image-match/python/py/Lib/site-packages/transformers/models/aria/configuration_aria.py

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#           This file was automatically generated from src/transformers/models/aria/modular_aria.py.
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# Copyright 2024 The Rhymes-AI Teams 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring
from ...utils.type_validators import interval
from ..auto import CONFIG_MAPPING, AutoConfig


@auto_docstring(checkpoint="rhymes-ai/Aria")
@strict
class AriaTextConfig(PreTrainedConfig):
    r"""
    moe_num_experts (`int`, *optional*, defaults to 8):
        The number of experts in the MoE layer.
    moe_topk (`int`, *optional*, defaults to 2):
        The number of top experts to route to for each token.
    moe_num_shared_experts (`int`, *optional*, defaults to 2):
        The number of shared experts.
    """

    model_type = "aria_text"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise",
        "layers.*.self_attn.k_proj": "colwise",
        "layers.*.self_attn.v_proj": "colwise",
        "layers.*.self_attn.o_proj": "rowwise",
        "layers.*.mlp.shared_experts.gate_proj": "colwise",
        "layers.*.mlp.shared_experts.up_proj": "colwise",
        "layers.*.mlp.shared_experts.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    vocab_size: int = 32000
    hidden_size: int = 4096

    intermediate_size: int = 4096
    num_hidden_layers: int = 32
    num_attention_heads: int = 32
    num_key_value_heads: int | None = None
    hidden_act: str = "silu"
    max_position_embeddings: int = 2048
    initializer_range: float = interval(min=0.0, max=1.0)(default=0.02)
    rms_norm_eps: float = 1e-6
    use_cache: bool = True
    pad_token_id: int | None = 2
    bos_token_id: int | None = 1
    eos_token_id: int | list[int] | None = 2
    pretraining_tp: int | None = 1
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: int | float | None = 0.0
    mlp_bias: bool = False
    head_dim: int | None = None
    base_config_key = "text_config"
    moe_num_experts: int = 8
    moe_topk: int = 2
    moe_num_shared_experts: int = 2

    def __post_init__(self, **kwargs):
        if self.head_dim is None:
            self.head_dim = self.hidden_size // self.num_attention_heads
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        super().__post_init__(**kwargs)

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
                f"heads ({self.num_attention_heads})."
            )


@auto_docstring(checkpoint="rhymes-ai/Aria")
@strict
class AriaConfig(PreTrainedConfig):
    r"""
    projector_patch_to_query_dict (`dict`, *optional*):
        Mapping of patch sizes to query dimensions.
    """

    model_type = "aria"
    attribute_map = {
        "image_token_id": "image_token_index",
    }
    sub_configs = {"text_config": AriaTextConfig, "vision_config": AutoConfig}

    vision_config: dict | PreTrainedConfig | None = None
    text_config: dict | AriaTextConfig | None = None
    vision_feature_layer: int | list[int] = -1
    projector_patch_to_query_dict: dict | None = None
    image_token_index: int = 9
    initializer_range: float = 0.02
    tie_word_embeddings: bool = False

    def __post_init__(self, **kwargs):
        # Convert the keys and values of projector_patch_to_query_dict to integers
        # This ensures consistency even if they were provided as strings
        if self.projector_patch_to_query_dict is None:
            self.projector_patch_to_query_dict = {
                1225: 128,
                4900: 256,
            }
        self.projector_patch_to_query_dict = {int(k): int(v) for k, v in self.projector_patch_to_query_dict.items()}
        self.max_value_projector_patch_to_query_dict = max(self.projector_patch_to_query_dict.values())

        if isinstance(self.vision_config, dict):
            self.vision_config["model_type"] = "idefics3_vision"
            self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config)
        elif self.vision_config is None:
            self.vision_config = CONFIG_MAPPING["idefics3_vision"]()

        if isinstance(self.text_config, dict) and "model_type" in self.text_config:
            self.text_config = AriaTextConfig(**self.text_config)
        elif self.text_config is None:
            self.text_config = AriaTextConfig()

        super().__post_init__(**kwargs)


__all__ = ["AriaConfig", "AriaTextConfig"]