# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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 json
import math
import random
import re
from typing import List, Optional

import numpy as np
import paddle
from tqdm import tqdm

from paddlenlp.utils.log import logger


def set_seed(seed):
    paddle.seed(seed)
    random.seed(seed)
    np.random.seed(seed)


def create_data_loader(dataset, mode="train", batch_size=1, trans_fn=None):
    """
    Create dataloader.
    Args:
        dataset(obj:`paddle.io.Dataset`): Dataset instance.
        mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
        batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
        trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
    Returns:
        dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
    """
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == "train" else False
    if mode == "train":
        sampler = paddle.io.DistributedBatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    dataloader = paddle.io.DataLoader(dataset, batch_sampler=sampler, return_list=True)
    return dataloader


def map_offset(ori_offset, offset_mapping):
    """
    map ori offset to token offset
    """
    for index, span in enumerate(offset_mapping):
        if span[0] <= ori_offset < span[1]:
            return index
    return -1


def reader(data_path, max_seq_len=512):
    """
    read json
    """
    with open(data_path, "r", encoding="utf-8") as f:
        for line in f:
            json_line = json.loads(line)
            content = json_line["content"].strip()
            prompt = json_line["prompt"]
            # Model Input is aslike: [CLS] Prompt [SEP] Content [SEP]
            # It include three summary tokens.
            if max_seq_len <= len(prompt) + 3:
                raise ValueError("The value of max_seq_len is too small, please set a larger value")
            max_content_len = max_seq_len - len(prompt) - 3
            if len(content) <= max_content_len:
                yield json_line
            else:
                result_list = json_line["result_list"]
                json_lines = []
                accumulate = 0
                while True:
                    cur_result_list = []
                    for result in result_list:
                        if result["end"] - result["start"] > max_content_len:
                            logger.warning(
                                "result['end'] - result ['start'] exceeds max_content_len, which will result in no valid instance being returned"
                            )
                        if (
                            result["start"] + 1 <= max_content_len < result["end"]
                            and result["end"] - result["start"] <= max_content_len
                        ):
                            max_content_len = result["start"]
                            break

                    cur_content = content[:max_content_len]
                    res_content = content[max_content_len:]

                    while True:
                        if len(result_list) == 0:
                            break
                        elif result_list[0]["end"] <= max_content_len:
                            if result_list[0]["end"] > 0:
                                cur_result = result_list.pop(0)
                                cur_result_list.append(cur_result)
                            else:
                                cur_result_list = [result for result in result_list]
                                break
                        else:
                            break

                    json_line = {"content": cur_content, "result_list": cur_result_list, "prompt": prompt}
                    json_lines.append(json_line)

                    for result in result_list:
                        if result["end"] <= 0:
                            break
                        result["start"] -= max_content_len
                        result["end"] -= max_content_len
                    accumulate += max_content_len
                    max_content_len = max_seq_len - len(prompt) - 3
                    if len(res_content) == 0:
                        break
                    elif len(res_content) < max_content_len:
                        json_line = {"content": res_content, "result_list": result_list, "prompt": prompt}
                        json_lines.append(json_line)
                        break
                    else:
                        content = res_content

                for json_line in json_lines:
                    yield json_line


def unify_prompt_name(prompt):
    # The classification labels are shuffled during finetuning, so they need
    # to be unified during evaluation.
    if re.search(r"\[.*?\]$", prompt):
        prompt_prefix = prompt[: prompt.find("[", 1)]
        cls_options = re.search(r"\[.*?\]$", prompt).group()[1:-1].split(",")
        cls_options = sorted(list(set(cls_options)))
        cls_options = ",".join(cls_options)
        prompt = prompt_prefix + "[" + cls_options + "]"
        return prompt
    return prompt


def get_relation_type_dict(relation_data, schema_lang="ch"):
    def compare(a, b, schema_lang="ch"):
        if schema_lang == "ch":
            a = a[::-1]
            b = b[::-1]

        res = ""
        for i in range(min(len(a), len(b))):
            if a[i] == b[i]:
                res += a[i]
            else:
                break
        if res == "":
            return res
        if schema_lang == "ch" and res[::-1][0] == "的":
            return res[::-1][1:]
        elif schema_lang == "en" and res[-3:] == " of":
            return res[:-3]
        return ""

