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class FlaubertForQuestionAnsweringOutput(ModelOutput):
"""
Base class for outputs of question answering models using a `SquadHead`. | 9,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.
start_top_log_probs (`torch.FloatTens... | 9,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
(beam-search).
end_top_index (`torch.LongTensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
In... | 9,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 9,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
class FlaubertForQuestionAnswering(FlaubertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = FlaubertModel(config)
self.qa_outputs = SQuADHead(config)
# Initialize weights and apply final processing
self.post_init() | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
@add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=FlaubertForQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Op... | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, FlaubertForQuestionAnsweringOutput]:
r"""
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for pos... | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
is_impossible (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels whether a question has an answer or no answer (SQuAD 2.0)
cls_index (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the classification token to use as input for compu... | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
Returns:
Example:
```python
>>> from transformers import XLMTokenizer, XLMForQuestionAnswering
>>> import torch
>>> tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-mlm-en-2048")
>>> model = XLMForQuestionAnswering.from_pretrained("FacebookAI/xlm-mlm-en-2048")
... | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
inputs_e... | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
return FlaubertForQuestionAnsweringOutput(
loss=outputs.loss,
start_top_log_probs=outputs.start_top_log_probs,
start_top_index=outputs.start_top_index,
end_top_log_probs=outputs.end_top_log_probs,
end_top_index=outputs.end_top_index,
cls_logits=out... | 9,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
class FlaubertForMultipleChoice(FlaubertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = FlaubertModel(config)
self.sequence_summary = SequenceSummary(config)
self.logits_proj = nn.Linear(config.num_label... | 9,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
@add_start_docstrings_to_model_forward(
FLAUBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
... | 9,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
) -> Union[Tuple, MultipleChoiceModelOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimensio... | 9,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
... | 9,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
transformer_outputs = self.transformer(
input_ids=input_ids,
attention_mask=attention_mask,
langs=langs,
token_type_ids=token_type_ids,
position_ids=position_ids,
lengths=lengths,
cache=cache,
head_mask=head_mask,
... | 9,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 9,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/modeling_flaubert.py |
class FlaubertConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`FlaubertModel`] or a [`TFFlaubertModel`]. It is
used to instantiate a FlauBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
Args:
pre_norm (`bool`, *optional*, defaults to `False`):
Whether to apply the layer normalization before or after the feed forward layer following the attention in
each layer (Vaswani et al., Tensor2Tensor for Neural Machine Translation. 2018)
layerdrop (`float`, *optional*, def... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_drop... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
order to only attend to the left-side context instead if a bidirectional context.
asm (`bool`, *optional*, defaults to `False`):
Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
layer.
n_langs (`int`, *optional*, defaults... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
embed_init_std (`float`, *optional*, defaults to 2048^-0.5):
The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
init_std (`int`, *optional*, defaults to 50257):
The standard deviation of the truncated_normal_initializer for initializing al... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
mask_index (`int`, *optional*, defaults to 5):
The index of the masking token in the vocabulary.
is_encoder(`bool`, *optional*, defaults to `True`):
Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
summary_type (`string`, *... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
Has to be one of the following options:
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of class... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Used in the sequence classification and multiple choice models.
Whether the projection outputs should have `config.num_labels... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
The dropout ratio to be used after the projection and activation.
start_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
end_n_top (`int`, *optional*, defaults to 5):
Used in the SQuAD evaluation script.
mask_token_id (`int`, *optional*, defa... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
def __init__(
self,
pre_norm=False,
layerdrop=0.0,
vocab_size=30145,
emb_dim=2048,
n_layers=12,
n_heads=16,
dropout=0.1,
attention_dropout=0.1,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=Fal... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
self.pre_norm = pre_norm
self.layerdrop = layerdrop
self.vocab_size = vocab_size
self.emb_dim = emb_dim
self.n_layers = n_layers
self.n_heads = n_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.gelu_activation = gelu_activation... | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
self.summary_activation = summary_activation
self.summary_proj_to_labels = summary_proj_to_labels
self.summary_first_dropout = summary_first_dropout
self.start_n_top = start_n_top
self.end_n_top = end_n_top
self.mask_token_id = mask_token_id
self.lang_id = lang_id | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
if "n_words" in kwargs:
self.n_words = kwargs["n_words"]
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs) | 9,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
class FlaubertOnnxConfig(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(
... | 9,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/configuration_flaubert.py |
class FlaubertTokenizer(PreTrainedTokenizer):
"""
Construct a Flaubert tokenizer. Based on Byte-Pair Encoding. The tokenization process is the following:
- Moses preprocessing and tokenization.
