# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2024 The Qwen team, Alibaba Group 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.
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"""Qwen2 model configuration"""

from ..configuration_utils import PretrainedConfig


__all__ = [
    "QWEN2_PRETRAINED_INIT_CONFIGURATION",
    "Qwen2Config",
    "QWEN2_PRETRAINED_RESOURCE_FILES_MAP",
]
QWEN2_PRETRAINED_INIT_CONFIGURATION = {
    # Hypothetical model weights (tiny-random-llama & micro-random-llama) for test only
    "__internal_testing__/micro-random-llama": {
        "architectures": ["LlamaForCausalLM"],
        "hidden_size": 64,
        "initializer_range": 0.02,
        "intermediate_size": 1000,
        "max_position_embeddings": 2048,
        "model_type": "llama",
        "num_attention_heads": 8,
        "num_hidden_layers": 1,
        "rms_norm_eps": 1e-06,
        "vocab_size": 32000,
        "bos_token_id": 1,
        "eos_token_id": 2,
        "pad_token_id": 0,
    },
    "__internal_testing__/tiny-random-llama": {
        "architectures": ["LlamaForCausalLM"],
        "hidden_size": 768,
        "initializer_range": 0.02,
        "intermediate_size": 11008,
        "max_position_embeddings": 2048,
        "model_type": "llama",
        "num_attention_heads": 8,
        "num_hidden_layers": 2,
        "rms_norm_eps": 1e-06,
        "vocab_size": 32000,
        "bos_token_id": 1,
        "eos_token_id": 2,
        "pad_token_id": 0,
    },
}

# Hypothetical model weights (tiny-random-llama) for test only
QWEN2_PRETRAINED_RESOURCE_FILES_MAP = {
    "model_state": {
        "__internal_testing__/micro-random-llama": "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/micro-random-llama/model_state.pdparams",
        "__internal_testing__/tiny-random-llama": "https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-llama/model_state.pdparams",
    },
}

class Qwen2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of
    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).

    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 151936):
            Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen2Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 22016):
            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.
        num_key_value_heads (`int`, *optional*, defaults to 32):
            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 checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
        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 32768):
            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`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        use_sliding_window (`bool`, *optional*, defaults to `False`):
            Whether to use sliding window attention.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
        max_window_layers (`int`, *optional*, defaults to 28):
            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    ```python
    >>> from transformers import Qwen2Model, Qwen2Config

    >>> # Initializing a Qwen2 style configuration
    >>> configuration = Qwen2Config()

    >>> # Initializing a model from the Qwen2-7B style configuration
    >>> model = Qwen2Model(configuration)

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

    model_type = "qwen2"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=151936,
        hidden_size=4096,
        intermediate_size=22016,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=32,
        hidden_act="silu",
        max_position_embeddings=32768,
        # seq_length=32768,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        pad_token_id=151643,
        bos_token_id=151643,
        eos_token_id=151643,
        use_sliding_window=False,
        sliding_window=4096,
        max_window_layers=28,
        use_flash_attention_for_generation=False,
        alibi=False,
        use_last_token_for_generation=False,
        attention_bias=True,
        attention_dropout=0.0,
        rope_scaling_factor=1.0,
        rope_scaling_type=None,
        dpo_config=None,
        use_fused_head_and_loss_fn=False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        # self.seq_length = seq_length
        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.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window
        self.max_window_layers = max_window_layers

        # 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.rope_theta = rope_theta
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout

        self.use_cache = use_cache
        self.rope_scaling_factor = rope_scaling_factor
        self.rope_scaling_type = rope_scaling_type

        self.pad_token_id = pad_token_id
        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.dpo_config = dpo_config
        self.use_flash_attention_for_generation = use_flash_attention_for_generation
        self.alibi = alibi
        self.use_fused_head_and_loss_fn = use_fused_head_and_loss_fn
        self.use_last_token_for_generation = use_last_token_for_generation

        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,
        )