text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
codebook_size = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
codebook_size = get_size_dict(codebook_size, param_name="codebook_size")
codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}
codebook_crop_size =... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
self.return_image_mask = return_image_mask
self.input_size_patches = input_size_patches
self.total_mask_patches = total_mask_patches
self.mask_group_min_patches = mask_group_min_patches
self.mask_group_max_patches = mask_group_max_patches
self.mask_group_min_aspect_ratio = mask_g... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
self.return_codebook_pixels = return_codebook_pixels
self.codebook_do_resize = codebook_do_resize
self.codebook_size = codebook_size
self.codebook_resample = codebook_resample
self.codebook_do_center_crop = codebook_do_center_crop
self.codebook_crop_size = codebook_crop_size
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
created using from_dict and kwargs e.g. `FlavaImageProcessor.from_pretrained(checkpoint, codeboo... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
@lru_cache()
def masking_generator(
self,
input_size_patches,
total_mask_patches,
mask_group_min_patches,
mask_group_max_patches,
mask_group_min_aspect_ratio,
mask_group_max_aspect_ratio,
) -> FlavaMaskingGenerator:
return FlavaMaskingGenerator(
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
# Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize with PILImageResampling.BILINEAR->PILImageResampling.BICUBIC
def resize(
self,
image: np.ndarray,
size: Dict[str, int],
resample: PILImageResampling = PILImageResampling.BICUBIC,
data_format: ... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels,... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Returns:
`np.ndarray`: The resized image.
"""
size = get_size_dict(size)
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
output_size = (size["height"], size["w... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
def _preprocess_image(
self,
image: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size: Dict[str, int] = None,
do_rescale: bool = None,
rescale_factor: float ... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_center_crop=do_center_crop,
crop_size=crop_size,
do_resize=do_res... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
if do_resize:
image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
if do_center_crop:
image = self.center_crop(image=image, size=crop_size, input_data_format=input_data_format)
if do_rescale:
image = self.rescale(image=... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: Optional[bool] = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[Dict[str, int]] = None,
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
# Codebook related params
return_codebook_pixels: Optional[bool] = None,
codebook_do_resize: Optional[bool] = None,
codebook_size: Optional[Dict[str, int]] = None,
codebook_resample: Optional[int] = None,
codebook_do_center_crop: Optional[bool] = None,
codebook_crop_size:... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resiz... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image values between [0 - 1].
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
input_size_patches (`int`, *optional*, defaults to `self.input_size_patches`):
Size of the patches to extract from the image.
total_mask_patches (`int`, *optional*, defaults to `self.total_mask_patches`):
Total number of patches to extract from the image.
mask_gro... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
Maximum aspect ratio of the patches to extract from the image.
return_codebook_pixels (`bool`, *optional*, defaults to `self.return_codebook_pixels`):
Whether to return the codebook pixels.
codebook_do_resize (`bool`, *optional*, defaults to `self.codebook_do_resize`):
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
codebook_crop_size (`Dict[str, int]`, *optional*, defaults to `self.codebook_crop_size`):
Size of the center crop of the codebook pixels. Only has an effect if `codebook_do_center_crop` is set
to `True`.
codebook_do_rescale (`bool`, *optional*, defaults to `self.codebook_do_r... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
codebook_image_mean (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_mean`):
Codebook pixels mean to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`.
codebook_image_std (`float` or `List[float]`, *optional*, defaults to `self.codebook_image... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `ChannelDimension.FIRST`: image in (... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size)
resample = resample if resample is not None else self.resample
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
return_image_mask = return_image_mask if return_image_mask is not None else self.return_image_mask
input_size_patches = input_size_patches if input_size_patches is not None else self.input_size_patches
total_mask_patches = total_mask_patches if total_mask_patches is not None else self.total_mask_patches... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
return_codebook_pixels = (
return_codebook_pixels if return_codebook_pixels is not None else self.return_codebook_pixels
)
codebook_do_resize = codebook_do_resize if codebook_do_resize is not None else self.codebook_do_resize
codebook_size = codebook_size if codebook_size is not None... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
codebook_crop_size = codebook_crop_size if codebook_crop_size is not None else self.codebook_crop_size
codebook_crop_size = get_size_dict(codebook_crop_size, param_name="codebook_crop_size")
codebook_do_map_pixels = (
codebook_do_map_pixels if codebook_do_map_pixels is not None else self.cod... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
images = make_list_of_images(images)
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
processed_images = [
self._preproce... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
if return_codebook_pixels:
codebook_images = [
self._preprocess_image(
image=img,
do_resize=codebook_do_resize,
size=codebook_size,
resample=codebook_resample,
do_center_crop=codebook_do_cente... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
if return_image_mask:
mask_generator = self.masking_generator(
input_size_patches=input_size_patches,
total_mask_patches=total_mask_patches,
mask_group_min_patches=mask_group_min_patches,
mask_group_max_patches=mask_group_max_patches,
... | 3,137 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/image_processing_flava.py |
class FlavaProcessor(ProcessorMixin):
r"""
Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.