- Normalizing all inputs text.
- The arguments `special_tokens` and the function `set_special_tokens`, ca... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
Args:
vocab_file (`str`):
Vocabulary file.
merges_file (`str`):
Merges file.
do_lowercase (`bool`, *optional*, defaults to `False`):
Controls lower casing.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token tha... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequenc... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
modeling. This is the token which the model will try to predict.
additional_special_tokens (`List[str]`, *optional*, defaults to `['<special0>', '<special1>', '<special2>', '<special3>', '<special4>', '<special5>', '<special6>', '<special7>', '<special8>', '<special9>']`):
List of additional special... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
vocab_files_names = VOCAB_FILES_NAMES | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
def __init__(
self,
vocab_file,
merges_file,
do_lowercase=False,
unk_token="<unk>",
bos_token="<s>",
sep_token="</s>",
pad_token="<pad>",
cls_token="</s>",
mask_token="<special1>",
additional_special_tokens=[
"<special0>... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
# always `False`
self.do_lowercase_and_remove_accent = False | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
self.do_lowercase = do_lowercase
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use FlaubertTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.sm ... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
with open(merges_file, encoding="utf-8") as merges_handle:
merges = merges_handle.read().split("\n")[:-1]
merges = [tuple... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
@property
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.do_lower_case
def do_lower_case(self):
return self.do_lowercase_and_remove_accent
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_punct_norm
def moses_punct_norm(self, text, lang):
if... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_tokenize
def moses_tokenize(self, text, lang):
if lang not in self.cache_moses_tokenizer:
moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
self.cache_moses_tokenizer[lang] = moses_tokenizer
else:
... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
self.ja_word_tokenizer = Mykytea.Mykytea(
f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
)
except (AttributeError, ImportError):
logger.error(
"Make sure you install KyTea (https://github.com/neubig/kytea) and it's pyth... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_vocab
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.bpe
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
- [sacremoses](https://github.com/alvations/sacremoses): port of Moses
- Install with `pip install sacremoses`
Args:
- bypass_tokenizer: Allow users to preprocess and tokenize the sentences externally (default = False)
(bool). If True, we only apply BPE.
Returns:
... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
split_tokens = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(token).split(" ")))
return split_tokens
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_token_to_id
def _convert_token_to_id(self, token):
"""Conver... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.convert_tokens_to_string
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = "".join(tokens).replace("</w>", " ").strip()
return out_string
# Copied ... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.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... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_special_tokens_mask
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token ... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Returns:
`Li... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
# Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merge_file}: BPE merge i... | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
try:
import sacremoses
except ImportError:
raise ImportError(
"You need to install sacremoses to use XLMTokenizer. "
"See https://pypi.org/project/sacremoses/ for installation."
)
self.sm = sacremoses | 9,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flaubert/tokenization_flaubert.py |
class ViTMAEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ViTMAEModel`]. It is used to instantiate an ViT
MAE model according to the specified arguments, defining the model architecture. Instantiating a configuration with
the defaults will yield a simil... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults t... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
laye... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
Number of attention heads for each attention layer in the decoder.
decoder_hidden_size (`int`, *optional*, defaults to 512):
Dimensionality of the decoder.
decoder_num_hidden_layers (`int`, *optional*, defaults to 8):
Number of hidden layers in the decoder.
decoder_interm... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
Example:
```python
>>> from transformers import ViTMAEConfig, ViTMAEModel
>>> # Initializing a ViT MAE vit-mae-base style configuration
>>> configuration = ViTMAEConfig()
>>> # Initializing a model (with random weights) from the vit-mae-base style configuration
>>> model = ViTMAEModel(configu... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
layer_norm_eps=1e-12,
i... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_pro... | 9,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/configuration_vit_mae.py |
class TFViTMAEModelOutput(ModelOutput):
"""
Class for TFViTMAEModel's outputs, with potential hidden states and attentions. | 9,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
mask (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor indicating which patches are masked (1) and whi... | 9,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, u... | 9,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
last_hidden_state: tf.Tensor = None
mask: tf.Tensor = None
ids_restore: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 9,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEDecoderOutput(ModelOutput):
"""
Class for TFViTMAEDecoder's outputs, with potential hidden states and attentions. | 9,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Args:
logits (`tf.Tensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple ... | 9,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
logits: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 9,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEForPreTrainingOutput(ModelOutput):
"""
Class for TFViTMAEForPreTraining's outputs, with potential hidden states and attentions. | 9,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Args:
loss (`tf.Tensor` of shape `(1,)`):
Pixel reconstruction loss.
