_RECORDS_FILENAME)
else:
... | 10,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
def block_has_answer(self, concat_inputs, answer_ids):
"""check if retrieved_blocks has answers."""
has_answers = []
start_pos = []
end_pos = []
max_answers = 0
for input_id in concat_inputs.input_ids:
input_id_list = input_id.tolist()
# Check ans... | 10,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
if len(start_pos[-1]) == 0:
has_answers.append(False)
else:
has_answers.append(True)
if len(start_pos[-1]) > max_answers:
max_answers = len(start_pos[-1])
# Pad -1 to max_answers
for start_pos_, end_pos_ in zip(start_pos, e... | 10,487 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/retrieval_realm.py |
class RealmTokenizer(PreTrainedTokenizer):
r"""
Construct a REALM tokenizer.
[`RealmTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation splitting and
wordpiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Use... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic toke... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengt... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Whether or not to tokenize Chinese characters. | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
stri... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
super().__init__(
do_lower_case=do_lower_case,
... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
@property
def do_lower_case(self):
return self.basic_tokenizer.do_lower_case
@property
def vocab_size(self):
return len(self.vocab)
def get_vocab(self):
return dict(self.vocab, **self.added_tokens_encoder)
def _tokenize(self, text):
split_tokens = []
if sel... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.ids_to_tokens.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = "... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Args:
text (`List[List[str]]`):
The batch of sequences to be encoded. Each sequence must be in this format: (batch_size,
num_candidates, text).
text_pair (`List[List[str]]`, *optional*):
The batch of sequences to be encoded. Each sequence must be i... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
# Always using a fixed sequence length to encode in order to stack candidates into a batch.
kwargs["padding"] = PaddingStrategy.MAX_LENGTH
batch_text = text
batch_text_pair = kwargs.pop("text_pair", None)
return_tensors = kwargs.pop("return_tensors", None)
output_data = {
... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
if encoded_input_ids is not None:
output_data["input_ids"].append(encoded_input_ids)
if encoded_attention_mask is not None:
output_data["attention_mask"].append(encoded_attention_mask)
if encoded_token_type_ids is not None:
output_data["token_type_... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) wit... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A REALM sequence
pair mask has the following format:
... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
... | 10,488 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
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... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
if never_split is None:
never_split = []
self.do_lower_case = do_lower_case
self.never_split = set(never_split)
self.tokenize_chinese_chars = tokenize_chinese_chars
... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
Args:
never_split (`List[str]`, *optional*)
Kept for backward compatibility purposes. Now implemented directly at the base class level (see
[`PreTrainedTokenizer.tokenize`]) List of token not to split.
"""
# union() returns a new set by concatenating the two s... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
#... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
split_tokens.extend(self._run_split_on_punc(token, never_split)) | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def _run_split_on_punc(self, text, never_split=None):
"""Splits punctuation on a piece of text."""
if never_split is not None and text in never_split:
return [text]
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is ... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
or (cp >= 0x2F800 and cp <= 0x2FA1F) #
): #
return True | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xFFFD or _is_control(char):
continue
if _is_whitespace(cha... | 10,489 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
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... | 10,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
... | 10,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens | 10,490 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm.py |
class RealmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of
1. [`RealmEmbedder`]
2. [`RealmScorer`]
3. [`RealmKnowledgeAugEncoder`]
4. [`RealmRetriever`]
5. [`RealmReader`]
6. [`RealmForOpenQA`]
It is used to instantiate an REALM model ac... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the REALM model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`], [`RealmKnowledgeAugEncoder`], or
[`RealmRead... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
intermediate_size (`int`, *optional*, defaults to 3072):
Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
The non-linear activation function (function or string) in the encoder a... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`RealmEmbedder`], [`RealmScorer`],
[`RealmKnowledgeAugEncoder`], or [`RealmReader`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard de... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
reader_seq_len (`int`, *optional*, defaults to 288+32):
Maximum sequence length of the reader.
num_block_records (`int`, *optional*, defaults to 13353718):
Number of block records.
searcher_beam_size (`int`, *optional*, defaults to 5000):
Beam size of the searcher. No... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
Example:
```python
>>> from transformers import RealmConfig, RealmEmbedder
>>> # Initializing a REALM realm-cc-news-pretrained-* style configuration
>>> configuration = RealmConfig()
>>> # Initializing a model (with random weights) from the google/realm-cc-news-pretrained-embedder style configura... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
retriever_proj_size=128,
num_hidden_layers=12,
num_attention_heads=12,
num_candidates=8,
intermediate_size=3072,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
attention_probs_... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
# Common config
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.retriever_proj_size = retriever_proj_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
... | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
# Retrieval config
self.num_block_records = num_block_records
self.searcher_beam_size = searcher_beam_size | 10,491 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/configuration_realm.py |
class RealmTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" REALM tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
[`RealmTokenizerFast`] is identical to [`BertTokenizerFast`] and runs end-to-end tokenization: punctuation
splitting and wordpiece.
This to... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
unk_token (`str`, *optional*, defaults to `"[UNK]"`):
The unknown token. A token that is no... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
The token used f... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
wordpieces_prefix (`str`, *optional*, defaults to `"##"`):
The prefix for subwords.
... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class = RealmTokenizer
def __init__(
self,
vocab_file=None,
tokenizer_file=None,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_toke... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
if (
normalizer_state.get("lowercase", do_lower_case) != do_lower_case
or normalizer_state.get("strip_accents", strip_accents) != strip_accents
or normalizer_state.get("handle_chinese_chars", toke... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
2. Always pad the sequences to *max_length*.
3. Must specify *max_length* in order to stack packs of candidates into a batch.
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
>>> # batch_size = 2, num_candidates = 2
>>> text = [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]
>>> tokenizer = RealmTokenizerFast.from_pretrained("google/realm-cc-news-pretrained-encoder")
>>> tokenized_text = tokenizer.batch_encode_candidates(text, max_leng... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
for idx, candidate_text in enumerate(batch_text):
if batch_text_pair is not None:
candidate_text_pair = batch_text_pair[idx]
else:
candidate_text_pair = None
encoded_candidates = super().__call__(candidate_text, candidate_text_pair, return_tensors=Non... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
output_data = {key: item for key, item in output_data.items() if len(item) != 0}
return BatchEncoding(output_data, tensor_type=return_tensors)
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequenc... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
if token_ids_1 is not None:
output += token_ids_1 + [self.sep_token_id]
return output
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to b... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
Returns:
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
"""
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_... | 10,492 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/tokenization_realm_fast.py |
class RealmEmbeddings(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... | 10,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len ... | 10,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
... | 10,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 10,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 10,493 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmSelfAttention(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}) ... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
query_layer = self.transpose_for_scores(mixed_query_layer)
use_cache = past_key_value is not None
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all ... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = q... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("b... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in RealmModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention sco... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
i... | 10,494 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmSelfOutput(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 ... | 10,495 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = REALM_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type
)
self.output = RealmSelfOutput(con... | 10,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 10,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,496 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmIntermediate(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... | 10,497 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmOutput(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... | 10,498 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
class RealmLayer(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 = RealmAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config... | 10,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 10,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
... | 10,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = out... | 10,499 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
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 | 10,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
next_decoder_cache = () if use_ca... | 10,500 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/realm/modeling_realm.py |
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