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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import torch

from ...cache_utils import Cache, DynamicCache
from ...configuration_utils import PreTrainedConfig
from ...masking_utils import create_causal_mask
from ...modeling_outputs import MoeModelOutputWithPast
from ...modeling_rope_utils import RopeParameters
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring
from ...utils.generic import merge_with_config_defaults
from ...utils.output_capturing import OutputRecorder, capture_outputs
from ..mixtral.modeling_mixtral import MixtralModel, MixtralPreTrainedModel
from ..olmo2.modeling_olmo2 import Olmo2Attention, Olmo2RMSNorm, Olmo2RotaryEmbedding
from ..olmoe.modeling_olmoe import (
    OlmoeDecoderLayer,
    OlmoeForCausalLM,
    OlmoeMLP,
    OlmoeSparseMoeBlock,
    OlmoeTopKRouter,
)


class FlexOlmoConfig(PreTrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`FlexOlmoModel`]. It is used to instantiate an FlexOlmo
    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 [allenai/FlexOlmo-7x7B-1T](https://huggingface.co/allenai/FlexOlmo-7x7B-1T).

    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 100352):
            Vocabulary size of the FlexOlmo model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`FlexOlmoModel`]
        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 4096):
            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*, defaults to 100277):
            Padding token id.
        bos_token_id (`int`, *optional*):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 100257):
            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`, defaults to `False`, *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.
        num_experts_per_tok (`int`, *optional*, defaults to 5):
            Number of selected experts.
        num_experts (`int`, *optional*, defaults to 7):
            Number of routed experts.
        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, including load balancing loss and router z-loss.
        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
            The aux loss factor for the total loss.
        norm_topk_prob (`bool`, *optional*, defaults to `False`):
            Whether to normalize the topk probabilities.

    ```python
    >>> from transformers import FlexOlmoModel, FlexOlmoConfig

    >>> # Initializing a FlexOlmo style configuration
    >>> configuration = FlexOlmoConfig()

    >>> # Initializing a model from the FlexOlmo style configuration
    >>> model = FlexOlmoModel(configuration)

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

    model_type = "flex_olmo"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_local_experts": "num_experts"}
    default_theta = 500000.0
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise_gather_output",  # we need to replicate here due to the added norm on q and k
        "layers.*.self_attn.k_proj": "colwise_gather_output",  # we need to replicate here due to the added norm on q and k
        "layers.*.self_attn.v_proj": "colwise_gather_output",  # we need to replicate here due to the added norm on q and k
        "layers.*.self_attn.o_proj": "rowwise_split_input",  # input is replicated due to the added norm on q and k
        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
        "layers.*.mlp.experts.down_proj": "rowwise",
        "layers.*.mlp.experts": "moe_tp_experts",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    def __init__(
        self,
        vocab_size: int | None = 100352,
        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 = 4096,
        initializer_range: float | None = 0.02,
        rms_norm_eps: float | None = 1e-06,
        use_cache: bool | None = True,
        pad_token_id: int | None = 100277,
        bos_token_id: int | None = None,
        eos_token_id: int | None = 100257,
        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,
        num_experts_per_tok: int | None = 5,
        num_experts: int | None = 7,
        output_router_logits: bool | None = False,
        router_aux_loss_coef: float | None = 0.01,
        norm_topk_prob: bool | None = False,
        **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.num_experts_per_tok = num_experts_per_tok
        self.num_experts = num_experts
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.norm_topk_prob = norm_topk_prob
        self.rope_parameters = rope_parameters

        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
        super().__init__(**kwargs)


# FlexOlmo RMS norm reuses Olmo2 RMS norm, which handles low precision slightly differently than the original Olmoe.
class FlexOlmoRMSNorm(Olmo2RMSNorm):
    pass


# FlexOlmo RMS norm reuses Olmo2 RMS norm, so that the output cos and sin are returned
# as float32 rather than the input type.
class FlexOlmoRotaryEmbedding(Olmo2RotaryEmbedding):
    pass


class FlexOlmoMLP(OlmoeMLP):
    pass


# FlexOlmo uses Olmo2 attention instead of OlmoE Attention since its `apply_rotary_pos_emb`
# implementation handles lower precision more faithfully to the Olmo codebase.
class FlexOlmoAttention(Olmo2Attention):
    pass


class FlexOlmoTopKRouter(OlmoeTopKRouter):
    pass


class FlexOlmoSparseMoeBlock(OlmoeSparseMoeBlock):
    pass


# FlexOlmo decoder layer is identical to OlmoE decoder layer except:
# - Norm is applied after attention/feedforward rather than before.
class FlexOlmoDecoderLayer(OlmoeDecoderLayer):
    def __init__(self, config: FlexOlmoConfig, layer_idx: int):
        super().__init__(config, layer_idx=layer_idx)
        self.post_attention_layernorm = FlexOlmoRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = FlexOlmoRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.self_attn = FlexOlmoAttention(config=config, layer_idx=layer_idx)
        del self.input_layernorm

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs,
    ) -> torch.FloatTensor:
        residual = hidden_states

        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


# FlexOlmo uses Mixtral model as its base instead of OlmoE model since Mixtral is more up-to-date with the rest
# of the transformers library. For example, it uses the newer mechanisms of recording submodule outputs.
class FlexOlmoPreTrainedModel(MixtralPreTrainedModel):
    _can_record_outputs = {
        "router_logits": OutputRecorder(FlexOlmoTopKRouter, index=0),
        "hidden_states": FlexOlmoDecoderLayer,
        "attentions": FlexOlmoAttention,
    }


# FlexOlmo uses Mixtral model as its base instead of OlmoE model since Mixtral is more up-to-date with the rest
# of the transformers library. For example, it uses the newer mechanisms of recording submodule outputs.
# FlexOlmo model is identical to Mixtral model except:
# - FlexOlmo does not use sliding window attention.
class FlexOlmoModel(MixtralModel):
    @merge_with_config_defaults
    @capture_outputs
    @auto_docstring
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

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

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

        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)

        causal_mask = create_causal_mask(
            config=self.config,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            past_key_values=past_key_values,
            position_ids=position_ids,
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        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,
                position_embeddings=position_embeddings,
                attention_mask=causal_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)

        return MoeModelOutputWithPast(  # only diff with Mistral is the output type, we need MoE
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


class FlexOlmoForCausalLM(OlmoeForCausalLM):
    pass


__all__ = [
    "FlexOlmoConfig",
    "FlexOlmoForCausalLM",
    "FlexOlmoModel",
    "FlexOlmoPreTrainedModel",
]