text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def generate(
self,
input_features,
generation_config=None,
logits_processor=None,
return_timestamps=None,
task=None,
language=None,
is_multilingual=None,
**kwargs,
):
if generation_config is None:
generation_config = self.g... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual:
if hasattr(generation_config, "language"):
forced_decoder_ids.append((1, generation_config.lang_to_id[generation_config.language]))
else:
forced_decoder_ids.append((1, None))
... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if (
hasattr(generation_config, "return_timestamps") and generation_config.return_timestamps
) or return_timestamps:
logits_processor = [
FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, decoder_input_length)
]
else:
if f... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a ... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": position_ids,
}
def update_inputs_for_generation(... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperForAudioClassificationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_... | 9,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
input_features,
encoder_outputs=None,
output_attentions=None,
output_hidden_states: bool = True,
return_dict: bool = True,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
... | 9,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if self.config.use_weighted_layer_sum:
hidden_states = jnp.stack(encoder_outputs, axis=1)
norm_weights = jax.nn.softmax(self.layer_weights, axis=-1)
hidden_states = jnp.sum(hidden_states * jnp.reshape(norm_weights, [-1, 1, 1]), axis=1)
else:
hidden_states = encode... | 9,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperForAudioClassification(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForAudioClassificationModule
dtype: jnp.dtype = jnp.float32
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input... | 9,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freez... | 9,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
def __call__(
self,
input_features: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional... | 9,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
rngs=rngs,
) | 9,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class BasicTextNormalizer:
def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
self.split_letters = split_letters
def __call__(self, s: str):
s = s.lower()
s = re.su... | 9,936 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
class EnglishNumberNormalizer:
"""
Convert any spelled-out numbers into arabic numbers, while handling:
- remove any commas
- keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
- spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
- spell out `one` and `... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
self.zeros = {"o", "oh", "zero"}
# fmt: off
self.ones = {
name: i
for i, name in enumerate(
["one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
self.tens = {
"twenty": 20,
"thirty": 30,
"forty": 40,
"fifty": 50,
"sixty": 60,
"seventy": 70,
"eighty": 80,
"ninety": 90,
}
self.tens_plural = {name.replace("y", "ies"): (value, "s") for name, value in self... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
self.multipliers = {
"hundred": 100,
"thousand": 1_000,
"million": 1_000_000,
"billion": 1_000_000_000,
"trillion": 1_000_000_000_000,
"quadrillion": 1_000_000_000_000_000,
"quintillion": 1_000_000_000_000_000_000,
"sextilli... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
self.preceding_prefixers = {
"minus": "-",
"negative": "-",
"plus": "+",
"positive": "+",
}
self.following_prefixers = {
"pound": "£",
"pounds": "£",
"euro": "€",
"euros": "€",
"dollar": "$",
... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
self.words = {
key
for mapping in [
self.zeros,
self.ones,
self.ones_suffixed,
self.tens,
self.tens_suffixed,
self.multipliers,
self.multipliers_suffixed,
self.precedin... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
def output(result: Union[str, int]):
nonlocal prefix, value
result = str(result)
if prefix is not None:
result = prefix + result
value = None
prefix = None
return result
if len(words) == 0:
return
for i... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
has_prefix = current[0] in self.prefixes
current_without_prefix = current[1:] if has_prefix else current
if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
# arabic numbers (potentially with s... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
prefix = current[0] if has_prefix else prefix
if f.denominator == 1:
value = f.numerator # store integers as int
else:
value = current_without_prefix
elif current not in self.words:
# non-numeric words
i... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
if value is None:
value = ones
elif isinstance(value, str) or prev in self.ones:
if prev in self.tens and ones < 10: # replace the last zero with the digit
