# Copyright 2023-present the HuggingFace Inc. team.
#
# 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.

from typing import Dict, List

import torch.nn as nn

from peft.utils import _freeze_adapter, _get_submodules

from .config import AdaptionPromptConfig, prepare_config
from .layer import AdaptedAttention
from .utils import is_adaption_prompt_trainable


class AdaptionPromptModel(nn.Module):
    """
    Implements adaption prompts as described in https://arxiv.org/pdf/2303.16199.pdf.

    The top L attention modules are replaced with AdaptedAttention modules that wrap the original ones, but insert
    trainable prompts with gates (for zero init).

    Notes on the multi-adapter pattern:
    - We store the states of different adapters by keeping a dictionary of AdaptedAttention modules indexed by adapter
      name.
    - Every time we switch adapters, we remove the modules of the currently active adapter from the model, store them
      in the dictionary, and replace them with the modules of the new adapter.
    - To avoid duplicated and potentially inconsistent state, the currently active adapter is always removed from the
      dictionary.
    - Disabling the adapter would also result in the modules being removed from the model.
    """

    def __init__(self, model, configs: Dict, adapter_name: str):
        super().__init__()
        self.model = model
        # Store adapter configs by name.
        self.peft_config: Dict[str, AdaptionPromptConfig] = {}
        # Store lists of the parents of the affected attention modules by adapter name.
        # We keep references to the parents so we can swap the adapters in-and-out of the model.
        self._parents: Dict[str, List[nn.Module]] = {}
        # Store lists of cached AdaptedAttention modules by name.
        self._cached_adapters: Dict[str, List] = {}
        # The name of the currently active adapter.
        self._active_adapter = None
        # Whether the adapter is enabled.
        self._enabled = True
        self.forward = self.model.forward
        self.add_adapter(adapter_name, configs[adapter_name])
        self._mark_only_adaption_prompts_as_trainable(self.model)

    def add_adapter(self, adapter_name: str, config: AdaptionPromptConfig) -> None:
        """Add an adapter with the given name and config."""
        config = prepare_config(config, self.model)
        if adapter_name in self.peft_config:
            raise ValueError(f"Adapter with name '{adapter_name}' already exists.")

        parents = []
        for name, _ in self.model.named_modules():
            if name.endswith(config.target_modules):
                par, _, _ = _get_submodules(self.model, name)
                parents.append(par)
        if len(parents) < config.adapter_layers:
            raise ValueError(
                f"Config specifies more adapter layers '{config.adapter_layers}'"
                f" than the model has '{len(parents)}'."
            )
        # Note that if the target modules are not in Sequential, ModuleList, or
        # some other PyTorch ordered container, the behavior is undefined as we
        # assume here that the order of the modules is the same as the order of
        # the transformer decoder layers.
        parents = parents[-config.adapter_layers :]
        self._parents[adapter_name] = parents

        # It is only None during initialization.
        # If it is disabled, we don't have to remove the modules.
        if self._active_adapter is not None and self._enabled:
            self._remove_adapted_attentions(self._active_adapter)
        self._active_adapter = adapter_name
        self.peft_config[adapter_name] = config
        self._create_adapted_attentions(config, parents)
        if not self._enabled:
            self._remove_adapted_attentions(self._active_adapter)

        if config.inference_mode:
            _freeze_adapter(self.model, adapter_name)

    def set_adapter(self, adapter_name: str) -> None:
        """Set the model to use the adapter with the given name."""
        if self._active_adapter == adapter_name:
            return
        if adapter_name not in self.peft_config:
            raise ValueError(f"Adapter with name '{adapter_name}' does not exist.")

        if self._enabled:
            self._remove_adapted_attentions(self._active_adapter)
            self._set_adapted_attentions(adapter_name)

        self._active_adapter = adapter_name

    def enable_adapter_layers(self):
        """Enable adapter layers by swapping in cached AdaptedAttention modules."""
        self._enabled = True
        self._set_adapted_attentions(self._active_adapter)

    def disable_adapter_layers(self):
        """Disable adapter layers by swapping out AdaptedAttention modules."""
        self._enabled = False
        self._remove_adapted_attentions(self._active_adapter)

    def _create_adapted_attentions(self, config: AdaptionPromptConfig, parents: List[nn.Module]) -> None:
        """Wrap LlamaAttention modules with newly created AdaptedAttention modules."""
        for par in parents:
            attn = AdaptedAttention(
                model_type=self.model.config.model_type,
                adapter_len=config.adapter_len,
                model=getattr(par, config.target_modules),
            )
            setattr(par, config.target_modules, attn)

    def _set_adapted_attentions(self, adapter_name: str) -> None:
        """Replace LlamaAttention modules with cached AdaptedAttention modules."""
        cached = self._cached_adapters[adapter_name]
        del self._cached_adapters[adapter_name]
        config = self.peft_config[adapter_name]
        for i, par in enumerate(self._parents[adapter_name]):
            setattr(par, config.target_modules, cached[i])

    def _remove_adapted_attentions(self, adapter_name: str) -> None:
        """Remove AdaptedAttention modules from the model and store them in the cache."""
        config = self.peft_config[adapter_name]
        adapted_attentions = []
        for par in self._parents[adapter_name]:
            attn = getattr(par, config.target_modules)
            adapted_attentions.append(attn)
            setattr(par, config.target_modules, attn.model)
        self._cached_adapters[adapter_name] = adapted_attentions

    def _mark_only_adaption_prompts_as_trainable(self, model: nn.Module) -> None:
        """Freeze all parameters of the model except the adaption prompts."""
        for n, p in model.named_parameters():
            if not is_adaption_prompt_trainable(n):
                p.requires_grad = False

    def __getattr__(self, name: str):
        """Forward missing attributes to the wrapped module."""
        try:
            return super().__getattr__(name)  # defer to nn.Module's logic
        except AttributeError:
            # This is necessary as e.g. causal models have various methods that we
            # don't want to re-implement here.
            if name == "model":  # see #1892: prevent infinite recursion if class is not initialized
                raise
            return getattr(self.model, name)
