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class QDQBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = QDQBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Li... | 10,146 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = QDQBertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | 10,147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertOnlyNSPHead(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 | 10,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = QDQBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predic... | 10,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = QDQBertConfig
load_tf_weights = load_tf_weights_in_qdqbert
base_model_prefix = "bert"
supports_g... | 10,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.... | 10,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertModel(QDQBertPreTrainedModel):
"""
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/... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
self.embeddings = QDQBertEmbeddings(config)
self.encoder = QDQBertEncoder(config)
self.pooler = QDQBertPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encod... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed ... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embeddi... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
... | 10,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertLMHeadModel(QDQBertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.weight", "predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `QDQBertLMHeadModel` as a standalone, ... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: ... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = F... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
def prepare_inputs_for_generation(
self,
input_ids: Optional[torch.LongTensor],
past_key_values=None,
attention_mask: Optional[torch.Tensor] = None,
**model_kwargs,
):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the ... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values}
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx... | 10,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertForMaskedLM(QDQBertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.weight", "predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `QDQBertForMaskedLM` make... | 10,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch... | 10,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
l... | 10,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embe... | 10,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
... | 10,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=... | 10,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertForNextSentencePrediction(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = QDQBertModel(config)
self.cls = QDQBertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init() | 10,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Option... | 10,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`: | 10,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, QDQBertForNextSentencePrediction
>>> import torch
>>> tokenizer = AutoTokenizer... | 10,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if "next_sentence_label" in kwargs:
warnings.warn(
"The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
" `labels` instead.",
FutureWarning,
)
labels = kwargs.pop("next_sentence_label")
... | 10,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
next_sentence_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1))
if not return_dict:
output = (seq_relationship_scores,) + outputs[2:]
return ((next_sentence... | 10,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertForSequenceClassification(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = QDQBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.c... | 10,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opti... | 10,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 10,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 10,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertForMultipleChoice(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = QDQBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and ap... | 10,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_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(
self,
i... | 10,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
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 dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 10,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.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
... | 10,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits... | 10,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertForTokenClassification(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifi... | 10,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optiona... | 10,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 10,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 10,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class QDQBertForQuestionAnswering(QDQBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = QDQBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
#... | 10,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
@add_start_docstrings_to_model_forward(QDQBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: ... | 10,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
... | 10,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_st... | 10,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 10,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 10,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/qdqbert/modeling_qdqbert.py |
class NatConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NatModel`]. It is used to instantiate a Nat model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yie... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
Args:
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 64):
... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 0.0):
The initial value for the layer scale. Disabled if <=0.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute. | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
Example:
```python
>>> from transformers import NatConfig, NatModel
>>> # Initializing a Nat shi-labs/nat-mini-in1k-224 style configuration
>>> configuration = NatConfig()
>>> # Initializing a model (with random weights) from the shi-labs/nat-mini-in1k-224 style configuration
>>> model = NatM... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
def __init__(
self,
patch_size=4,
num_channels=3,
embed_dim=64,
depths=[3, 4, 6, 5],
num_heads=[2, 4, 8, 16],
kernel_size=7,
mlp_ratio=3.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.kernel_size = kernel_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
... | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
) | 10,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/configuration_nat.py |
class NatEncoderOutput(ModelOutput):
"""
Nat encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | 10,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.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 stage) of shape `(batch... | 10,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, .... | 10,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatModelOutput(ModelOutput):
"""
Nat model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.... | 10,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.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 stage) of shape `(batch... | 10,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: Optional[torch.FloatTensor] = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = No... | 10,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatImageClassifierOutput(ModelOutput):
"""
Nat outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of sh... | 10,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.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 stage) of shape `(batch... | 10,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.
"""
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: O... | 10,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatEmbeddings(nn.Module):
"""
Construct the patch and position embeddings.
"""
def __init__(self, config):
super().__init__()
self.patch_embeddings = NatPatchEmbeddings(config)
self.norm = nn.LayerNorm(config.embed_dim)
self.dropout = nn.Dropout(config.hidden_dro... | 10,163 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | 10,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor:
_, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the co... | 10,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatDownsampler(nn.Module):
"""
Convolutional Downsampling Layer.
Args:
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization layer class.
"""
def __init__(self, dim: int, norm_layer: nn.M... | 10,165 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.... | 10,166 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NeighborhoodAttention(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
... | 10,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 3, 1, 2, 4)
def forward(
self,
hidden_states:... | 10,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
# Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases.
attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, 1)
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attentio... | 10,167 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NeighborhoodAttentionOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
... | 10,168 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NeighborhoodAttentionModule(nn.Module):
def __init__(self, config, dim, num_heads, kernel_size):
super().__init__()
self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size)
self.output = NeighborhoodAttentionOutput(config, dim)
self.pruned_heads = set()
def p... | 10,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.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)
def forward(
self,
... | 10,169 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_a... | 10,170 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.den... | 10,171 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatLayer(nn.Module):
def __init__(self, config, dim, num_heads, drop_path_rate=0.0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.kernel_size = config.kernel_size
self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps)
... | 10,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
def maybe_pad(self, hidden_states, height, width):
window_size = self.kernel_size
pad_values = (0, 0, 0, 0, 0, 0)
if height < window_size or width < window_size:
pad_l = pad_t = 0
pad_r = max(0, window_size - width)
pad_b = max(0, window_size - height)
... | 10,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
attention_outputs = self.attention(hidden_states, output_attentions=output_attentions)
attention_output = attention_outputs[0]
was_padded = pad_values[3] > 0 or pad_values[5] > 0
if was_padded:
attention_output = attention_output[:, :height, :width, :].contiguous()
if self... | 10,172 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatStage(nn.Module):
def __init__(self, config, dim, depth, num_heads, drop_path_rate, downsample):
super().__init__()
self.config = config
self.dim = dim
self.layers = nn.ModuleList(
[
NatLayer(
config=config,
... | 10,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor]:
_, height, width, _ = hidden_states.size()
for i, layer_module in enumerate(self.layers):
layer_outputs = layer_module(hidden_states, output_attent... | 10,173 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.num_levels = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.levels = nn.ModuleList(
[
... | 10,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
def forward(
self,
hidden_states: torch.Tensor,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
output_hidden_states_before_downsampling: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, NatEncoderO... | 10,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
hidden_states = layer_outputs[0]
hidden_states_before_downsampling = layer_outputs[1]
if output_hidden_states and output_hidden_states_before_downsampling:
# rearrange b h w c -> b c h w
reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2... | 10,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return NatEncoderOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
reshaped_h... | 10,174 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
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