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#           This file was automatically generated from src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_qwen2_5_vl.py file directly. One of our CI enforces this.
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# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
import inspect

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...modeling_rope_utils import RopeParameters
from ...utils import auto_docstring


@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct")
@strict
class Qwen2_5_VLVisionConfig(PreTrainedConfig):
    r"""
    tokens_per_second (`int`, *optional*, defaults to 41):
        Number of tokens to merge for each second of video.
    window_size (`int`, *optional*, defaults to 11):
        Size of windows.
    out_hidden_size (`int`, *optional*, defaults to 3584):
        The output hidden size of the vision model.
    fullatt_block_indexes (`int`, *optional*, defaults to `[7, 15, 23, 31]`):
        Indices of layers with full attention
    """

    model_type = "qwen2_5_vl_vision"
    base_config_key = "vision_config"

    depth: int = 32
    hidden_size: int = 3584
    hidden_act: str = "silu"
    intermediate_size: int = 3420
    num_heads: int = 16
    in_channels: int = 3
    patch_size: int | list[int] | tuple[int, int] = 14
    spatial_merge_size: int = 2
    temporal_patch_size: int | list[int] | tuple[int, int] = 2
    tokens_per_second: int = 4
    window_size: int = 112
    out_hidden_size: int = 3584
    fullatt_block_indexes: list[int] | tuple[int, ...] = (7, 15, 23, 31)
    initializer_range: float = 0.02


@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct")
@strict
class Qwen2_5_VLTextConfig(PreTrainedConfig):
    r"""
    ```python
    >>> from transformers import Qwen2_5_VLTextModel, Qwen2_5_VLConfig

    >>> # Initializing a Qwen2_5_VL style configuration
    >>> configuration = Qwen2_5_VLConfig()

    >>> # Initializing a model from the Qwen2-VL-7B style configuration
    >>> model = Qwen2_5_VLTextModel(configuration)

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

    model_type = "qwen2_5_vl_text"
    base_config_key = "text_config"
    keys_to_ignore_at_inference = ["past_key_values"]
    default_theta = 1000000.0
    # Default tensor parallel plan for base model `Qwen2_5_VL`
    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.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.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"]),
    }
    ignore_keys_at_rope_validation = {"mrope_section"}

    vocab_size: int = 152064
    hidden_size: int = 8192
    intermediate_size: int = 29568
    num_hidden_layers: int = 80
    num_attention_heads: int = 64
    num_key_value_heads: int | None = 8
    hidden_act: str = "silu"
    max_position_embeddings: int = 32768
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-05
    use_cache: bool = True
    use_sliding_window: bool | None = False
    sliding_window: int | None = 4096
    max_window_layers: int | None = 80
    layer_types: list[str] | None = None
    attention_dropout: float | int | None = 0.0
    rope_parameters: RopeParameters | dict | None = None
    bos_token_id: int | None = 151643
    eos_token_id: int | list[int] | None = 151645
    pad_token_id: int | None = None

    def __post_init__(self, **kwargs):
        self.sliding_window = self.sliding_window if self.use_sliding_window else None

        # for backward compatibility
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention"
                if self.sliding_window is not None and i >= self.max_window_layers
                else "full_attention"
                for i in range(self.num_hidden_layers)
            ]

        super().__post_init__(**kwargs)

    def convert_rope_params_to_dict(self, **kwargs):
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or self.rope_parameters
        self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {}

        # Standardize and validate the correctness of rotary position embeddings parameters
        self.rope_parameters.setdefault("rope_theta", kwargs.pop("rope_theta", self.default_theta))
        if self.rope_parameters.get("rope_type", self.rope_parameters.get("type")) == "mrope":
            self.rope_parameters["rope_type"] = "default"
        self.standardize_rope_params()
        return kwargs


@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct")
@strict
class Qwen2_5_VLConfig(PreTrainedConfig):
    r"""
    Example:

    ```python
    >>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig

    >>> # Initializing a Qwen2_5_VL style configuration
    >>> configuration = Qwen2_5_VLConfig()

    >>> # Initializing a model from the Qwen2-VL-7B style configuration
    >>> model = Qwen2_5_VLForConditionalGeneration(configuration)

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

    model_type = "qwen2_5_vl"
    sub_configs = {"vision_config": Qwen2_5_VLVisionConfig, "text_config": Qwen2_5_VLTextConfig}
    keys_to_ignore_at_inference = ["past_key_values"]

    text_config: dict | PreTrainedConfig | None = None
    vision_config: dict | PreTrainedConfig | None = None
    image_token_id: int = 151655
    video_token_id: int = 151656
    vision_start_token_id: int = 151652
    vision_end_token_id: int = 151653
    tie_word_embeddings: bool = False

    def __post_init__(self, **kwargs):
        if isinstance(self.vision_config, dict):
            self.vision_config = self.sub_configs["vision_config"](**self.vision_config)
        elif self.vision_config is None:
            self.vision_config = self.sub_configs["vision_config"]()

        # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig`
        text_params = inspect.signature(self.sub_configs["text_config"].__init__).parameters.keys()
        text_params = list(text_params) + ["rope_parameters", "rope_scaling", "rope_theta"]
        text_kwargs = {key: kwargs.pop(key) for key in text_params if key in kwargs}

        if isinstance(self.text_config, dict):
            self.text_config = self.sub_configs["text_config"](**self.text_config)
        elif self.text_config is None:
            # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig`
            text_kwargs["dtype"] = kwargs.get("torch_dtype", kwargs.get("dtype"))  # don't pop the dtype
            self.text_config = self.sub_configs["text_config"](**text_kwargs)

        super().__post_init__(**kwargs)


__all__ = ["Qwen2_5_VLConfig", "Qwen2_5_VLVisionConfig", "Qwen2_5_VLTextConfig"]
