# 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 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))


# needed for prefix-tuning of StarCoder models
def starcoder_model_postprocess_past_key_value(past_key_values):
    result = []
    for k in past_key_values:
        k = k[:, :, 0]
        k = k.permute([1, 2, 0, 3])
        k = k.reshape(*k.shape[:-2], -1)
        result.append(k)
    return tuple(result)


TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = {
    "bloom": bloom_model_postprocess_past_key_value,
    "gpt_bigcode": starcoder_model_postprocess_past_key_value,
}


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"],
    "gpt_bigcode": ["c_attn"],
    "mpt": ["Wqkv"],
    "RefinedWebModel": ["query_key_value"],
    "RefinedWeb": ["query_key_value"],
    "falcon": ["query_key_value"],
    "btlm": ["c_proj", "c_attn"],
    "codegen": ["qkv_proj"],
    "mistral": ["q_proj", "v_proj"],
    "stablelm": ["q_proj", "v_proj"],
    "phi": ["Wqkv", "out_proj", "fc1", "fc2"],
}

TRANSFORMERS_MODELS_TO_IA3_TARGET_MODULES_MAPPING = {
    "t5": ["k", "v", "wo"],
    "mt5": ["k", "v", "wi_1"],
    "gpt2": ["c_attn", "mlp.c_proj"],
    "bloom": ["query_key_value", "mlp.dense_4h_to_h"],
    "roberta": ["key", "value", "output.dense"],
    "opt": ["q_proj", "k_proj", "fc2"],
    "gptj": ["q_proj", "v_proj", "fc_out"],
    "gpt_neox": ["query_key_value", "dense_4h_to_h"],
    "gpt_neo": ["q_proj", "v_proj", "c_proj"],
    "bart": ["q_proj", "v_proj", "fc2"],
    "gpt_bigcode": ["c_attn", "mlp.c_proj"],
    "llama": ["k_proj", "v_proj", "down_proj"],
    "bert": ["key", "value", "output.dense"],
    "deberta-v2": ["key_proj", "value_proj", "output.dense"],
    "deberta": ["in_proj", "output.dense"],
    "RefinedWebModel": ["query_key_value", "dense_4h_to_h"],
    "RefinedWeb": ["query_key_value", "dense_4h_to_h"],
    "falcon": ["query_key_value", "dense_4h_to_h"],
}

TRANSFORMERS_MODELS_TO_IA3_FEEDFORWARD_MODULES_MAPPING = {
    "t5": ["wo"],
    "mt5": [],
    "gpt2": ["mlp.c_proj"],
    "bloom": ["mlp.dense_4h_to_h"],
    "roberta": ["output.dense"],
    "opt": ["fc2"],
    "gptj": ["fc_out"],
    "gpt_neox": ["dense_4h_to_h"],
    "gpt_neo": ["c_proj"],
    "bart": ["fc2"],
    "gpt_bigcode": ["mlp.c_proj"],
    "llama": ["down_proj"],
    "bert": ["output.dense"],
    "deberta-v2": ["output.dense"],
    "deberta": ["output.dense"],
    "RefinedWeb": ["dense_4h_to_h"],
    "RefinedWebModel": ["dense_4h_to_h"],
    "falcon": ["dense_4h_to_h"],
}

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"],
    "llama": ["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"],
    "gpt_bigcode": ["c_attn"],
    "deberta": ["in_proj"],
    # "layoutlm": ["query", "value"],
}

COMMON_LAYERS_PATTERN = ["layers", "h", "block", "blocks", "layer"]
WEIGHTS_NAME = "adapter_model.bin"
SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
CONFIG_NAME = "adapter_config.json"
EMBEDDING_LAYER_NAMES = ["embed_tokens", "lm_head"]
