Computes an attention mask of the form `(batch, seq_len)` with an attention for each element in the batch that stops at the corresponding ele

Convert Language Codes to format tokenizer uses if required

Create the repo if needed, cleans up repo_id with deprecated kwargs `repo_url` and `organization`, retrieves the token.

All context managers that the model should be initialized under go here. Args: torch_dtype (`torch.dtype`, *optional*):

Returns the list of files with their last modification timestamp.

Prepares the special tokens for generation, overwriting the generation config with their processed versions converted to tensor.

Uploads all modified files in `working_dir` to `repo_id`, based on `files_timestamps`.

Commit new `contents` to `filenames` inside a given `repo`.

Convert the inner content to tensors. Args: tensor_type (`str` or [`~utils.TensorType`], *optional*): Th

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

This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of thi

This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of thi

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

Imports transformers directly Args: path (`str`): The path to the source file file (`str`, *optional*): The file to join with the

r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, FlaxBartForConditionalGeneration

Creates a random float32 tensor

Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is created using from_dic

r""" Instantiate a Bark processor associated with a pretrained model. Args: pretrained_model_name_or_path (`str` or `os.P

Get element from dictionary by path. If element is not present, recursively add empty dictionaries. Args: d (dict): the dictionary t

Retrieves all the files associated to a model. Args: model_type (`str`): A valid model type (like "bert" or "gpt2") framewor

Find the target (height, width) dimension of the output image after resizing given the input image and the desired size. Args: i

Convert frequency from hertz to mels. Args: freq (`float` or `np.ndarray`): The frequency, or multiple frequencies, in h

Checks if `torch_musa` is installed and potentially if a MUSA is in the environment

Serializes this instance to a Python dictionary. Returns: `Dict[str, Any]`: Dictionary of all the attributes that make u

Execute Python code with a given environment and return the stdout/stderr as strings. If `should_fail=True`, the command is expected to fail.

Add a callback to the current list of [`~transformers.TrainerCallback`]. Args: callback (`type` or [`~transformers.Traine

A utility to add some content inside a given file. Args: file_name (`str` or `os.PathLike`): The name of the file in which we want to

A utility to add some content inside a given text. Args: text (`str`): The text in which we want to insert some content. conte

Converts an amplitude spectrogram to the decibel scale. This computes `20 * log10(spectrogram / reference)`, using basic logarithm properties

Converts a batch of amplitude spectrograms to the decibel scale. This computes `20 * log10(spectrogram / reference)`, using basic logarithm p

Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to the size given, it will b

Save the modeling files corresponding to a custom model/configuration/tokenizer etc. in a given folder. Optionally adds the proper fields in

This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this

This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this

Encodes pixel values into quantized tokens. Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels,

Filter a list of files to only keep the ones corresponding to a list of frameworks. Args: files (`List[Union[str, os.PathLike]]`): T

Creates a random float32 tensor

Creates a random float32 tensor