    relation_type_dict = {}
    added_list = []
    for i in range(len(relation_data)):
        added = False
        if relation_data[i][0] not in added_list:
            for j in range(i + 1, len(relation_data)):
                match = compare(relation_data[i][0], relation_data[j][0], schema_lang=schema_lang)
                if match != "":
                    match = unify_prompt_name(match)
                    if relation_data[i][0] not in added_list:
                        added_list.append(relation_data[i][0])
                        relation_type_dict.setdefault(match, []).append(relation_data[i][1])
                    added_list.append(relation_data[j][0])
                    relation_type_dict.setdefault(match, []).append(relation_data[j][1])
                    added = True
            if not added:
                added_list.append(relation_data[i][0])
                if schema_lang == "ch":
                    suffix = relation_data[i][0].rsplit("的", 1)[1]
                    suffix = unify_prompt_name(suffix)
                    relation_type = suffix
                else:
                    prefix = relation_data[i][0].split(" of ", 1)[0]
                    prefix = unify_prompt_name(prefix)
                    relation_type = prefix
                relation_type_dict.setdefault(relation_type, []).append(relation_data[i][1])
    return relation_type_dict


def add_entity_negative_example(examples, texts, prompts, label_set, negative_ratio):
    negative_examples = []
    positive_examples = []
    with tqdm(total=len(prompts)) as pbar:
        for i, prompt in enumerate(prompts):
            redundants = list(set(label_set) ^ set(prompt))
            redundants.sort()

            num_positive = len(examples[i])
            if num_positive != 0:
                actual_ratio = math.ceil(len(redundants) / num_positive)
            else:
                # Set num_positive to 1 for text without positive example
                num_positive, actual_ratio = 1, 0

            if actual_ratio <= negative_ratio or negative_ratio == -1:
                idxs = [k for k in range(len(redundants))]
            else:
                idxs = random.sample(range(0, len(redundants)), negative_ratio * num_positive)

            for idx in idxs:
                negative_result = {"content": texts[i], "result_list": [], "prompt": redundants[idx]}
                negative_examples.append(negative_result)
            positive_examples.extend(examples[i])
            pbar.update(1)
    return positive_examples, negative_examples


def add_relation_negative_example(redundants, text, num_positive, ratio):
    added_example = []
    rest_example = []

    if num_positive != 0:
        actual_ratio = math.ceil(len(redundants) / num_positive)
    else:
        # Set num_positive to 1 for text without positive example
        num_positive, actual_ratio = 1, 0

    all_idxs = [k for k in range(len(redundants))]
    if actual_ratio <= ratio or ratio == -1:
        idxs = all_idxs
        rest_idxs = []
    else:
        idxs = random.sample(range(0, len(redundants)), ratio * num_positive)
        rest_idxs = list(set(all_idxs) ^ set(idxs))

    for idx in idxs:
        negative_result = {"content": text, "result_list": [], "prompt": redundants[idx]}
        added_example.append(negative_result)

    for rest_idx in rest_idxs:
        negative_result = {"content": text, "result_list": [], "prompt": redundants[rest_idx]}
        rest_example.append(negative_result)

    return added_example, rest_example


def add_full_negative_example(examples, texts, relation_prompts, predicate_set, subject_goldens, schema_lang="ch"):
    with tqdm(total=len(relation_prompts)) as pbar:
        for i, relation_prompt in enumerate(relation_prompts):
            negative_sample = []
            for subject in subject_goldens[i]:
                for predicate in predicate_set:
                    # The relation prompt is constructed as follows:
                    # subject + "的" + predicate -> Chinese
                    # predicate + " of " + subject -> English
                    if schema_lang == "ch":
                        prompt = subject + "的" + predicate
                    else:
                        prompt = predicate + " of " + subject
                    if prompt not in relation_prompt:
                        negative_result = {"content": texts[i], "result_list": [], "prompt": prompt}
                        negative_sample.append(negative_result)
            examples[i].extend(negative_sample)
            pbar.update(1)
    return examples


def generate_cls_example(text, labels, prompt_prefix, options):
    random.shuffle(options)
    cls_options = ",".join(options)
    prompt = prompt_prefix + "[" + cls_options + "]"

    result_list = []
    example = {"content": text, "result_list": result_list, "prompt": prompt}
    for label in labels:
        start = prompt.rfind(label) - len(prompt) - 1
        end = start + len(label)
        result = {"text": label, "start": start, "end": end}
        example["result_list"].append(result)
    return example


def convert_cls_examples(raw_examples, prompt_prefix="情感倾向", options=["正向", "负向"]):
    """
    Convert labeled data export from doccano for classification task.
    """
    examples = []
    logger.info("Converting doccano data...")
    with tqdm(total=len(raw_examples)):
        for line in raw_examples:
            items = json.loads(line)
            # Compatible with doccano >= 1.6.2
            if "data" in items.keys():
                text, labels = items["data"], items["label"]
            else:
                text, labels = items["text"], items["label"]
            example = generate_cls_example(text, labels, prompt_prefix, options)
            examples.append(example)
    return examples


def convert_ext_examples(
    raw_examples,
    negative_ratio,
    prompt_prefix="情感倾向",
    options=["正向", "负向"],
    separator="##",
    is_train=True,
    schema_lang="ch",
):
    """
    Convert labeled data export from doccano for extraction and aspect-level classification task.
    """

