6i lddlmZddlZddlZddlZddlmZddlmZddl m Z ddl m Z m Z mZddlZddlmZmZmZmZdd lmZed j1d z Zgd Zd ddddddZddhZddhZddddddddd d! Zej>d"Z Gd#d$Z!Gd%d&Z"Gd'd(Z#Gd)d*Z$Gd+d,Z%Gd-d.Z&hd/Z'd0Z(d1Z)d2Z*d3Z+d4Z,dMd5Z-dNd6Z.d7Z/dOd8Z0dPd9Z1dQd:Z2dRd;Z3d<Z4dSd=Z5d>Z6d?Z7d@Z8dAZ9dBZ:dCZ;dDZdGZ?dHZ@ dTdIZAdUdJZBdVddddKdLZCy)W) annotationsN)Mapping)Path) UnionType)Unionget_args get_origin)MODELS_TO_PIPELINE#PIPELINE_TASKS_TO_SAMPLE_DOCSTRINGSPT_SAMPLE_DOCSTRINGS_prepare_output_docstrings) ModelOutputsrc transformers)zconfiguration_*.pyz modeling_*.pyztokenization_*.pyzprocessing_*.pyzimage_processing_*_fast.pyzimage_processing_*.pyzfeature_extractor_*.py)image_processing_autoIMAGE_PROCESSOR_MAPPING_NAMES)tokenization_autoTOKENIZER_MAPPING_NAMES)video_processing_autoVIDEO_PROCESSOR_MAPPING_NAMES)feature_extraction_autoFEATURE_EXTRACTOR_MAPPING_NAMES)processing_autoPROCESSOR_MAPPING_NAMES)configuration_autoCONFIG_MAPPING_NAMES)image_processor_classtokenizer_classvideo_processor_classfeature_extractor_classprocessor_class config_class preprocess__call__ImageProcessorFastProcessorMixinOpenAIGPTConfig XCLIPConfig Kosmos2ConfigKosmos2_5ConfigDonutSwinConfig EsmConfigParakeetCTCConfig LasrCTCConfigWav2Vec2Config) openaizx-clipkosmos2z kosmos2-5donutesmfoldparakeetlasrzwav2vec2-with-lmz*\[(.+?)\]\((https://huggingface\.co/.+?)\)ceZdZdddZdddZdddZdddZdddZdddZd ddZ d ddZ d ddZ d ddZ d ddZ dddZdddZdddZdddZdddZdddZdddZdddZdddZdddZdddZdddZy)ImageProcessorArgsz Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. N descriptionshapez Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If passing in videos with pixel values between 0 and 1, set `do_rescale=False`. z& Whether to resize the image. z> Describes the maximum input dimensions to the model. zR The size by which to make sure both the height and width can be divided. zP Whether to default to a square image when resizing, if size is an int. z Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. z+ Whether to center crop the image. z@ Size of the output image after applying `center_crop`. z Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model. a The size in `{"height": int, "width" int}` to pad the images to. Must be larger than any image size provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest height and width in the batch. Applied only when `do_pad=True.` z' Whether to rescale the image. zR Rescale factor to rescale the image by if `do_rescale` is set to `True`. z) Whether to normalize the image. ze Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. zw Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to `True`. z. Whether to convert the image to RGB. zU Returns stacked tensors if set to `pt, otherwise returns a list of tensors. zc Only `ChannelDimension.FIRST` is supported. Added for compatibility with slow processors. a 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 `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. zf The device to process the images on. If unset, the device is inferred from the input images. a5 Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157 z The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models. )__name__ __module__ __qualname__imagesvideos do_resizesize size_divisordefault_to_squareresampledo_center_crop crop_sizedo_padpad_size do_rescalerescale_factor do_normalize image_mean image_stddo_convert_rgbreturn_tensors data_formatinput_data_formatdevicedisable_groupingimage_seq_length]/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/utils/auto_docstring.pyr8r8Rs F F I  D L  H N I F  H J N L J I N N K  F   rWr8c(eZdZdddZdddZdddZdd dZd d dZd d dZddiZ ddiZ ddiZ dddZ dddZ dddZdddZdddZdddZdddZdddZdddZd ddZd!ddZd"ddZd#ddZd$ddZd%d&dZd'd(dZd'd(dZd)ddZd*d+dZd,d-dZy). ProcessorArgsz2 The image processor is a required input. z{image_processor_class})r:typez, The tokenizer is a required input. z{tokenizer_class}z2 The video processor is a required input. z{video_processor_class}z2 The audio processor is a required input. z{audio_processor_class}z4 The feature extractor is a required input. z{feature_extractor_class}z\ A Jinja template to convert lists of messages in a chat into a tokenizable string. strr:z The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If you pass a pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs. a  The audio or batch of audios