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#           This file was automatically generated from src/transformers/models/glm_ocr/modular_glm_ocr.py.
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# Copyright 2026 the HuggingFace 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


@auto_docstring(checkpoint="zai-org/GLM-OCR")
@strict
class GlmOcrVisionConfig(PreTrainedConfig):
    r"""
    out_hidden_size (`int`, *optional*, defaults to 4096):
        The output hidden size of the vision model.

    Example:

    ```python
    >>> from transformers import GlmOcrVisionConfig, GlmOcrVisionModel

    >>> # Initializing a GlmOcrVisionConfig GLM-4.1V-9B style configuration
    >>> configuration = GlmOcrVisionConfig()

    >>> # Initializing a model (with random weights) from the GLM-4.1V-9B configuration
    >>> model = GlmOcrVisionModel(configuration)

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

    model_type = "glm_ocr_vision"
    base_config_key = "vision_config"

    depth: int = 24
    hidden_size: int = 1024
    hidden_act: str = "silu"
    attention_bias: bool = True
    attention_dropout: float | int = 0.0
    num_heads: int = 16
    in_channels: int = 3
    image_size: int | list[int] | tuple[int, int] = 336
    patch_size: int | list[int] | tuple[int, int] = 14
    rms_norm_eps: float = 1e-05
    spatial_merge_size: int = 2
    temporal_patch_size: int | list[int] | tuple[int, int] = 2
    out_hidden_size: int = 1536
    intermediate_size: int = 4096
    initializer_range: float = 0.02


@auto_docstring(checkpoint="zai-org/GLM-OCR")
@strict
class GlmOcrTextConfig(PreTrainedConfig):
    r"""
    Example:

    ```python
    >>> from transformers import GlmOcrTextModel, GlmOcrConfig

    >>> # Initializing a GLM-OCR style configuration
    >>> configuration = GlmOcrConfig()

    >>> # Initializing a model from the GLM-OCR style configuration
    >>> model = GlmOcrTextModel(configuration)

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

    model_type = "glm_ocr_text"
    base_config_key = "text_config"
    keys_to_ignore_at_inference = ["past_key_values"]
    # Default tensor parallel plan for base model `GlmOcr`
    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_up_proj": "colwise_gather_output",  # we need to replicate here due to the `chunk` operation
        "layers.*.mlp.down_proj": "rowwise_split_input",  # input is replicated due to the `chunk` operation
    }
    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 = 59392
    hidden_size: int = 1024
    intermediate_size: int = 4096
    num_hidden_layers: int = 16
    num_attention_heads: int = 16
    num_key_value_heads: int = 8
    hidden_act: str = "silu"
    max_position_embeddings: int = 131072
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-05
    use_cache: bool = True
    attention_dropout: float | int = 0.0
    rope_parameters: RopeParameters | dict | None = None
    pad_token_id: int | None = None

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

        super().__post_init__(**kwargs)


@auto_docstring(checkpoint="zai-org/GLM-OCR")
@strict
class GlmOcrConfig(PreTrainedConfig):
    r"""
    image_start_token_id (`int`, *optional*, defaults to 59256):
        The image start token index to encode the start of image.
    image_end_token_id (`int`, *optional*, defaults to 59257):
        The image end token index to encode the end of image.
    video_start_token_id (`int`, *optional*, defaults to 59258):
        The video start token index to encode the start of video.
    video_end_token_id (`int`, *optional*, defaults to 59259):
        The video end token index to encode the end of video.

    ```python
    >>> from transformers import GlmOcrForConditionalGeneration, GlmOcrConfig

    >>> # Initializing a GLM-OCR style configuration
    >>> configuration = GlmOcrConfig()

    >>> # Initializing a model from the GLM-OCR style configuration
    >>> model = GlmOcrForConditionalGeneration(configuration)

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

    model_type = "glm_ocr"
    sub_configs = {"vision_config": GlmOcrVisionConfig, "text_config": GlmOcrTextConfig}
    keys_to_ignore_at_inference = ["past_key_values"]

    text_config: dict | PreTrainedConfig | None = None
    vision_config: dict | PreTrainedConfig | None = None

    image_token_id: int = 59280
    video_token_id: int = 59281
    image_start_token_id: int = 59256
    image_end_token_id: int = 59257
    video_start_token_id: int = 59258
    video_end_token_id: int = 59259
    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"](**kwargs)

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

        super().__post_init__(**kwargs)


__all__ = ["GlmOcrConfig", "GlmOcrTextConfig", "GlmOcrVisionConfig"]
