image-match/python/py/Lib/site-packages/transformers/models/mvp/configuration_mvp.py

# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""MVP model configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="RUCAIBox/mvp")
@strict
class MvpConfig(PreTrainedConfig):
    r"""
    use_prompt (`bool`, *optional*, defaults to `False`):
        Whether or not to use prompt.
    prompt_length (`int`, *optional*, defaults to 100):
        The length of prompt.
    prompt_mid_dim (`int`, *optional*, defaults to 800):
        Dimensionality of the "intermediate" layer in prompt.

    Example:

    ```python
    >>> from transformers import MvpConfig, MvpModel

    >>> # Initializing a MVP RUCAIBox/mvp style configuration
    >>> configuration = MvpConfig()

    >>> # Initializing a model (with random weights) from the RUCAIBox/mvp style configuration
    >>> model = MvpModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "mvp"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_attention_heads": "encoder_attention_heads",
        "hidden_size": "d_model",
        "num_hidden_layers": "encoder_layers",
    }

    vocab_size: int = 50267
    max_position_embeddings: int = 1024
    encoder_layers: int = 12
    encoder_ffn_dim: int = 4096
    encoder_attention_heads: int = 16
    decoder_layers: int = 12
    decoder_ffn_dim: int = 4096
    decoder_attention_heads: int = 16
    encoder_layerdrop: float | int = 0.0
    decoder_layerdrop: float | int = 0.0
    activation_function: str = "gelu"
    d_model: int = 1024
    dropout: float | int = 0.1
    attention_dropout: float | int = 0.0
    activation_dropout: float | int = 0.0
    init_std: float = 0.02
    classifier_dropout: float | int = 0.0
    scale_embedding: bool = False
    use_cache: bool = True
    pad_token_id: int | None = 1
    bos_token_id: int | None = 0
    eos_token_id: int | list[int] | None = 2
    is_encoder_decoder: bool = True
    decoder_start_token_id: int | None = 2
    use_prompt: bool = False
    prompt_length: int = 100
    prompt_mid_dim: int = 800
    is_decoder: bool = False
    tie_word_embeddings: bool = True


__all__ = ["MvpConfig"]