Run prediction and returns predictions and potential metrics. Depending on the dataset and your use case, your test dataset may cont

Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline) as the current one.

Moves the transformation object to GPU memory Returns: A version of the transformation on GPU

A utility that returns number of image patches for a given image size. Args: height (`int`): Height of t

Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a no

This is a context manager to temporarily change transformers modules logging level to the desired value and have it restored to the original

Args: dimension: fixed_dimension: num_token_to_add: Returns:

Checks if `torch.hpu` is available and potentially if a HPU is in the environment

Whether or not this process is the global main process (when training in a distributed fashion on several machines, this is only goin

Util for integration test. Text is tokenized and then reverted back to text. Both results are then checked. Args:

Converts an index (integer) in a token (str) using the vocab.

Method to initialize the backbone. This method is called by the constructor of the base class after the pretrained model weights have

Checks whether `chars` is a punctuation character.

Creates a draft of a model card using the information available to the `Trainer`. Args: language (`str`, *optional*):

Overrides the `from_dict` method from the base class to make sure `pad_and_return_pixel_mask` is updated if image processor is create

Returns the sequence length of the cached states. A layer index can be optionally passed.

Returns the sequence length of the cached states. A layer index can be optionally passed.

Returns the sequence length of the cached states. A layer index can be optionally passed.

Groups videos by shape. Returns a dictionary with the shape as key and a list of videos with that shape as value, and a dictionary with t

Creates a frequency bin conversion matrix used to obtain a mel spectrogram. This is called a *mel filter bank*, and various implementation ex

Collates the audio and text inputs, as well as their targets, into a padded batch. Audio inputs are padded by SpeechT5FeatureExtract

Temporarily patches the variables defines in `tests_fetcher` to use a different location for the repo.

Verify that the config arguments to be passed to load_backbone are valid

Converts a token (str) in an id using the vocab.

Checks whether `chars` is a control character.

Checks whether `chars` is a whitespace character.

Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width in the batch and optio

This function prepares a list of numpy videos.

This function generates a JSON schema for a given function, based on its docstring and type hints. This is mostly used for passing lists of t

Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding s

Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width in the batch and optio

This method forwards all its arguments to [`Owlv2ImageProcessor.post_process_object_detection`]. Please refer to the docstring of thi

Saves the attributes of this processor (tokenizer...) in the specified directory so that it can be reloaded using the [`~BarkProcesso

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the gi

Depending on the input and internal state we might trigger a warning about a sequence that is too long for its corresponding model

Converts a sequence of tokens (string) in a single string.

Converts bounding boxes from corners format to center format. corners format: contains the coordinates for the top-left and bottom-right cor

Decodes the given frames into an output audio waveform. Note that the output might be a bit bigger than the input. In that case, any

This method returns a set difference between the keys in the tracked state dict and the one we have access so far. This is an effective method