# 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 __future__ import annotations

import importlib
import os
from typing import Optional

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForQuestionAnswering,
    AutoModelForSeq2SeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoTokenizer,
)

from .config import PeftConfig
from .peft_model import (
    PeftModel,
    PeftModelForCausalLM,
    PeftModelForFeatureExtraction,
    PeftModelForQuestionAnswering,
    PeftModelForSeq2SeqLM,
    PeftModelForSequenceClassification,
    PeftModelForTokenClassification,
)
from .utils.constants import TOKENIZER_CONFIG_NAME
from .utils.other import check_file_exists_on_hf_hub


MODEL_TYPE_TO_PEFT_MODEL_MAPPING: dict[str, type[PeftModel]] = {
    "SEQ_CLS": PeftModelForSequenceClassification,
    "SEQ_2_SEQ_LM": PeftModelForSeq2SeqLM,
    "CAUSAL_LM": PeftModelForCausalLM,
    "TOKEN_CLS": PeftModelForTokenClassification,
    "QUESTION_ANS": PeftModelForQuestionAnswering,
    "FEATURE_EXTRACTION": PeftModelForFeatureExtraction,
}


class _BaseAutoPeftModel:
    _target_class = None
    _target_peft_class = None

    def __init__(self, *args, **kwargs):
        # For consistency with transformers: https://github.com/huggingface/transformers/blob/91d7df58b6537d385e90578dac40204cb550f706/src/transformers/models/auto/auto_factory.py#L400
        raise EnvironmentError(  # noqa: UP024
            f"{self.__class__.__name__} is designed to be instantiated "
            f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
            f"`{self.__class__.__name__}.from_config(config)` methods."
        )

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path,
        adapter_name: str = "default",
        is_trainable: bool = False,
        config: Optional[PeftConfig] = None,
        revision: Optional[str] = None,
        **kwargs,
    ):
        r"""
        A wrapper around all the preprocessing steps a user needs to perform in order to load a PEFT model. The kwargs
        are passed along to `PeftConfig` that automatically takes care of filtering the kwargs of the Hub methods and
        the config object init.
        """
        peft_config = PeftConfig.from_pretrained(pretrained_model_name_or_path, revision=revision, **kwargs)
        base_model_path = peft_config.base_model_name_or_path
        base_model_revision = peft_config.revision

        task_type = getattr(peft_config, "task_type", None)

        if cls._target_class is not None:
            target_class = cls._target_class
        elif cls._target_class is None and task_type is not None:
            # this is only in the case where we use `AutoPeftModel`
            raise ValueError(
                "Cannot use `AutoPeftModel` with a task type, please use a specific class for your task type. (e.g. `AutoPeftModelForCausalLM` for `task_type='CAUSAL_LM'`)"
            )

        if task_type is not None:
            expected_target_class = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[task_type]
            if cls._target_peft_class.__name__ != expected_target_class.__name__:
                raise ValueError(
                    f"Expected target PEFT class: {expected_target_class.__name__}, but you have asked for: {cls._target_peft_class.__name__}"
                    " make sure that you are loading the correct model for your task type."
                )
        elif task_type is None and getattr(peft_config, "auto_mapping", None) is not None:
            auto_mapping = getattr(peft_config, "auto_mapping", None)
            base_model_class = auto_mapping["base_model_class"]
            parent_library_name = auto_mapping["parent_library"]

            parent_library = importlib.import_module(parent_library_name)
            target_class = getattr(parent_library, base_model_class)
        else:
            raise ValueError(
                "Cannot infer the auto class from the config, please make sure that you are loading the correct model for your task type."
            )

        base_model = target_class.from_pretrained(base_model_path, revision=base_model_revision, **kwargs)

        tokenizer_exists = False
        if os.path.exists(os.path.join(pretrained_model_name_or_path, TOKENIZER_CONFIG_NAME)):
            tokenizer_exists = True
        else:
            token = kwargs.get("token", None)
            if token is None:
                token = kwargs.get("use_auth_token", None)

            tokenizer_exists = check_file_exists_on_hf_hub(
                repo_id=pretrained_model_name_or_path,
                filename=TOKENIZER_CONFIG_NAME,
                revision=revision,
                repo_type=kwargs.get("repo_type", None),
                token=token,
            )

        if tokenizer_exists and hasattr(base_model, "get_input_embeddings"):
            tokenizer = AutoTokenizer.from_pretrained(
                pretrained_model_name_or_path, trust_remote_code=kwargs.get("trust_remote_code", False)
            )
            embedding_size = base_model.get_input_embeddings().weight.shape[0]
            if len(tokenizer) > embedding_size:
                # only resize if the tokenizer has a larger vocab size than there are embeddings
                base_model.resize_token_embeddings(len(tokenizer))

        return cls._target_peft_class.from_pretrained(
            base_model,
            pretrained_model_name_or_path,
            adapter_name=adapter_name,
            is_trainable=is_trainable,
            config=config,
            **kwargs,
        )


class AutoPeftModel(_BaseAutoPeftModel):
    _target_class = None
    _target_peft_class = PeftModel


class AutoPeftModelForCausalLM(_BaseAutoPeftModel):
    _target_class = AutoModelForCausalLM
    _target_peft_class = PeftModelForCausalLM


class AutoPeftModelForSeq2SeqLM(_BaseAutoPeftModel):
    _target_class = AutoModelForSeq2SeqLM
    _target_peft_class = PeftModelForSeq2SeqLM


class AutoPeftModelForSequenceClassification(_BaseAutoPeftModel):
    _target_class = AutoModelForSequenceClassification
    _target_peft_class = PeftModelForSequenceClassification


class AutoPeftModelForTokenClassification(_BaseAutoPeftModel):
    _target_class = AutoModelForTokenClassification
    _target_peft_class = PeftModelForTokenClassification


class AutoPeftModelForQuestionAnswering(_BaseAutoPeftModel):
    _target_class = AutoModelForQuestionAnswering
    _target_peft_class = PeftModelForQuestionAnswering


class AutoPeftModelForFeatureExtraction(_BaseAutoPeftModel):
    _target_class = AutoModel
    _target_peft_class = PeftModelForFeatureExtraction