# coding=utf-8
# 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.

import copy

import torch


# needed for prefix-tuning of bloom model
def bloom_model_postprocess_past_key_value(past_key_values):
    past_key_values = torch.cat(past_key_values)
    total_layers, batch_size, num_attention_heads, num_virtual_tokens, head_dim = past_key_values.shape
    keys = past_key_values[: total_layers // 2]
    keys = keys.transpose(2, 3).reshape(
        total_layers // 2, batch_size * num_attention_heads, head_dim, num_virtual_tokens
    )
    values = past_key_values[total_layers // 2 :]
    values = values.reshape(total_layers // 2, batch_size * num_attention_heads, num_virtual_tokens, head_dim)

    return tuple(zip(keys, values))


def prepare_model_for_int8_training(model, use_gradient_checkpointing=True):
    r"""
    This method wraps the entire protocol for preparing a model before running a training. This includes:
        1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm
        head to fp32

    Args:
        model, (`transformers.PreTrainedModel`):
            The loaded model from `transformers`
    """
    loaded_in_8bit = getattr(model, "is_loaded_in_8bit", False)

    for name, param in model.named_parameters():
        # freeze base model's layers
        param.requires_grad = False

    # cast all non INT8 parameters to fp32
    for param in model.parameters():
        if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
            param.data = param.data.to(torch.float32)

    if loaded_in_8bit and use_gradient_checkpointing:
        # For backward compatibility
        if hasattr(model, "enable_input_require_grads"):
            model.enable_input_require_grads()
        else:

            def make_inputs_require_grad(module, input, output):
                output.requires_grad_(True)

            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

        # enable gradient checkpointing for memory efficiency
        model.gradient_checkpointing_enable()

    return model


# copied from transformers.models.bart.modeling_bart
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    """
    Shift input ids one token to the right.

    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids
        pad_token_id (`int`): The id of the `padding` token.
        decoder_start_token_id (`int`): The id of the `start` token.
    """
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
    shifted_input_ids[:, 0] = decoder_start_token_id

    if pad_token_id is None:
        raise ValueError("self.model.config.pad_token_id has to be defined.")
    # replace possible -100 values in labels by `pad_token_id`
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

    return shifted_input_ids


class ModulesToSaveWrapper(torch.nn.Module):
    def __init__(self, module_to_save, adapter_name):
        super().__init__()
        self.original_module = module_to_save
        self.modules_to_save = torch.nn.ModuleDict({})
        self.update(adapter_name)
        self.active_adapter = adapter_name

    def update(self, adapter_name):
        self.modules_to_save.update(torch.nn.ModuleDict({adapter_name: copy.deepcopy(self.original_module)}))

    def forward(self, *args, **kwargs):
        if self.active_adapter not in self.modules_to_save:
            return self.original_module(*args, **kwargs)
        return self.modules_to_save[self.active_adapter](*args, **kwargs)


def _get_submodules(model, key):
    parent = model.get_submodule(".".join(key.split(".")[:-1]))
    target_name = key.split(".")[-1]
    target = model.get_submodule(key)
    return parent, target, target_name


def _freeze_adapter(model, adapter_name):
    for n, p in model.named_parameters():
        if adapter_name in n:
            p.requires_grad = False


def _set_trainable(model, adapter_name):
    key_list = [key for key, _ in model.named_modules()]
    for key in key_list:
        target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save)
        if target_module_found:
            parent, target, target_name = _get_submodules(model, key)
            if isinstance(target, ModulesToSaveWrapper):
                target.update(adapter_name)
            else:
                for param in target.parameters():
                    param.requires_grad = True
                setattr(parent, target_name, ModulesToSaveWrapper(target, adapter_name))


def _set_adapter(model, adapter_name):
    for module in model.modules():
        if isinstance(module, ModulesToSaveWrapper):
            module.active_adapter = adapter_name


def fsdp_auto_wrap_policy(model):
    import functools
    import os

    from accelerate import FullyShardedDataParallelPlugin
    from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy

    from ..tuners import PrefixEncoder, PromptEmbedding, PromptEncoder

    def lambda_policy_fn(module):
        if (
            len(list(module.named_children())) == 0
            and getattr(module, "weight", None) is not None
            and module.weight.requires_grad
        ):
            return True
        return False

    lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn)
    transformer_wrap_policy = functools.partial(
        transformer_auto_wrap_policy,
        transformer_layer_cls=(
            PrefixEncoder,
            PromptEncoder,
            PromptEmbedding,
            FullyShardedDataParallelPlugin.get_module_class_from_name(
                model, os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "")
            ),
        ),
    )

    auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy])
    return auto_wrap_policy


def transpose(weight, fan_in_fan_out):
    return weight.T if fan_in_fan_out else weight


TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = {
    "t5": ["q", "v"],
    "mt5": ["q", "v"],
    "bart": ["q_proj", "v_proj"],
    "gpt2": ["c_attn"],
    "bloom": ["query_key_value"],
    "blip-2": ["q", "v", "q_proj", "v_proj"],
    "opt": ["q_proj", "v_proj"],
    "gptj": ["q_proj", "v_proj"],
    "gpt_neox": ["query_key_value"],
    "gpt_neo": ["q_proj", "v_proj"],
    "bert": ["query", "value"],
    "roberta": ["query", "value"],
    "xlm-roberta": ["query", "value"],
    "electra": ["query", "value"],
    "deberta-v2": ["query_proj", "value_proj"],
    "deberta": ["in_proj"],
    "layoutlm": ["query", "value"],
    "llama": ["q_proj", "v_proj"],
    "chatglm": ["query_key_value"],
}

TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = {
    "t5": ["q", "k", "v", "o", "wi", "wo"],
    "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"],
    "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
    # "gpt2": ["c_attn"],
    # "bloom": ["query_key_value"],
    "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
    # "gptj": ["q_proj", "v_proj"],
    # "gpt_neox": ["query_key_value"],
    # "gpt_neo": ["q_proj", "v_proj"],
    # "bert": ["query", "value"],
    "roberta": ["query", "key", "value", "dense"],
    # "xlm-roberta": ["query", "value"],
    # "electra": ["query", "value"],
    "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"],
    # "deberta": ["in_proj"],
    # "layoutlm": ["query", "value"],
}

TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = {
    "bloom": bloom_model_postprocess_past_key_value,
}

WEIGHTS_NAME = "adapter_model.bin"
CONFIG_NAME = "adapter_config.json"
