# Copyright 2024 EleutherAI 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.
"""GraniteMoeShared model configuration"""

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


logger = logging.get_logger(__name__)


class GraniteMoeSharedConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GraniteMoeSharedModel`]. It is used to instantiate an GraniteMoeShared
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the [ibm-research/moe-7b-1b-active-shared-experts](https://huggingface.co/ibm-research/moe-7b-1b-active-shared-experts).

    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PreTrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 32000):
            Vocabulary size of the GraniteMoeShared model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GraniteMoeSharedModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
            `num_attention_heads`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 2048):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 1):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 2):
            End of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
            with longer `max_position_embeddings`.
        attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        embedding_multiplier (`float`, *optional*, defaults to 1.0): embedding multiplier
        logits_scaling (`float`, *optional*, defaults to 1.0): divisor for output logits
        residual_multiplier (`float`, *optional*, defaults to 1.0): residual multiplier
        attention_multiplier (`float`, *optional*, defaults to 1.0): attention multiplier
        num_local_experts (`int`, *optional*, defaults to 8): total number of experts
        num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabling this will also
            allow the model to output the auxiliary loss.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxiliary loss coefficient
        shared_intermediate_size (`int`, *optional*, defaults to 0): intermediate size for shared experts. 0 implies
            no shared experts.

    ```python
    >>> from transformers import GraniteMoeSharedModel, GraniteMoeSharedConfig

    >>> # Initializing a GraniteMoeShared granitemoe-3b style configuration
    >>> configuration = GraniteMoeSharedConfig()

    >>> # Initializing a model from the granitemoe-7b style configuration
    >>> model = GraniteMoeSharedModel(configuration)

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

    model_type = "granitemoeshared"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size: int | None = 32000,
        hidden_size: int | None = 4096,
        intermediate_size: int | None = 11008,
        num_hidden_layers: int | None = 32,
        num_attention_heads: int | None = 32,
        num_key_value_heads: int | None = None,
        hidden_act: str | None = "silu",
        max_position_embeddings: int | None = 2048,
        initializer_range: float | None = 0.02,
        rms_norm_eps: int | None = 1e-6,
        use_cache: bool | None = True,
        pad_token_id: int | None = None,
        bos_token_id: int | None = 1,
        eos_token_id: int | None = 2,
        tie_word_embeddings: bool | None = False,
        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
        attention_bias: bool | None = False,
        attention_dropout: float | None = 0.0,
        embedding_multiplier: float | None = 1.0,
        logits_scaling: float | None = 1.0,
        residual_multiplier: float | None = 1.0,
        attention_multiplier: float | None = 1.0,
        num_local_experts: int | None = 8,
        num_experts_per_tok: int | None = 2,
        output_router_logits: bool | None = False,
        router_aux_loss_coef: float | None = 0.001,
        shared_intermediate_size: int | None = 0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads

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

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout

        self.embedding_multiplier = embedding_multiplier
        self.logits_scaling = logits_scaling
        self.residual_multiplier = residual_multiplier
        self.attention_multiplier = attention_multiplier

        self.num_local_experts = num_local_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.shared_intermediate_size = shared_intermediate_size

        self.tie_word_embeddings = tie_word_embeddings
        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id

        # this model has rope embedding type, hardcoded for BC
        self.position_embedding_type = "rope"
        self.rope_parameters = rope_parameters

        super().__init__(**kwargs)


__all__ = ["GraniteMoeSharedConfig"]
