# coding=utf-8
# Copyright 2023 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.
"""Open-Llama model configuration"""

from ....configuration_utils import PreTrainedConfig
from ....utils import logging


logger = logging.get_logger(__name__)


class OpenLlamaConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`OpenLlamaModel`]. It is used to instantiate an
    Open-Llama 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
    [s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1).

    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 Open-Llama model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`OpenLlamaModel`]
        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 encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        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. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        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-12):
            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`.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_parameters (`RopeParameters`, *optional*):
            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty 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`.

        Example:

    ```python
    >>> from transformers import OpenLlamaModel, OpenLlamaConfig

    >>> # Initializing a Open-Llama open_llama-7b style configuration
    >>> configuration = OpenLlamaConfig()

    >>> # Initializing a model from the open_llama-7b style configuration
    >>> model = OpenLlamaModel(configuration)

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

    model_type = "open-llama"

    def __init__(
        self,
        vocab_size=100000,
        hidden_size=4096,
        intermediate_size=11008,
        num_hidden_layers=32,
        num_attention_heads=32,
        hidden_act="silu",
        max_position_embeddings=2048,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        use_memory_efficient_attention=True,
        hidden_dropout_prob=0.1,
        attention_dropout_prob=0.1,
        use_stable_embedding=True,
        shared_input_output_embedding=True,
        rope_theta=10000.0,
        rope_parameters=None,
        **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
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.use_memory_efficient_attention = kwargs.pop(
            "use_memorry_efficient_attention", use_memory_efficient_attention
        )
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_dropout_prob = attention_dropout_prob
        self.use_stable_embedding = use_stable_embedding
        self.shared_input_output_embedding = shared_input_output_embedding
        self.rope_theta = rope_theta
        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
        rope_scaling = kwargs.pop("rope_scaling", None)
        self.rope_parameters = rope_scaling or rope_parameters
        self._rope_parameters_validation()

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_parameters_validation(self):
        """
        Validate the `rope_parameters` configuration.
        """
        if self.rope_parameters is None:
            return

        if not isinstance(self.rope_parameters, dict) or len(self.rope_parameters) != 2:
            raise ValueError(
                f"`rope_parameters` must be a dictionary with two fields, `type` and `factor`, got {self.rope_parameters}"
            )
        rope_parameters_type = self.rope_parameters.get("type", None)
        rope_parameters_factor = self.rope_parameters.get("factor", None)
        if rope_parameters_type is None or rope_parameters_type not in ["linear", "dynamic"]:
            raise ValueError(
                f"`rope_parameters`'s type field must be one of ['linear', 'dynamic'], got {rope_parameters_type}"
            )
        if (
            rope_parameters_factor is None
            or not isinstance(rope_parameters_factor, float)
            or rope_parameters_factor <= 1.0
        ):
            raise ValueError(f"`rope_parameters`'s factor field must be a float > 1, got {rope_parameters_factor}")


__all__ = ["OpenLlamaConfig"]