value = value[:-1] + str(ones)
else:
va... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
elif isinstance(value, str) or prev in self.ones:
if prev in self.tens and ones < 10:
yield output(value[:-1] + str(ones) + suffix)
else:
yield output(str(value) + str(ones) + suffix)
elif ones < 10:
... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
value = str(value) + str(tens)
else:
if value % 100 == 0:
value += tens
else:
value = str(value) + str(tens)
elif current in self.tens_suffixed:
# ordinal or cardinal; yield the number rig... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
elif isinstance(value, str) or value == 0:
f = to_fraction(value)
p = f * multiplier if f is not None else None
if f is not None and p.denominator == 1:
value = p.numerator
else:
yield output(... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
yield output(str(p.numerator) + suffix)
else:
yield output(value)
yield output(str(multiplier) + suffix)
else: # int
before = value // 1000 * 1000
residual = value % 1000
valu... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
if next in self.words or next_is_numeric:
prefix = self.preceding_prefixers[current]
else:
yield output(current)
elif current in self.following_prefixers:
# apply prefix (dollars, cents, etc.) only after a number
if valu... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
yield output(current)
else:
yield output(str(value) + suffix)
else:
yield output(current)
elif current in self.specials:
if next not in self.words and not next_is_numeric:
# apply special hand... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
ones = self.ones.get(next, 0)
value = str(value or "") + str(ones) * repeats
skip = True
else:
if value is not None:
yield output(value)
yield output(current)
e... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
if value is not None:
yield output(value)
def preprocess(self, s: str):
# replace "<number> and a half" with "<number> point five"
results = []
segments = re.split(r"\band\s+a\s+half\b", s)
for i, segment in enumerate(segments):
if len(segment.strip()) == 0:... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
# but remove spaces which could be a suffix
s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
return s
def postprocess(self, s: str):
def combine_cents(m: Match):
try:
currency = m.group(1)
integer = m.group(2)
cents = int(m.... | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
def __call__(self, s: str):
s = self.preprocess(s)
s = " ".join(word for word in self.process_words(s.split()) if word is not None)
s = self.postprocess(s)
return s | 9,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
class EnglishSpellingNormalizer:
"""
Applies British-American spelling mappings as listed in [1].
[1] https://www.tysto.com/uk-us-spelling-list.html
"""
def __init__(self, english_spelling_mapping):
self.mapping = english_spelling_mapping
def __call__(self, s: str):
return " "... | 9,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
class EnglishTextNormalizer:
def __init__(self, english_spelling_mapping):
self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
self.replacers = {
# common contractions
r"\bwon't\b": "will not",
r"\bcan't\b": "can not",
r"\blet's\b": "let us",
... | 9,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
r"\bgov\b": "governor ",
r"\bald\b": "alderman ",
r"\bgen\b": "general ",
r"\bsen\b": "senator ",
r"\brep\b": "representative ",
r"\bpres\b": "president ",
r"\brev\b": "reverend ",
r"\bhon\b": "honorable ",
r"\basst\b": "ass... | 9,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
r"'s\b": " is",
r"'d\b": " would",
r"'ll\b": " will",
r"'t\b": " not",
r"'ve\b": " have",
r"'m\b": " am",
}
self.standardize_numbers = EnglishNumberNormalizer()
self.standardize_spellings = EnglishSpellingNormalizer(english_spelling_map... | 9,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
def __call__(self, s: str):
s = s.lower()
s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets
s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis
s = re.sub(self.ignore_patterns, "", s)
s = re.sub(r"\s+'", "'", s) # standardize when there... | 9,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
s = re.sub(r"\s+", " ", s) # replace any successive whitespace characters with a space
return s | 9,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/english_normalizer.py |
class LlavaOnevisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlavaOnevisionForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
Args:
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `SiglipVisionConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
The config object or dictionary of the text back... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
If `"full"`, the full vision features are used.
vision_feature_layer (`int`, *optional*, defaults to -1):
The index of the layer to select the vision feature.
