from typing import Optional

import torch
from torch import nn

from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PreTrainedConfig
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast
from ...modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring
from ...utils.generic import check_model_inputs
from ..mistral.configuration_mistral import MistralConfig
from ..qwen2.modeling_qwen2 import (
    Qwen2Attention,
    Qwen2DecoderLayer,
    Qwen2ForCausalLM,
    Qwen2ForQuestionAnswering,
    Qwen2ForSequenceClassification,
    Qwen2ForTokenClassification,
    Qwen2MLP,
    Qwen2Model,
    Qwen2PreTrainedModel,
    Qwen2RMSNorm,
    Qwen2RotaryEmbedding,
)


class MinistralConfig(MistralConfig, PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MinistralModel`]. It is used to instantiate an
    Ministral 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 Ministral-8B-Instruct-2410.

    [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)
    [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410)

    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 Ministral model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MinistralModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 14336):
            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 8):
            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 `8`.
        head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
            The attention head dimension.
        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 `4096*32`):
            The maximum sequence length that this model might ever be used with. Ministral's sliding window attention
            allows sequence of up to 4096*32 tokens.
        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*):
            The id of the padding token.
        bos_token_id (`int`, *optional*, defaults to 1):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the "end-of-sequence" token.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        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`.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention window size. If not specified, will default to `4096`.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        layer_types (`list`, *optional*):
            Attention pattern for each layer.

    ```python
    >>> from transformers import MinistralModel, MinistralConfig

    >>> # Initializing a Ministral 8B style configuration
    >>> configuration = MinistralConfig()

    >>> # Initializing a model from the Ministral 8B style configuration
    >>> model = MinistralModel(configuration)

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

    model_type = "ministral"

    def __init__(
        self,
        vocab_size: Optional[int] = 32000,
        hidden_size: Optional[int] = 4096,
        intermediate_size: Optional[int] = 14336,
        num_hidden_layers: Optional[int] = 32,
        num_attention_heads: Optional[int] = 32,
        num_key_value_heads: Optional[int] = 8,
        head_dim: Optional[int] = None,
        hidden_act: Optional[str] = "silu",
        max_position_embeddings: Optional[int] = 4096 * 32,
        initializer_range: Optional[float] = 0.02,
        rms_norm_eps: Optional[float] = 1e-6,
        use_cache: Optional[bool] = True,
        pad_token_id: Optional[int] = None,
        bos_token_id: Optional[int] = 1,
        eos_token_id: Optional[int] = 2,
        tie_word_embeddings: Optional[bool] = False,
        rope_parameters: Optional[RopeParameters] = None,
        sliding_window: Optional[int] = 4096,
        attention_dropout: Optional[float] = 0.0,
        layer_types: Optional[list[str]] = None,
        **kwargs,
    ):
        PreTrainedConfig.__init__(
            self,
            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,
        )
        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.sliding_window = sliding_window
        self.head_dim = head_dim

        # 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_dropout = attention_dropout
        self.layer_types = layer_types
        # 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

        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if self.sliding_window is not None else "full_attention"
            ] * num_hidden_layers

        # Validate the correctness of rotary position embeddings parameters
        rope_theta = getattr(self, "rope_theta", 10000.0)
        standardize_rope_params(self, rope_theta=rope_theta)
        rope_config_validation(self)


class MinistralMLP(Qwen2MLP):
    pass


class MinistralAttention(Qwen2Attention):
    def __init__(self, config, layer_idx: int):
        super().__init__(config, layer_idx)
        # Match Mistral: q/k/v do not have bias
        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)


class MinistralRMSNorm(Qwen2RMSNorm):
    pass


class MinistralDecoderLayer(Qwen2DecoderLayer):
    pass


class MinistralPreTrainedModel(Qwen2PreTrainedModel):
    pass


class MinistralRotaryEmbedding(Qwen2RotaryEmbedding):
    pass


class MinistralModel(Qwen2Model):
    def __init__(self, config: MinistralConfig):
        super().__init__(config)
        del self.has_sliding_layers

    @check_model_inputs()
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            # Prepare mask arguments
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values if use_cache else None,
        )


class MinistralForCausalLM(Qwen2ForCausalLM):
    pass


class MinistralForSequenceClassification(Qwen2ForSequenceClassification):
    pass


class MinistralForTokenClassification(Qwen2ForTokenClassification):
    pass


class MinistralForQuestionAnswering(Qwen2ForQuestionAnswering):
    pass


__all__ = [
    "MinistralConfig",
    "MinistralPreTrainedModel",
    "MinistralModel",
    "MinistralForCausalLM",
    "MinistralForSequenceClassification",
    "MinistralForTokenClassification",
    "MinistralForQuestionAnswering",
]