[`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the
[`~FlavaProcessor.__call__`] and [`~... | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
feature_extractor = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",
... | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
def __call__(
self,
images: Optional[ImageInput] = None,
text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str,... | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
This method uses [`FlavaImageProcessor.__call__`] method to prepare image(s) for the model, and
[`BertTokenizerFast.__call__`] to prepare text for the model. | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
Please refer to the docstring of the above two methods for more information.
"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.") | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
if text is not None:
encoding = self.tokenizer(
text=text,
add_special_tokens=add_special_tokens,
padding=padding,
truncation=truncation,
max_length=max_length,
stride=stride,
pad_to_multiple_of=p... | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
return_codebook_pixels=return_codebook_pixels,
return_tensors=return_tensors,
**kwargs,
) | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
if text is not None and images is not None:
encoding.update(image_features)
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
def batch_decode(self, *args, **kwar... | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def feature_extractor_class(... | 3,138 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/flava/processing_flava.py |
class AlbertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`AlbertModel`] or a [`TFAlbertModel`]. It is used
to instantiate an ALBERT model according to the specified arguments, defining the model architecture. Instantiating
a configuration with the defau... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
Args:
vocab_size (`int`, *optional*, defaults to 30000):
Vocabulary size of the ALBERT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`].
embedding_size (`int`, *optional*, defaults t... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
The dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
inner_group_num (`int`, *optional*, defaults to 1):
The number of inner repetition of attention and ffn.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu_new"`):
T... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
(e.g., 512 or 1024 or 2048).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`AlbertModel`] or [`TFAlbertModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncate... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.1... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
Examples:
```python
>>> from transformers import AlbertConfig, AlbertModel
>>> # Initializing an ALBERT-xxlarge style configuration
>>> albert_xxlarge_configuration = AlbertConfig()
>>> # Initializing an ALBERT-base style configuration
>>> albert_base_configuration = AlbertConfig(
... ... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
def __init__(
self,
vocab_size=30000,
embedding_size=128,
hidden_size=4096,
num_hidden_layers=12,
num_hidden_groups=1,
num_attention_heads=64,
intermediate_size=16384,
inner_group_num=1,
hidden_act="gelu_new",
hidden_dropout_prob=0,... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
self.vocab_size = vocab_size
self.embedding_size = embedding_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_hidden_groups = num_hidden_groups
self.num_attention_heads = num_attention_heads
self.inner_group_num = inner_group_num
... | 3,139 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
class AlbertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 3,140 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/configuration_albert.py |
class AlbertEmbeddings(nn.Module):
"""
Construct the embeddings from word, position and token_type embeddings.
