text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def normalize(self, x: str) -> str:
"""Cover moses empty string edge case. They return empty list for '' input!"""
return self.punc_normalizer(x) if x else ""
def _convert_token_to_id(self, token):
return self.current_encoder.get(token, self.current_encoder[self.unk_token])
def remove_... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
def batch_decode(self, sequences, **kwargs):
"""
Convert a list of lists of token ids into a list of strings by calling decode. | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special to... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
Returns:
`List[str]`: The list of decoded sentences.
"""
return super().batch_decode(sequences, **kwargs)
def decode(self, token_ids, **kwargs):
"""
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens a... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the ... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
Returns:
`str`: The decoded sentence.
"""
return super().decode(token_ids, **kwargs)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
sp_model = self.spm_source if self._... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
if token_ids_1 is None:
return token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic fo... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
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")
return
saved_files = [] | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
if self.separate_vocabs:
out_src_vocab_file = os.path.join(
save_directory,
(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],
)
out_tgt_vocab_file = os.path.join(
save_directory,
(filename_... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
for spm_save_filename, spm_orig_path, spm_model in zip(
[VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]],
self.spm_files,
[self.spm_source, self.spm_target],
):
spm_save_path = os.path.join(
save_directory, (filename_prefix + "-" ... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
def get_src_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def get_tgt_vocab(self):
return dict(self.target_encoder, **self.added_tokens_decoder)
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state.update(
{k: None for k in ["spm_... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
def _special_token_mask(self, seq):
all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def get_special_tokens_mask(
... | 3,752 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/tokenization_marian.py |
class TFMarianSinusoidalPositionalEmbedding(keras.layers.Layer):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, **kwargs):
super().__init__(**kwargs)
if embedding_dim % 2 != 0:
raise NotImplement... | 3,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
self.weight = self.add_weight(
name="embeddings",
shape=[self.num_positions, self.embedding_dim],
)
weight = tf.cast(weight, dtype=self.weight.dtype)
self.weight.assign(weight)
super().build(input_shape)
@staticmethod
def _init_weight(n_pos: int, dim: i... | 3,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def call(
self, input_shape: tf.TensorShape, past_key_values_length: int = 0, position_ids: tf.Tensor | None = None
):
"""Input is expected to be of size [bsz x seqlen]."""
if position_ids is None:
seq_len = input_shape[1]
position_ids = tf.range(past_key_values_lengt... | 3,753 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
self.k_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
self.q_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
self.v_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
self.out_proj = keras.layers.Dense(embed_dim, use_bias=bias, name="out_pro... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = shape_list(hidden_states) | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
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_states (first "if" case)
# if uni-directional self-attention (decoder... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
src_len = shape_list(key_states)[1]
attn_weights = tf.matmul(query_states, key_states, transpose_b=True)
tf.debugging.assert_equal(
shape_list(attn_weights),
[bsz * self.num_heads, tgt_len, src_len],
message=(
f"Attention weights should be of size {(b... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
attention_mask = tf.cast(attention_mask, dtype=attn_weights.dtype)
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
attn_weights = stable_softmax(attn_weigh... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
attn_probs = self.dropout(attn_weights, training=training)
attn_output = tf.matmul(attn_probs, value_states)
tf.debugging.assert_equal(
shape_list(attn_output),
[bsz * self.num_heads, tgt_len, self.head_dim],
message=(
f"`attn_output` should be of siz... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "k_proj", None) is not None:
with tf.name_scope(self.k_proj.name):
self.k_proj.build([None, None, self.embed_dim])
if getattr(self, "q_proj", None) is not N... | 3,754 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianEncoderLayer(keras.layers.Layer):
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMarianAttention(
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, nam... | 3,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: np.ndarray | tf.Tensor | None,
layer_head_mask: tf.Tensor | None,
training: Optional[bool] = False,
) -> tf.Tensor:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch... | 3,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
tf.debugging.assert_equal(
shape_list(hidden_states),
shape_list(residual),
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states ... | 3,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 3,755 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianDecoderLayer(keras.layers.Layer):
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.d_model
self.self_attn = TFMarianAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
self.self_attn_layer_norm = keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
self.encoder_attn = TFMarianAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
name="encoder_attn",
is_deco... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: np.ndarray | tf.Tensor | None = None,
encoder_hidden_states: np.ndarray | tf.Tensor | None = None,
encoder_attention_mask: np.ndarray | tf.Tensor | None = None,
layer_head_mask: tf.Tensor | None = None,
cro... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size
`(decoder_attention_heads,)... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.activation_dropout(hidden_states, training=training)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "self_attn", None) is not None:
with tf.name_scope(self.self_attn.name):
self.self_attn.build(None)
if getattr(self, "self_attn_layer_norm", None) is not No... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if getattr(self, "fc2", None) is not None:
with tf.name_scope(self.fc2.name):
self.fc2.build([None, None, self.config.decoder_ffn_dim])
if getattr(self, "final_layer_norm", None) is not None:
with tf.name_scope(self.final_layer_norm.name):
self.final_layer... | 3,756 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianPreTrainedModel(TFPreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model" | 3,757 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianEncoder(keras.layers.Layer):
config_class = MarianConfig
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TFMarianEncoderLayer`].
