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class UdopCellEmbeddings(nn.Module):
def __init__(self, max_2d_position_embeddings=501, hidden_size=1024):
super(UdopCellEmbeddings, self).__init__()
self.max_2d_position_embeddings = max_2d_position_embeddings
self.x_position_embeddings = nn.Embedding(max_2d_position_embeddings, hidden_siz... | 3,061 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
embeddings = (
left_position_embeddings
+ upper_position_embeddings
+ right_position_embeddings
+ lower_position_embeddings
)
return embeddings | 3,061 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class RelativePositionBiasBase(nn.Module, ABC):
"""
Base class of relative biases. | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Args:
num_heads (`int`):
Number of attention heads in the model, it will create embeddings of size `num_heads`, which will be added to the scores of each token pair.
relative_attention_num_buckets (`int`, *optional*, defaults to 32):
Pair token metric (distance in the sequence, d... | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Whether to multiply relative distances by a random scalar.
expand (`bool`, *optional*, defaults to `False`):
Whether to expand an existing pretrained model with subsequent additions of prefix_bucket.
""" | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def __init__(
self,
num_heads=None,
relative_attention_num_buckets=32,
bidirectional=True,
scaling_factor=1,
max_distance=128,
level="tokens",
augmentation=False,
prefix_bucket=False,
expand=False,
):
super(RelativePositionBiasB... | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
@abstractmethod
def prepare_input(
self,
attention_mask: Optional[Tensor] = None,
bbox: Optional[Dict[str, Any]] = None,
) -> Tensor:
pass
def get_bucket(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
relative_positio... | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
return relative_position.to(torch.long)
def forward(self, attention_mask: Optional[Tensor] = None, bbox: Optional[Dict[str, Any]] = None) -> Tensor:
# re-using pretrained model with subsequent addition of prefix_bucket
if self.expand and self.prefix_bucket:
new_bias = nn.Embedding(self.... | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if self.prefix_bucket:
if rp_bucket.size(0) == 1 and attention_mask.size(0) > 1:
rp_bucket = rp_bucket.repeat(attention_mask.size(0), 1, 1)
# based on assumption that prefix bboxes are negative
is_prefix = bbox[:, :, 1] < 0
num_prefix = is_prefix.sum(-1)
... | 3,062 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class RelativePositionBias1D(RelativePositionBiasBase):
def __init__(self, scaling_factor=1, max_distance=128, **kwargs):
"""
Reimplementation of T5 relative position bias. Distance between given tokens is their distance in the sequence.
Parameters are the same as in base class
"""
... | 3,063 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class RelativePositionBiasHorizontal(RelativePositionBiasBase):
def __init__(self, scaling_factor=100, max_distance=100, **kwargs):
"""
Represents in the bucket embeddings horizontal distance between two tokens. Parameters are the same as in base
class
"""
super().__init__(sc... | 3,064 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class RelativePositionBiasVertical(RelativePositionBiasBase):
def __init__(self, scaling_factor=100, max_distance=100, **kwargs):
"""
Represents in the bucket embeddings vertical distance between two tokens. Parameters are the same as in base
class
"""
super().__init__(scalin... | 3,065 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class RelativePositionBiasAggregated(nn.Module):
def __init__(self, modules: Sequence[RelativePositionBiasBase]):
"""
Class which sums up various computed biases.
Args:
modules (Sequence[RelativePositionBiasBase]):
List of relative bias modules.
"""
... | 3,066 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopStack(UdopPreTrainedModel):
"""
This class is based on `T5Stack`, but modified to take into account the image modality as well as 2D position
embeddings.