vision_aspect_r... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
Example:
```python
>>> from transformers import LlavaOnevisionForConditionalGeneration, LlavaOnevisionConfig, SiglipVisionConfig, Qwen2Config
>>> # Initializing a CLIP-vision config
>>> vision_config = SiglipVisionConfig()
>>> # Initializing a Llama config
>>> text_config = Qwen2Config()
... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
def __init__(
self,
vision_config=None,
text_config=None,
image_token_index=151646,
video_token_index=151647,
projector_hidden_act="gelu",
vision_feature_select_strategy="full",
vision_feature_layer=-1,
vision_aspect_ratio="anyres_max_9",
i... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
self.vision_feature_select_strategy = vision_feature_select_strategy
self.vision_feature_layer = vision_feature_layer
self.vision_aspect_ratio = vision_aspect_ratio
image_grid_pinpoints = (
image_grid_pinpoints
if image_grid_pinpoints is not None
else [
... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
[1920, 384],
[1920, 768],
[1920, 1152],
[1920, 1536],
[1920, 1920],
[1920, 2304],
[2304, 384],
[2304, 768],
[2304, 1152],
[2304, 1536],
[2304, 1920],
... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
if isinstance(vision_config, dict):
vision_config["model_type"] = (
vision_config["model_type"] if "model_type" in vision_config else "siglip_vision_model"
)
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
elif vision_config is Non... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen2"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["qwen2"]()
... | 9,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/configuration_llava_onevision.py |
class LlavaOnevisionProcessorKwargs(ProcessingKwargs, total=False):
# see processing_utils.ProcessingKwargs documentation for usage.
_defaults = {
"text_kwargs": {
"padding": False,
},
"image_kwargs": {},
"video_kwargs": {},
} | 9,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
class LlavaOnevisionProcessor(ProcessorMixin):
r"""
Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
[`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`Ll... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
Args:
image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`LlamaTokenizerFast`], *optional*):
The tokenizer is a required input.
video_processor ([`LlavaOnevisionVideoProcessor`], *optional*):
Th... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
video_token (`str`, *optional*, defaults to `"<video>"`):
Special token used to denote video location.
""" | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
attributes = ["image_processor", "tokenizer", "video_processor"]
valid_kwargs = [
"chat_template",
"num_image_tokens",
"vision_feature_select_strategy",
"image_token",
"video_token",
]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
def __init__(
self,
image_processor=None,
tokenizer=None,
video_processor=None,
num_image_tokens=None,
vision_feature_select_strategy=None,
chat_template=None,
image_token="<image>",
video_token="<video>",
**kwargs,
):
self.num_... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
audio=None,
videos: VideoInput = None,
**kwargs: Unpack[LlavaOnevisionProcessorKwargs],
) -> BatchFeature:
"""
Ma... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last ... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError("Invalid input text. Please provide a string, or a list of strings")
image_inputs = video_inputs = {}
if images is not None:
image_inpu... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
one_video = to_numpy_array(video_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(one_video[0], channel_dim=output_kwargs["images_kwargs"].get("data_format"))
num_frames = one_video.shape[0] # frame dim is always after batch dim
patches_height_width = int(math.sq... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
def _expand_image_tokens(
self,
text: List[TextInput],
image_sizes: Iterable[Union[List[int], int]],
height: int,
width: int,
special_token: str,
num_frames: int = 1,
):
prompt_strings = []
for sample in text:
while special_token in... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
sample = sample.replace(special_token, "<placeholder>" * num_image_tokens * num_frames, 1)
prompt_strings.append(sample)
text = [sample.replace("<placeholder>", special_token) for sample in prompt_strings]
return text | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
image_grid_pinpoints = self.image_processor.image_grid_pinpoints
height_best_resolution, width_best_resolution = select_best_resolution(
[orig_height, orig_width], image_grid_pinpoints
... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
"""
Get number of features for a given image with height/width. LLaVA-NeXT is different from LLaVA
because it divided each image into patches depending on its resolution. Therefore we need ... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
original_aspect_ratio = width / height
current_aspect_ratio = current_width / current_height
if original_aspect_ratio > current_aspect_ratio:
new_height = int(height * (current_width / width))
padding = (current_height - new_height) // 2
current_height -= padding * 2
... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method f... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
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_... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
outputs = super().save_pretrained(save_directory, **kwargs)
if video_processor_present:
self.attributes += ["video_processor"]
return outputs
# override to load video-config from a separate config file
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwarg... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
try:
video_processor = AutoImageProcessor.from_pretrained(
pretrained_model_name_or_path, subfolder="video_processor"
)
processor.video_processor = video_processor
except EnvironmentError:
# this means users are using prev version of saved processo... | 9,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/processing_llava_onevision.py |
class LlavaOnevisionImageProcessor(BaseImageProcessor):
r"""
Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame. | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.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
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified sc... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Whether to convert the image to RGB.
""" | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
model_input_names = ["pixel_values_videos"] | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
image_grid_pinpoints: List = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool ... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
[384, 2304],
[768, 384],
[768, 768],
[768, 1152],
[768, 1536],
[768, 1920],
[768, 2304],
[1152, 384],
[1152, 768],
[1152, 1152],
[1152, 1536],
[... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
self.do_resize = do_resize
self.size = size
self.image_grid_pinpoints = image_grid_pinpoints
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is n... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.pad
def pad(
self,
image: np.ndarray,
padding: Union[int, Tuple[int, int], Iterable[Tuple[int, int]]],
mode: PaddingMode = PaddingMode.CONSTANT,
constant_values: Union[float, Iter... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Args:
image (`np.ndarray`):
The image to pad.
padding (`int` or `Tuple[int, int]` or `Iterable[Tuple[int, int]]`):
Padding to apply to the edges of the height, width axes. Can be one of three formats:
- `((before_height, after_height), (before_widt... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
- `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array.
constant_values (`float` or `Iterable[float]`, *optional*):
The value to use for the padding if `mode` is `"constant"`.
data_format (`str` or `ChannelDimension`, *optional*):
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
If unset, will use the inferred format of the input image. | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Returns:
`np.ndarray`: The padded image.
"""
# call the general `pad` if padding on `height/width`, otherwise it's the `num_patched` dim
if isinstance(padding, int) or len(padding) != 4:
return pad(image, padding, mode, constant_values, data_format, input_data_format) | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image)
if mode == PaddingMode.CONSTANT:
image = np.pad(image, padding, mode="constant", constant_values=constant_values)
elif mode == PaddingMode.REFLECT:
image = np.pad(image, padding, m... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._resize_for_patching
def _resize_for_patching(
self, image: np.array, target_resolution: tuple, resample, input_data_format: ChannelDimension
) -> np.array:
"""
Resizes an image to a target r... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
# Resize the image
resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format)
return resized_image
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor._pad_for_patching
def _pad_for_patching(
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
# Copied from transformers.models.llava_next.image_processing_llava_next.LlavaNextImageProcessor.get_image_patches
def get_image_patches(
self,
image: np.array,
grid_pinpoints,
size: tuple,
patch_size: int,
resample: PILImageResampling,
data_format: ChannelDim... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Args:
image (np.array):
The input image to be processed.
grid_pinpoints (List):
A string representation of a list of possible resolutions.
size (`tuple`):
Size to resize the original image to.
patch_size (`int`):
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
possible_resolutions = grid_pinpoints
image_size = get_image_size(image, channel_dim=input_data_format)
best_resolution = select_best_resolution(image_size, possible_resolutions)
resized_image = self._resize_for_patching(
image, best_resolution, resample=resample, input_data_format=... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
resized_original_image = resize(
image,
size=size,
resample=resample,
data_format=data_format,
input_data_format=input_data_format,
)
image_patches = [resized_original_image] + patches
return image_patches
# Copied from transform... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Args:
pixel_values (`List[np.ndarray]`):
An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. Can be one of:
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
If unset, will use the inferred format of the input image. | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Returns:
List[`np.ndarray`]: The padded images.
"""
max_patch = max(len(x) for x in pixel_values)
pixel_values = [
self.pad(
image,
padding=((0, max_patch - image.shape[0]), (0, 0), (0, 0), (0, 0)),
data_format=data_format,
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
def _preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float,... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resampl... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviati... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
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, height, width) format.
- `"channels_last"` or `ChannelDimens... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
if do_rescale:
images = [
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
for image in images
]
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.