to be prepared. Each audio can be a NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T is the sample length of the audio. z If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. Nr9aG Whether or not to add special tokens when encoding the sequences. This will use the underlying [`PretrainedTokenizerBase.build_inputs_with_special_tokens`] function, which defines which tokens are automatically added to the input ids. This is useful if you want to add `bos` or `eos` tokens automatically. boola Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence is provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). z'bool, str or [`~utils.PaddingStrategy`]a Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). z= 7.5` (Volta). z Whether to return token type IDs. If left to the default, will return the token type IDs according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are token type IDs?](../glossary#token-type-ids) a Whether to return the attention mask. If left to the default, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) z Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead of returning overflowing tokens. zC Whether or not to return special tokens mask information. z Whether or not to return `(char_start, char_end)` for each token. This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using Python's tokenizer, this method will raise `NotImplementedError`. zE Whether or not to return the lengths of the encoded inputs. z@ Whether or not to print more information and warnings. z Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids` method). zstr, list[str] or list[int]a The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a list of strings (pretokenized string). If you pass pretokenized input, set `is_split_into_words=True` to avoid ambiguity with batched inputs. z!str, list[str] or list[list[str]]a! Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace) which it will tokenize. This is useful for NER or token classification. zd Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale. z(list[list[int]] or list[list[list[int]]]zY Word-level integer labels (for token classification tasks such as FUNSD, CORD). zlist[int] or list[list[int]]) r<r=r>image_processor tokenizervideo_processoraudio_processorfeature_extractor chat_templatetextaudioaudiosrPadd_special_tokenspadding truncation max_lengthstridepad_to_multiple_ofreturn_token_type_idsreturn_attention_maskreturn_overflowing_tokensreturn_special_tokens_maskreturn_offsets_mapping return_lengthverbose text_pair text_targettext_pair_targetis_split_into_wordsboxes word_labelsrVrWrXrZrZs* O$ I* O* O,  M  D  E F N  : G O#J* J F      ! "  M G . I 4 K 4   ;  E/ KrWrZcjeZdZdddZdddZdddZdddZdddZd d dZd d dZ d ddZ dddZ dddZ dddZ dd dZdd dZdddZdddZdddZdddZdddZdddZdddZdddZdddZdddZdd dZd!d dZd"ddZd#ddZd$ddZd%d&dZd'd(dZ d)ddZ!d*ddZ"d+d,dZ#d-d.dZ$d/d0dZ%y)1 ModelArgsa7 Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. (of shape `(batch_size, sequence_length)`r9aC Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. Na" Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) a Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library (`pip install torchcodec`) or the soundfile library (`pip install soundfile`). To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`. See [`{processor_class}.__call__`] for details. z Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) z Mask to avoid performing attention on certain token indices. By default, a causal mask will be used, to make sure the model can only look at previous inputs in order to predict the future. z/of shape `(batch_size, target_sequence_length)`z Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. 5of shape `(batch_size, sequence_length, hidden_size)`a, Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. a  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) z Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) a Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only [`~cache_utils.Cache`] instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, [`~cache_utils.DynamicCache`] will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. a Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. a Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) a( Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. z