"""
def __init__(self, config, embed_tokens=None, embed_patches=None):
super().__init__(config)
self.embed_tokens = em... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# get weights from encoder position bias
self.relative_bias = self._get_relative_bias(config)
def _tie_weights(self):
for bias in self.relative_bias.biases:
if isinstance(bias, RelativePositionBias1D):
self._tie_or_clone_weights(
bias.relative_attenti... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def forward(
self,
input_ids=None,
attention_mask=None,
bbox=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
pixel_values=None,
visual_bbox=None,
image_embeddings=None,
position_bias=None,
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# input embeddings processing | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if input_ids is not None and inputs_embeds is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None a... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# encoder_attention_mask = attention_mask
logger.warning("Empty batch")
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if inputs_embeds is None:
if self.embed_tokens is None:
raise ValueError("You have to intialize the model with valid token embeddings")
inputs_embeds = self.embed_tokens(input_ids)
if pixel_values is not None:
image_embeddings = self.embed_patches(pixel_value... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if not self.is_decoder and bbox is not None:
inputs_embeds += self.cell_2d_embedding(bbox)
batch_size, seq_length = input_shape
if use_cache is True:
assert self.is_decoder, "`use_cache` can only be set to `True` if {} is used as a decoder".format(self) | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# initialize past_key_values
return_legacy_cache = False
return_self_attention_cache = False
if self.is_decoder and (use_cache or past_key_values is not None):
if isinstance(past_key_values, Cache) and not isinstance(past_key_values, EncoderDecoderCache):
return_self_... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
elif past_key_values is None:
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
elif not self.is_decoder:
# do not pass cache object down the line for encoder stack
# it messes indexing later in decoder-stack because cache object is modified in-place
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
)
if attention_mask ... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if self.config.is_decoder:
causal_mask = self._update_causal_mask(
attention_mask,
inputs_embeds,
cache_position,
past_key_values.self_attention_cache if past_key_values is not None else None,
output_attentions,
)
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.num_layers)
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
if ... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
layer_outputs = layer_module(
hidden_states,
attention_mask=causal_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_posit... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention weights),
# (self-attention position bias), (cross-attention weights), (cross-attention position bias)
position_bias = layer_outputs[2]
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
next_cache = next_decoder_cache if use_cache else None
if return_self_attention_cache:
next_cache = past_key_values.self_attention_cache
if return_legacy_cache:
next_cache = past_key_values.to_legacy_cache()
if not return_dict:
return tuple(
v... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
return causal_mask | 3,067 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopModel(UdopPreTrainedModel):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
"decoder... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
decoder_config = deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = UdopStack(decoder_config, self.shared)
# Initialize weights and apply final processing
self.p... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
@add_start_docstrings_to_model_forward(UDOP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Tensor = None,
attention_mask: Tensor = None,
bbox: Dict[str, Any] = None,
pixel_values: Op... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[Tensor, ...]:
r"""
Returns: | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Example:
```python
>>> from transformers import AutoProcessor, AutoModel
>>> from datasets import load_dataset
>>> import torch
>>> # load model and processor
>>> # in this case, we already have performed OCR ourselves
>>> # so we initialize the processor with `... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
>>> decoder_input_ids = torch.tensor([[model.config.decoder_start_token_id]])
>>> # forward pass
>>> outputs = model(**inputs, decoder_input_ids=decoder_input_ids)
>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1, 1024]
```"""
... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
bbox=bbox,
pixel_values=pixel_values,
visual_bbox=visua... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=e... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_output... | 3,068 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopForConditionalGeneration(UdopPreTrainedModel, GenerationMixin):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_atte... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
decoder_config = deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = UdopStack(decoder_config, self.shared)
# The weights of the language modeling head are shared with th... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def get_decoder(self):
return self.decoder | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
@add_start_docstrings_to_model_forward(UDOP_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Tensor = None,
attention_mask: Tensor = None,
bbox: Dict[str, Any] = None,
pixel_values: Optio... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
labels: Optional[Tensor] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[Tensor, ...]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_s... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, UdopForConditionalGeneration
>>> from datasets import load_dataset
>>> # load model and processor
>>> # in this case, we already have performed OCR ourselves
>>> # so we initialize the pro... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
>>> # one can use the various task prefixes (prompts) used during pre-training
>>> # e.g. the task prefix for DocVQA is "Question answering. "
>>> question = "Question answering. What is the date on the form?"