"""
def __init__(self, config: AlbertConfig):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
... | 3,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 3,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.Float... | 3,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(... | 3,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
... | 3,141 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertAttention(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple o... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.pruned_he... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention.transpose_for_scores
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# Update hyper params and store pruned heads
self.num_attention_heads = self.num_attention_heads - len(heads)
self.all_head_size = self.attention_head_size * self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.T... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention ... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
seq_length = hidden_states.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(seq_lengt... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.attention_dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = at... | 3,142 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertSdpaAttention(AlbertAttention):
def __init__(self, config):
super().__init__(config)
self.dropout_prob = config.attention_probs_dropout_prob
self.require_contiguous_qkv = not is_torch_greater_or_equal_than_2_2 | 3,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
if self.position_emb... | 3,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
return super().forward(hidden_states, attention_mask, head_mask, output_attentions) | 3,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
batch_size, seq_len, _ = hidden_states.size()
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
# SDPA with memory-efficient backend is brok... | 3,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
attention_output = torch.nn.functional.scaled_dot_product_attention(
query=query_layer,
key=key_layer,
value=value_layer,
attn_mask=attention_mask,
dropout_p=self.dropout_prob if self.training else 0.0,
is_causal=False,
)
attention... | 3,143 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertLayer(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.config = config
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_n... | 3,144 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor]:
atte... | 3,144 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
def ff_chunk(self, attention_output: torch.Tensor) -> torch.Tensor:
ffn_output = self.ffn(attention_output)
ffn_output = self.activation(ffn_output)
ffn_output = self.ffn_output(ffn_output)
return ffn_output | 3,144 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertLayerGroup(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torc... | 3,145 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if output_hidden_states:
layer_hidden_states = layer_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if output_hidden_states:
outputs = outputs + (layer_hidden_states,)
if output_attentions:
outputs = outputs + (layer_attentions,)
retu... | 3,145 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertTransformer(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.config = config
self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in r... | 3,146 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
for i in range(self.config.num_hidden_layers):
# Number of layers in a hidden group
layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)
# Index of the hidden group
group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidd... | 3,146 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
) | 3,146 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = AlbertConfig
load_tf_weights = load_tf_weights_in_albert
base_model_prefix = "albert"
_supports_s... | 3,147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0... | 3,147 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForPreTrainingOutput(ModelOutput):
"""
Output type of [`AlbertForPreTraining`]. | 3,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`torch.FloatTensor` of shape `(batch_size, seque... | 3,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
shape `(batch_size, sequence_length, hidden_size)`. | 3,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 3,148 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertModel(AlbertPreTrainedModel):
config_class = AlbertConfig
base_model_prefix = "albert"
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True):
super().__init__(config)
self.config = config
self.embeddings = AlbertEmbeddings(config)
self.encoder... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} ALBERT has
a different architecture in that its layers are shared across groups, which then has inner groups. If an ALBER... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
Any layer with in index other than [0,1,2,3] will result in an error. See base class PreTrainedModel for more
information about head pruning
"""
for layer, heads in heads_to_prune.items():
group_idx = int(layer / self.config.inner_group_num)
inner_group_idx = int(layer - ... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Opt... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.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:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=device)
if token_type_ids is None:
if hasattr(self.embeddings, "token_type_ids"):
buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
buffered_token_type_ids_ex... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if use_sdpa_attention_mask:
extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
attention_mask, embedding_output.dtype, tgt_len=seq_length
)
else:
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) if self.pooler is not None else None
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
po... | 3,149 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForPreTraining(AlbertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
def __init__(self, config: AlbertConfig):
super().__init__(config)
self.albert = AlbertModel(config)
self.predictions = AlbertMLMHead(config)
self.... | 3,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=AlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional... | 3,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
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
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
sente... | 3,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, AlbertForPreTraining
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
>>> model = AlbertForPreTraining.from_pretrained("albert/albert-base-v2")
>>> i... | 3,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
outputs = self.albert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_... | 3,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
if not return_dict:
output = (prediction_scores, sop_scores) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return AlbertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
sop_logits=sop_sc... | 3,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertMLMHead(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.dense = nn.Linear(config.hidden_size, config.emb... | 3,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
def _tie_weights(self) -> None:
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
... | 3,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertSOPHead(nn.Module):
def __init__(self, config: AlbertConfig):
super().__init__()
self.dropout = nn.Dropout(config.classifier_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
... | 3,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
class AlbertForMaskedLM(AlbertPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["predictions.decoder.bias", "predictions.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.albert = AlbertModel(config, add_pooling_layer=False)
self.predictions = AlbertMLMH... | 3,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
@add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Float... | 3,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` | 3,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoTokenizer, AlbertForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("albert/albert-base-v2")
>>> model = AlbertForMaskedLM.from_pretrained("albert/albert-base-v2")
>>> # add m... | 3,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/albert/modeling_albert.py |
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