Internally used error class for signalling an optional dependency was not found.

AddedToken represents a token to be added to a Tokenizer An AddedToken can have special options defining the way it should behave.

Module class that surfaces all objects but only performs associated imports when the objects are requested.

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `

r""" Holds the output of the image processor specific `__call__` methods. This class is derived from a python dictionary and can be used as a

This subclass of `argparse.ArgumentParser` uses type hints on dataclasses to generate arguments. The class is designed to play well with the

Holds the output of the [`~tokenization_utils_base.PreTrainedTokenizerBase.__call__`], [`~tokenization_utils_base.PreTrainedTokenizerBase.enc

TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using [`HfA

" A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed. Args: - config_ma

Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Args: model ([`PreTrained

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

Base class for outputs of token classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `l

Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`tf.Tensor` of shape `(batch_s

Context manager to capture `logging` streams Args: logger: 'logging` logger object Returns: The captured output is avai

r""" Construct a BERT tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main met

Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same leng

Base class for outputs of question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `lab

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor`

Base class for masked language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` i

r""" Class that holds a configuration for a generation task. A `generate` call supports the following generation methods for text-decoder, tex

Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, retur

Data collator that will dynamically pad the inputs received, as well as the labels. Args: tokenizer ([`PreTrainedTokenizer`] or [`Pr

r""" This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to instantiate a BERT mode

Base class for sequence-to-sequence language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned w

Data collator that will dynamically pad the inputs received. Args: tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]

Base class for outputs of multiple choice models. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor`

Data collator that will dynamically pad the inputs received, as well as the labels. Args: tokenizer ([`PreTrainedTokenizer`] or [`Pr

Base class for outputs of sentence classification models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned wh

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (`bool

Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, retur

An error raised when the interpretor cannot evaluate a Python expression, due to syntax error or unsupported operations.

r""" This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA model according to the

A Linear layer with built-in nonstandard initializations. Called just like torch.nn.Linear. Implements the initializers in 1.11.4, plus some

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

A 3D rotation. Depending on how the object is initialized, the rotation is represented by either a rotation matrix or a quaternion, though bo

Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when

r""" Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as methods for loading/do

Implements Adam algorithm with weight decay fix as introduced in [Decoupled Weight Decay Regularization](https://arxiv.org/abs/1711.05101).

Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`

Base class for outputs of question answering models. Args: loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `s

Base class for sequence-to-sequence language models outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the num

Base class for outputs of token classification models. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of

A single training/test example for simple sequence classification. Args: guid: Unique id for the example. text_a: string. Th

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args:

Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents contents, and it formats

Base class for masked language models outputs. Args: loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-mas

r""" This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART model according to the sp

Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, nu

This is a wrapper class about all possible attributes and features that you can play with a model that has been loaded using `auto-awq` libra

Base class for causal language model (or autoregressive) outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length

Holds the basic information about a new model for the add-new-model-like command. Args: model_name (`str`): The model name.

Quantizes the given activation. Args: activation_bit (`int`): Bitwidth for the quantized activation. act_range_m

A class representing a rigid transformation. Little more than a wrapper around two objects: a Rotation object and a [*, 3] translation Design

Base class for outputs of multiple choice models. Args: loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labe

Base class for outputs of backbones. Args: feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, wid

This is a wrapper class about all possible attributes and features that you can play with a model that has been loaded using `bitsandbytes`.

Evaluation output (always contains labels), to be used to compute metrics. Parameters: predictions (`np.ndarray`): Predictions of th

This is a wrapper class about all possible attributes and features that you can play with a model that has been loaded using `optimum` api fo

r""" This is the configuration class to store the configuration of a [`PatchTSMixerModel`]. It is used to instantiate a PatchTSMixer model acc

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`tf.Tensor` of shape

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args:

r""" Constructs a Wav2Vec2 processor which wraps a Wav2Vec2 feature extractor and a Wav2Vec2 CTC tokenizer into a single processor. [`Wav

This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to instantiate a GPT-2 model a

Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `l

Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `l

Mixin that contain utilities for preparing image features.

Index grouping entries within a tensor.

Index grouping entries within a tensor.

`Memory` NamedTuple have a single field `bytes` and you can get a human readable str of the number of mega bytes by calling `__repr__`

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor`