# coding=utf-8
# Copyright 2018 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.
""" Convert pytorch checkpoints to TensorFlow """


import argparse
import logging
import os

from transformers import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    ElectraConfig,
    FlaubertConfig,
    GPT2Config,
    OpenAIGPTConfig,
    RobertaConfig,
    T5Config,
    TFAlbertForPreTraining,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPT2LMHeadModel,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFT5ForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    cached_path,
    is_torch_available,
    load_pytorch_checkpoint_in_tf2_model,
)


if is_torch_available():
    import torch
    import numpy as np
    from transformers import (
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        GPT2LMHeadModel,
        GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLMRobertaForMaskedLM,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        CamembertForMaskedLM,
        CamembertForSequenceClassification,
        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        FlaubertWithLMHeadModel,
        DistilBertForMaskedLM,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        T5ForConditionalGeneration,
        T5_PRETRAINED_MODEL_ARCHIVE_MAP,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
    )
else:
    (
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        GPT2LMHeadModel,
        GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLMRobertaForMaskedLM,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
        RobertaForMaskedLM,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        CamembertForMaskedLM,
        CamembertForSequenceClassification,
        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        FlaubertWithLMHeadModel,
        DistilBertForMaskedLM,
        DistilBertForSequenceClassification,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        T5ForConditionalGeneration,
        T5_PRETRAINED_MODEL_ARCHIVE_MAP,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
    ) = (
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
        None,
    )


logging.basicConfig(level=logging.INFO)

MODEL_CLASSES = {
    "bert": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-base-cased-finetuned-mrpc": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "gpt2": (
        GPT2Config,
        TFGPT2LMHeadModel,
        GPT2LMHeadModel,
        GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlnet": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm-roberta": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "openai-gpt": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta": (
        RobertaConfig,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta-large-mnli": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "camembert": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "flaubert": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert-base-distilled-squad": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "albert": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "t5": (
        T5Config,
        TFT5ForConditionalGeneration,
        T5ForConditionalGeneration,
        T5_PRETRAINED_MODEL_ARCHIVE_MAP,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "electra": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def convert_pt_checkpoint_to_tf(
    model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True
):
    if model_type not in MODEL_CLASSES:
        raise ValueError("Unrecognized model type, should be one of {}.".format(list(MODEL_CLASSES.keys())))

    config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        config_file = cached_path(aws_config_map[config_file], force_download=not use_cached_models)
    config = config_class.from_json_file(config_file)
    config.output_hidden_states = True
    config.output_attentions = True
    print("Building TensorFlow model from configuration: {}".format(str(config)))
    tf_model = model_class(config)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_model_maps:
        pytorch_checkpoint_path = cached_path(
            aws_model_maps[pytorch_checkpoint_path], force_download=not use_cached_models
        )
    # Load PyTorch checkpoint in tf2 model:
    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)

    if compare_with_pt_model:
        tfo = tf_model(tf_model.dummy_inputs, training=False)  # build the network

        state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu")
        pt_model = pt_model_class.from_pretrained(
            pretrained_model_name_or_path=None, config=config, state_dict=state_dict
        )

        with torch.no_grad():
            pto = pt_model(**pt_model.dummy_inputs)

        np_pt = pto[0].numpy()
        np_tf = tfo[0].numpy()
        diff = np.amax(np.abs(np_pt - np_tf))
        print("Max absolute difference between models outputs {}".format(diff))
        assert diff <= 2e-2, "Error, model absolute difference is >2e-2: {}".format(diff)

    # Save pytorch-model
    print("Save TensorFlow model to {}".format(tf_dump_path))
    tf_model.save_weights(tf_dump_path, save_format="h5")


def convert_all_pt_checkpoints_to_tf(
    args_model_type,
    tf_dump_path,
    model_shortcut_names_or_path=None,
    config_shortcut_names_or_path=None,
    compare_with_pt_model=False,
    use_cached_models=False,
    remove_cached_files=False,
    only_convert_finetuned_models=False,
):
    assert os.path.isdir(args.tf_dump_path), "--tf_dump_path should be a directory"

    if args_model_type is None:
        model_types = list(MODEL_CLASSES.keys())
    else:
        model_types = [args_model_type]

    for j, model_type in enumerate(model_types, start=1):
        print("=" * 100)
        print(" Converting model type {}/{}: {}".format(j, len(model_types), model_type))
        print("=" * 100)
        if model_type not in MODEL_CLASSES:
            raise ValueError(
                "Unrecognized model type {}, should be one of {}.".format(model_type, list(MODEL_CLASSES.keys()))
            )

        config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            model_shortcut_names_or_path = list(aws_model_maps.keys())
        if config_shortcut_names_or_path is None:
            config_shortcut_names_or_path = model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1
        ):
            print("-" * 100)
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print("    Skipping finetuned checkpoint {}".format(model_shortcut_name))
                    continue
                model_type = model_shortcut_name
            elif only_convert_finetuned_models:
                print("    Skipping not finetuned checkpoint {}".format(model_shortcut_name))
                continue
            print(
                "    Converting checkpoint {}/{}: {} - model_type {}".format(
                    i, len(aws_config_map), model_shortcut_name, model_type
                )
            )
            print("-" * 100)

            if config_shortcut_name in aws_config_map:
                config_file = cached_path(aws_config_map[config_shortcut_name], force_download=not use_cached_models)
            else:
                config_file = cached_path(config_shortcut_name, force_download=not use_cached_models)

            if model_shortcut_name in aws_model_maps:
                model_file = cached_path(aws_model_maps[model_shortcut_name], force_download=not use_cached_models)
            else:
                model_file = cached_path(model_shortcut_name, force_download=not use_cached_models)

            if os.path.isfile(model_shortcut_name):
                model_shortcut_name = "converted_model"

            convert_pt_checkpoint_to_tf(
                model_type=model_type,
                pytorch_checkpoint_path=model_file,
                config_file=config_file,
                tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"),
                compare_with_pt_model=compare_with_pt_model,
            )
            if remove_cached_files:
                os.remove(config_file)
                os.remove(model_file)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
    )
    parser.add_argument(
        "--model_type",
        default=None,
        type=str,
        help="Model type selected in the list of {}. If not given, will download and convert all the models from AWS.".format(
            list(MODEL_CLASSES.keys())
        ),
    )
    parser.add_argument(
        "--pytorch_checkpoint_path",
        default=None,
        type=str,
        help="Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
        "If not given, will download and convert all the checkpoints from AWS.",
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        help="The config json file corresponding to the pre-trained model. \n"
        "This specifies the model architecture. If not given and "
        "--pytorch_checkpoint_path is not given or is a shortcut name"
        "use the configuration associated to the shortcut name on the AWS",
    )
    parser.add_argument(
        "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
    )
    parser.add_argument(
        "--use_cached_models",
        action="store_true",
        help="Use cached models if possible instead of updating to latest checkpoint versions.",
    )
    parser.add_argument(
        "--remove_cached_files",
        action="store_true",
        help="Remove pytorch models after conversion (save memory when converting in batches).",
    )
    parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
    args = parser.parse_args()

    # if args.pytorch_checkpoint_path is not None:
    #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
    #                                 args.pytorch_checkpoint_path,
    #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
    #                                 args.tf_dump_path,
    #                                 compare_with_pt_model=args.compare_with_pt_model,
    #                                 use_cached_models=args.use_cached_models)
    # else:
    convert_all_pt_checkpoints_to_tf(
        args.model_type.lower() if args.model_type is not None else None,
        args.tf_dump_path,
        model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
        if args.pytorch_checkpoint_path is not None
        else None,
        config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
        compare_with_pt_model=args.compare_with_pt_model,
        use_cached_models=args.use_cached_models,
        remove_cached_files=args.remove_cached_files,
        only_convert_finetuned_models=args.only_convert_finetuned_models,
    )
