text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
... | 9,576 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py |
class TFLayoutLMEmbeddings(keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.max_pos... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.config.type_vocab_size, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_scope(... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
with tf.name_scope("y_position_embeddings"):
self.y_position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_2d_position_embeddings, self.hidden_size],
initializer=get_initializer(self.initializer_range),
)
with tf.name_sc... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
if self.built:
return
self.built = True
if getattr(self, "LayerNorm", None) is not None:
with tf.name_scope(self.LayerNorm.name):
self.LayerNorm.build([None, None, self.config.hidden_size])
def call(
self,
input_ids: tf.Tensor = None,
... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=inp... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
if bbox is None:
bbox = bbox = tf.fill(input_shape + [4], value=0)
try:
left_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 0])
upper_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 1])
right_position_embeddings = tf.... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
final_embeddings = (
inputs_embeds
+ position_embeds
+ token_type_embeds
+ left_posi... | 9,577 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMSelfAttention(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **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 th... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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"
)
... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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,
attention_mask: tf.Tensor,
... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(sel... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_s... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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 = ... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "query", None) is not None:
with tf.name_scope(self.query.name):
self.query.build([None, None, self.config.hidden_size])
if getattr(self, "key", None) is no... | 9,578 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMSelfOutput(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.L... | 9,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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])
if getattr(self, "LayerNorm", None)... | 9,579 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMAttention(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.self_attention = TFLayoutLMSelfAttention(config, name="self")
self.dense_output = TFLayoutLMSelfOutput(config, name="output")
def prune_heads(self, heads... | 9,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def call(
self,
input_tensor: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) ... | 9,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
outputs = (attention_output,) + self_outputs[1:] | 9,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
return outputs
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, "den... | 9,580 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMIntermediate(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
... | 9,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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]) | 9,581 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMOutput(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.Layer... | 9,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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.intermediate_size])
if getattr(self, "LayerNorm",... | 9,582 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMLayer(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.attention = TFLayoutLMAttention(config, name="attention")
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
... | 9,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_value: Tuple[tf.Tensor] | None,
output_attentions: bool,
training... | 9,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
... | 9,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
input_tensor=attention_output,
attention_mask=at... | 9,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
intermediate_output = self.intermediate(hidden_states=attention_output)
layer_output... | 9,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "attention", None) is not None:
with tf.name_scope(self.attention.name):
self.attention.build(None)
if getattr(self, "intermediate", None) is not None:
... | 9,583 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMEncoder(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] | 9,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor | None,
encoder_attention_mask: tf.Tensor | None,
past_key_values: Tuple[Tuple[tf.Tensor]] | None,
use_cache: Optional[bool],
... | 9,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
past_key_value = past_key_values[i] if past_key_values is not None else None
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask[i],
encoder_hidden_states=encoder_hidden_states,
... | 9,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None
)
return... | 9,584 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMPooler(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh"... | 9,585 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMPredictionHeadTransform(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
... | 9,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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])
if getattr(self, "LayerNorm", None)... | 9,586 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMLMPredictionHead(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.transform = TFLayoutLMPredictionHeadTransform... | 9,587 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def set_output_embeddings(self, value: tf.Variable):
self.input_embeddings.weight = value
self.input_embeddings.vocab_size = shape_list(value)[0]
def get_bias(self) -> Dict[str, tf.Variable]:
return {"bias": self.bias}
def set_bias(self, value: tf.Variable):
self.bias = value["... | 9,587 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMMLMHead(keras.layers.Layer):
def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs):
super().__init__(**kwargs)
self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions")
def call(self, sequence_output: tf.Ten... | 9,588 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMMainLayer(keras.layers.Layer):
config_class = LayoutLMConfig
def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs):
super().__init__(**kwargs)
self.config = config
self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings")
... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
@unpack_inputs
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | tf.Tensor | None = None,
attention_mask: np.ndarray | tf.Tensor | None = None,
token_type_ids: np.ndarray | tf.Tensor | None = None,
position_ids: np.ndarray | tf.Tensor | Non... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds") | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if bbox is None:
bbox = tf.fill(dims=input_shape + [4], value=0)
embedding_output = self.embeddings(... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT,... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# eff... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.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... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
# Need to pass these required positional arguments to `Encoder`
encoder_hidden_states=encoder_hidden_states,
encoder_attent... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
return TFBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
... | 9,589 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LayoutLMConfig
base_model_prefix = "layoutlm"
@property
def input_signature(self):
... | 9,590 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm") | 9,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(
output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC
)
def call(
self,
input_ids: TFModelInputTyp... | 9,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
r"""
Returns: | 9,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
Examples:
```python
>>> from transformers import AutoTokenizer, TFLayoutLMModel
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
>... | 9,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> outputs = model(
... ... | 9,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None) | 9,591 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"cls.seq_relationship",
r"cls.predicti... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def get_prefix_bias_name(self) -> str:
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.ndarray | ... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
labels (`tf.Tensor` or `np.ndarray` 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
... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layo... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> labels = tokenizer("Hello world... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> loss = outputs.loss
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_e... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
return TFMaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self,... | 9,592 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_rel... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
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
`confi... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForSequenceClassification.fro... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> sequence_label = tf.convert_to_t... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, ... | 9,593 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
... | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox: np.nda... | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
>>> model = TFLayoutLMForTokenClassification.from_pret... | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> encoding = tokenizer(" ".join(words), return_tensors="tf")
>>> input_ids = encoding["input_ids"]
>>> attention_mask = encoding["attention_mask"]
>>> token_type_ids = encoding["token_type_ids"]
>>> bbox = tf.convert_to_tensor([token_boxes])
>>> token_labels = tf.convert_to_ten... | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask... | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "la... | 9,594 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [
r"pooler",
r"mlm___cls",
r"nsp___cls",
... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: TFModelInputType | None = None,
bbox:... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` or `np.ndarray` 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 ... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True)... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
>>> encoding = tokenizer(
... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf"
... )
>>> bbox = []
>>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
... if s == 1:
... ... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
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_att... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not return_dict:
output = (start_log... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "layoutlm", None) is not None:
with tf.name_scope(self.layoutlm.name):
self.layoutlm.build(None)
if getattr(self, "qa_outputs", None) is not None:
... | 9,595 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py |
class LayoutLMTokenizer(PreTrainedTokenizer):
r"""
Construct a LayoutLM tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods. | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
do_basic_tokenize (`bool`, *optional*, defaults to `True`):
Whether or not to do basic toke... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
pad_token (`str`, *optional*, defaults to `"[PAD]"`):
The token used for padding, for example when batching sequences of different lengt... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
Whether or not to tokenize Chinese characters. | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
This should likely be deactivated for Japanese (see this
[issue](https://github.com/huggingface/transformers/issues/328)).
strip_accents (`bool`, *optional*):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lo... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
stri... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
do_lower_case=do_lower_case,
never_split=never_split,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
) | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token))
super().__init__(
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def _tokenize(self, text, split_special_tokens=False):
split_tokens = []
if self.do_basic_tokenize:
for token in self.basic_tokenizer.tokenize(
text, never_split=self.all_special_tokens if not split_special_tokens else None
):
# If the token is par... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
Returns:
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
"""
if token_ids_1 is None:
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
cls = [self.cls_token_id]
sep = [self.sep_token_id]
return cls ... | 9,596 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py |
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