logits (`tf.Tensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
Pixel reconstruction logits.
mask (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Tensor... | 9,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`. Attentions weights after the attention softmax, u... | 9,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
loss: tf.Tensor | None = None
logits: tf.Tensor = None
mask: tf.Tensor = None
ids_restore: tf.Tensor = None
hidden_states: Tuple[tf.Tensor] | None = None
attentions: Tuple[tf.Tensor] | None = None | 9,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEEmbeddings(keras.layers.Layer):
"""
Construct the CLS token, position and patch embeddings.
"""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = TFViTMAEPatchEmbeddings(config, name="patch_embeddings")
self.nu... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
self.cls_token = self.add_weight(
shape=(1, 1, self.config.hidden_size),
initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
trainable=True,
name="cls_token",
)
self.position_embeddings... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
if self.built:
return
self.built = True
if getattr(self, "patch_embeddings", None) is not None:
with tf.name_scope(self.patch_embeddings.name):
self.patch_embeddings.build(None)
def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
if num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.position_embeddings[:, 1:]
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
patch_po... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
Args:
sequence (`tf.Tensor` of shape `(batch_size, sequence_length, dim)`)
noise (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) which is
mainly used for testing purposes to control randomness and maintain the reproducibility
"""
batch_size, seq... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# generate the binary mask: 0 is keep, 1 is remove
# this hack is needed because TF's EagerTensors don't support
# assignment
mask_keep = tf.zeros((batch_size, len_keep))
mask_remove = tf.ones((batch_size, seq_length - len_keep))
mask = tf.concat([mask_keep, mask_remove], axis=-1... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def call(
self, pixel_values: tf.Tensor, noise: tf.Tensor = None, interpolate_pos_encoding: bool = False
) -> tf.Tensor:
batch_size, num_channels, height, width = shape_list(pixel_values)
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
... | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
return embeddings, mask, ids_restore | 9,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEPatchEmbeddings(keras.layers.Layer):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
""" | 9,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) els... | 9,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
self.projection = keras.layers.Conv2D(
filters=hidden_size,
kernel_size=patch_size,
strides=patch_size,
padding="valid",
data_format="channels_last",
kernel_initializer="glorot_uniform", # following torch.nn.Linear
bias_initializer="ze... | 9,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def call(
self, pixel_values: tf.Tensor, training: bool = False, interpolate_pos_encoding: bool = False
) -> tf.Tensor:
batch_size, num_channels, height, width = shape_list(pixel_values)
if tf.executing_eagerly():
if num_channels != self.num_channels:
raise ValueE... | 9,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
projection = self.projection... | 9,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAESelfAttention(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **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 nu... | 9,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
self.query = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = keras.layers.Dense(
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
... | 9,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
head_mask: tf.Tensor,
... | 9,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = ... | 9,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
return outputs
def build(self, input_shape=None):
... | 9,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAESelfOutput(keras.layers.Layer):
"""
The residual connection is defined in TFViTMAELayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
... | 9,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size]) | 9,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEAttention(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFViTMAESelfAttention(config, name="attention")
self.dense_output = TFViTMAESelfOutput(config, name="output")
def prune_heads(self, heads):
... | 9,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attention", None) is not None:
with tf.name_scope(self.self_attention.name):
self.self_attention.build(None)
if getattr(self, "dense_output", None) is... | 9,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEIntermediate(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
i... | 9,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "dense", None) is not None:
with tf.name_scope(self.dense.name):
self.dense.build([None, None, self.config.hidden_size]) | 9,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAEOutput(keras.layers.Layer):
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout =... | 9,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
class TFViTMAELayer(keras.layers.Layer):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: ViTMAEConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFViTMAEAttention(config, name="attention")
self.intermediate = TFViTMAEInterme... | 9,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py |
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