Args:
config: MarianConfig
"""
def __init__(self, config: MarianConfig, embed_tokens... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
self.embed_tokens = embed_tokens
self.embed_positions = TFMarianSinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
name="embed_positions",
)
self.layers = [TFMarianEncoderLayer(config, name=f"layers.{i}") for i in range(config.encod... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
@unpack_inputs
def call(
self,
input_ids: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
head_mask: tf.Tensor | None = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[boo... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into assoc... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
for more detail. This argument can be used only in eager mode, in graph mode the value in the config
will be used instead.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used
i... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
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")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(in... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# check attention mask and invert
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
else:
attention_mask = None
encoder_states = () if output_hidden_states else None
all... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# encoder layers
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def build(self, input_shape=None):
if se... | 3,758 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianDecoder(keras.layers.Layer):
config_class = MarianConfig
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TFMarianDecoderLayer`]
Args:
config: MarianConfig
embed_tokens: output embedding
"""
def __init__(self, config: MarianC... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
self.dropout = keras.layers.Dropout(config.dropout)
def get_embed_tokens(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
@unpack_inputs
def call(
self,
input_ids: tf.Tensor | None = None,
inputs_embeds: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
position_ids: tf.Tensor | None = None,
encoder_hidden_states: tf.Tensor | None = None,
encoder_attention_mask: t... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
[What are attention masks?](../glossary#attention-mask)
position_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embed... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
[What are attention masks?](../glossary#attention-mask)
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.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)`.
input... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
in the config will be used instead.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail. This argument can be used only in eager mode, in graph mode the value in the co... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
hidden_states = inputs_embeds
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
if input_shape[-1] > 1:
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
else:
combined_attention_mask = _expand_mask(
... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None
present_key_values = () if use_cache else None
# check if head... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
dropout_probability = random.uniform(0, 1)
if training and (dropout_pro... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer(
hidden_states,
attention_mask=combined_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
layer_he... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if not return_dict:
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns
else:
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_values,
... | 3,759 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianMainLayer(keras.layers.Layer):
config_class = MarianConfig
def __init__(self, config: MarianConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.shared = keras.layers.Embedding(
input_dim=config.vocab_size,
output_dim=config.d_mo... | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
@unpack_inputs
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask: tf.Tensor | None = None,
decoder_input_ids: tf.Tensor | None = None,
decoder_attention_mask: tf.Tensor | None = None,
decoder_position_ids: tf.Tensor | None = None,
head_mask: t... | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
use_cache = False | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
) | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden... | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
elif not return_dict and not isinstance(encoder_outputs, tuple):
encoder_outputs = encoder_outputs.to_tuple() | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
decoder_outputs = self.decoder(
decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
c... | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
return TFSeq2SeqModelOutput(
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_outp... | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
# The shared/tied weights expect to be in the model base namespace
# Adding "/" to the end (not the start!) of a tf.name_scope puts it in the root namespace rather than
# the current one.
... | 3,760 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianModel(TFMarianPreTrainedModel):
def __init__(self, config: MarianConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model = TFMarianMainLayer(config, name="model")
def get_encoder(self):
return self.model.encoder
def get_decoder(self):
... | 3,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TFSeq2SeqModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
inpu... | 3,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
training: bool = False,
**kwargs,
) -> Tuple[tf.Tensor] | TFSeq2SeqModelOutput:
outputs = self.model(
input_ids=input_ids,
attention_mask=... | 3,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
training=training,
) | 3,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
return outputs
# Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidd... | 3,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
... | 3,761 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class BiasLayer(keras.layers.Layer):
"""
Bias as a layer. It is used for serialization purposes: `keras.Model.save_weights` stores on a per-layer basis,
so all weights have to be registered in a layer.
"""
def __init__(self, shape, initializer, trainable, name, **kwargs):
super().__init__(n... | 3,762 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class TFMarianMTModel(TFMarianPreTrainedModel, TFCausalLanguageModelingLoss):
_keys_to_ignore_on_load_unexpected = [
r"model.encoder.embed_tokens.weight",
r"model.decoder.embed_tokens.weight",
]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwarg... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def get_bias(self):
return {"final_logits_bias": self.bias_layer.bias}
def set_bias(self, value):
# Replaces the existing layers containing bias for correct (de)serialization.
vocab_size = value["final_logits_bias"].shape[-1]
self.bias_layer = BiasLayer(
name="final_logi... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@add_end_docstrings(MARIAN_GENERATION_EXAMPLE)
def call(
self,
input_ids: tf.Tensor | None = None,
attention_mask... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
labels: tf.Tensor | None = None,
training: bool = False,
) -> Tuple[tf.Tensor] | TFSeq2SeqLMOutput:
r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels fo... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
Returns:
"""
if labels is not None:
labels = tf.where(
labels == self.config.pad_token_id,
tf.fill(shape_list(labels), tf.cast(-100, labels.dtype)),
labels,
)
use_cache = False
if decoder_input_ids is None ... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
head_mask=he... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return TFSeq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values, # index 1 o... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
# Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidde... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_st... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past_key_values
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no pa... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
return {
"input_ids": None, # encoder_outputs is defined. input_ids not needed
"encoder_outputs": encoder_outputs,
"past_key_values": past_key_values,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mas... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None)
if getattr(self, "bias_layer", None) is not None:
with t... | 3,763 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/marian/modeling_tf_marian.py |
class ViltImageProcessor(BaseImageProcessor):
r"""
Constructs a ViLT image processor. | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge":... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
overridden by the `resample` parameter in the `preprocess` method.
do_rescale (`bool`, *optional*, def... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
method. Can be overridden by the `do_normalize` parameter in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in t... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
Whether to pad the image to the `(max_height, max_width)` of the images in the batch. Can be overridden by
the `do_pad` parameter in the `preprocess` method.
""" | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
size_divisor: int = 32,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 2... | 3,764 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vilt/image_processing_vilt.py |
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