r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-unca

r""" Instantiate a [`ModelCard`] from a pre-trained model model card. Parameters: pretrained_model_name_or_path: either:

Number of byte required for this data type Returns: Integer > 0

Will look for the words added to the trie within `text`. Output is the original string splitted along the boundaries of the words fou

Creates a random int32 tensor of the shape within the vocab size.

Concatenates transformations along a new dimension. Args: ts: A list of T objects dim:

Analogous to torch.unsqueeze. The dimension is relative to the shared dimensions of the rotation/translation. Args: dim:

Transforms the `input_audio` to a discrete representation made out of `music_tokens`. Args: input_audio (`torch.Tensor`)

r""" # Using torch.hub ! import torch tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'bert-base-uncased'

Save a model card object to the directory or file `save_directory_or_file`.

r""" Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.

r""" Generates sequences of token ids for models with a language modeling head. <Tip warning={true}> Most generation-contro

Creates a `tf.initializers.TruncatedNormal` with the given range. Args: initializer_range (*float*, defaults to 0.02): Standard devi

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

Loads the dictionary from a text file with the format: ``` <symbol0> <count0> <symbol1> <count1> ... ```

Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):

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

Prune a linear layer to keep only entries in index. Used to remove heads. Args: layer (`torch.nn.Linear`): The layer to prune.

Returns a copy of the Rotation whose underlying Tensor has been detached from its torch graph. Returns: A copy of the Ro

<Tip warning={true}> This method is deprecated, `__call__` should be used instead. </Tip>

Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the vocabulary. Args:

Creates a random float32 tensor

Convert self to a tuple containing all the attributes/keys that are not `None`.

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

Main training entry point. Args: resume_from_checkpoint (`str` or `bool`, *optional*): If a `str`, local

Tokenize the provided input and eventually returns corresponding tokens id: - **text_input**: String to tokenize - **return_ids**: Bo

Utility factory method to build a [`Pipeline`]. Pipelines are made of: - A [tokenizer](tokenizer) in charge of mapping raw textual

Simplifies an object by rounding float numbers, and downcasting tensors/numpy arrays to get simple equality test within tests.

Log `logs` on the various objects watching training. Subclass and override this method to inject custom behavior. Args:

Converts the old size parameter in the config into the new dict expected in the config. This is to ensure backwards compatibility with the ol

Flatten a nested dict into a single level dict.

Applies the transformation to a coordinate tensor. Args: pts: A [*, 3] coordinate tensor. Returns: T

Creates a new instance of this constraint. Args: stateful(`bool`): Whether to not only copy the constraint for new insta

This is a thin wrapper that sets the model's loss output head as the loss if the user does not specify a loss function themselves.

Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`]. Args: data (`di

Serializes this instance to a Python dictionary.

Need to document this

Returns the model's output embeddings. Returns: `nn.Module`: A torch module mapping hidden states to vocabulary.

Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch` and/or `tf` (if installed). Args: seed (`int

This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifie

Serializes this instance to a JSON string.

Filter dataset with heuristics. Config, test and has_no_keywords files are removed with a given probability.

Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*. Parameters: commit_mes

Constructs a `ModelCard` from a Python dictionary of parameters.

Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer. Args

Args: tmp_dir (`string`, *optional*): if `None`: - a unique temporary path will be created

Embeds the upper level music tokens and upsamples them to provide as audio conditioning.