>>> encoding = processor(image, question, text_pair=words, boxes=boxes, return_tensors... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
bbox=bbox,
visual_bbox=visual_bbox,
pixel_values=pixel_values,
attention_mask=attentio... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=e... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.config.d_model**-0.5)
... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
return Seq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attenti... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
reordered_decoder_past = ()
for layer_past_states in past_key_values:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need ... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if reordered_layer_past_states[0].shape != layer_past_states[0].shape:
raise ValueError(
f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched"
)
if len(reordere... | 3,069 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopEncoderModel(UdopPreTrainedModel):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
]
def __init__(self, config: UdopConfig... | 3,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.set_input_embeddings(new_embeddings)
def get_encoder(self):
return self.encoder
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {laye... | 3,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
@add_start_docstrings_to_model_forward(UDOP_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutputWithAttentionMask, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Tensor = None,
bbox: Dict[str, Any] = None,
attention_mask: Tensor = None,
... | 3,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
>>> # load model and processor
>>> # in this case, we already have performed OCR ourselves
>>> # so we initialize the processor with `apply_ocr=False`
>>> processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
>>> model = UdopEncoderModel.from_pretrained("micr... | 3,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
>>> outputs = model(**encoding)
>>> last_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not Non... | 3,070 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopTokenizer(PreTrainedTokenizer):
"""
Adapted from [`LayoutXLMTokenizer`] and [`T5Tokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this supercla... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.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... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
The bounding box to use for the special [SEP] token.
pad_to... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
to set:
- `enable_sampling`: Enable... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.
legacy (`bool`, *optional*, defaults to `True`):
Whether or not the `legacy` behaviour of the tokenizer should be used. Legacy is before the merge of #24622
whi... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base", legacy=False)
>>> tokenizer.encode("Hello <extra_id_0>.") # the extra space `[3]` is no longer here
[8774, 32099, 5, 1]
```
Checkout the pull request and the issue [here](https://github.com/huggingface/transformers/p... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def __init__(
self,
vocab_file,
eos_token="</s>",
unk_token="<unk>",
sep_token="</s>",
pad_token="<pad>",
sep_token_box=[1000, 1000, 1000, 1000],
pad_token_box=[0, 0, 0, 0],
pad_token_label=-100,
only_label_first_subword=True,
addit... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
self.legacy = legacy
self.add_prefix_space = add_prefix_space
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
# addi... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
super().__init__(
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
sep_token_box=sep_token_box,
pad_token_box=pad_token_box,
pad_token_label=pad_token_label,
only_label_first_subword=only_l... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.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 lis... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
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=Tru... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_sentinel_token_ids
def get_sentinel_token_ids(self):
return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()]
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present
def _... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences
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... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for s... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
if token_ids_1 is None:
return token_ids_0
else:
token_ids_1 = self._add_eos_if_not_presen... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.tokenize
def tokenize(self, text: "TextInput", **kwargs) -> List[str]:
"""
Converts a string to a list of tokens. If `self.legacy` is set to `False`, a prefix token is added unless the
first token is special.
"""
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
`['H', 'e', 'y']` instead of `['▁He', 'y']`. Thus we always encode `f"{unk_token}text"` and stri... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.sp_model.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
current_sub_tokens = []
out_string = ""
prev_is_special = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " ... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.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")
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
@add_end_docstrings(UDOP_ENCODE_KWARGS_DOCSTRING)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[Lis... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# input mode in this case.
if not self._in_target_context_manager:
self._switch_to_input_mode()
encodings = self.call_boxes(text=text, text_pair=text_pair, boxes=boxes, word_labels=word_labels, **kwargs)
if text_target is not None:
self._switch_to_target_mode(... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if text_target is None:
return encodings
elif text is None:
return target_encodings
else:
encodings["labels"] = target_encodings["input_ids"]
return encodings | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def call_boxes(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels: Optional[Union[List[... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with word-level normalized bounding boxes and optional labels. | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
words)... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
# Input type checking for clearer error
def _is_valid_text_input(t):
if isinstance(t, str):
# Strings are fine
return True
elif isinstance(t, (list, tuple)):
# List are fine as long as they are...
if len(t) == 0:
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if text_pair is not None:
# in case text + text_pair are provided, text = questions, text_pair = words
if not _is_valid_text_input(text):
raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
if not isinstance(text_pai... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if text_pair is not None:
is_batched = isinstance(text, (list, tuple))
else:
is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
words = text if text_pair is None else text_pair
if boxes is None:
raise ValueError("You ... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
if is_batched:
if text_pair is not None and len(text) != len(text_pair):
raise ValueError(
f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
f" {len(text_pair)}."
)
batch_text_or_text_pairs =... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
def batch_encode_plus_boxes(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
boxes: Optional[List[List[List[int]]]] = None,
word_labels: Optional[List[List[i... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences. | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
Args:
batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[List[int], List[int]]]`):
Batch of sequences or pair of sequences to be encoded. This can be a list o... | 3,071 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py |
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