    def _sep_cls_label(label, separator):
        label_list = label.split(separator)
        if len(label_list) == 1:
            return label_list[0], None
        return label_list[0], label_list[1:]

    texts = []
    entity_examples = []
    relation_examples = []
    entity_cls_examples = []
    entity_prompts = []
    relation_prompts = []
    entity_label_set = []
    entity_name_set = []
    predicate_set = []
    subject_goldens = []
    inverse_relation_list = []
    predicate_list = []

    logger.info("Converting doccano data...")
    with tqdm(total=len(raw_examples)) as pbar:
        for line in raw_examples:
            items = json.loads(line)
            entity_id = 0
            if "data" in items.keys():
                relation_mode = False
                if isinstance(items["label"], dict) and "entities" in items["label"].keys():
                    relation_mode = True
                text = items["data"]
                entities = []
                relations = []
                if not relation_mode:
                    # Export file in JSONL format which doccano < 1.7.0
                    # e.g. {"data": "", "label": [ [0, 2, "ORG"], ... ]}
                    for item in items["label"]:
                        entity = {"id": entity_id, "start_offset": item[0], "end_offset": item[1], "label": item[2]}
                        entities.append(entity)
                        entity_id += 1
                else:
                    # Export file in JSONL format for relation labeling task which doccano < 1.7.0
                    # e.g. {"data": "", "label": {"relations": [ {"id": 0, "start_offset": 0, "end_offset": 6, "label": "ORG"}, ... ], "entities": [ {"id": 0, "from_id": 0, "to_id": 1, "type": "foundedAt"}, ... ]}}
                    entities.extend([entity for entity in items["label"]["entities"]])
                    if "relations" in items["label"].keys():
                        relations.extend([relation for relation in items["label"]["relations"]])
            else:
                # Export file in JSONL format which doccano >= 1.7.0
                # e.g. {"text": "", "label": [ [0, 2, "ORG"], ... ]}
                if "label" in items.keys():
                    text = items["text"]
                    entities = []
                    for item in items["label"]:
                        entity = {"id": entity_id, "start_offset": item[0], "end_offset": item[1], "label": item[2]}
                        entities.append(entity)
                        entity_id += 1
                    relations = []
                else:
                    # Export file in JSONL (relation) format
                    # e.g. {"text": "", "relations": [ {"id": 0, "start_offset": 0, "end_offset": 6, "label": "ORG"}, ... ], "entities": [ {"id": 0, "from_id": 0, "to_id": 1, "type": "foundedAt"}, ... ]}
                    text, relations, entities = items["text"], items["relations"], items["entities"]
            texts.append(text)

            entity_example = []
            entity_prompt = []
            entity_example_map = {}
            entity_map = {}  # id to entity name
            for entity in entities:
                entity_name = text[entity["start_offset"] : entity["end_offset"]]
                entity_map[entity["id"]] = {
                    "name": entity_name,
                    "start": entity["start_offset"],
                    "end": entity["end_offset"],
                }

                entity_label, entity_cls_label = _sep_cls_label(entity["label"], separator)

                # Define the prompt prefix for entity-level classification
                # xxx + "的" + 情感倾向 -> Chinese
                # Sentiment classification + " of " + xxx -> English
                if schema_lang == "ch":
                    entity_cls_prompt_prefix = entity_name + "的" + prompt_prefix
                else:
                    entity_cls_prompt_prefix = prompt_prefix + " of " + entity_name
                if entity_cls_label is not None:
                    entity_cls_example = generate_cls_example(
                        text, entity_cls_label, entity_cls_prompt_prefix, options
                    )

                    entity_cls_examples.append(entity_cls_example)

                result = {"text": entity_name, "start": entity["start_offset"], "end": entity["end_offset"]}
                if entity_label not in entity_example_map.keys():
                    entity_example_map[entity_label] = {
                        "content": text,
                        "result_list": [result],
                        "prompt": entity_label,
                    }
                else:
                    entity_example_map[entity_label]["result_list"].append(result)

                if entity_label not in entity_label_set:
                    entity_label_set.append(entity_label)
                if entity_name not in entity_name_set:
                    entity_name_set.append(entity_name)
                entity_prompt.append(entity_label)

            for v in entity_example_map.values():
                entity_example.append(v)

            entity_examples.append(entity_example)
            entity_prompts.append(entity_prompt)

            subject_golden = []  # Golden entity inputs
            relation_example = []
            relation_prompt = []
            relation_example_map = {}
            inverse_relation = []
            predicates = []
            for relation in relations:
                predicate = relation["type"]
                subject_id = relation["from_id"]
                object_id = relation["to_id"]
                # The relation prompt is constructed as follows:
                # subject + "的" + predicate -> Chinese
                # predicate + " of " + subject -> English
                if schema_lang == "ch":
                    prompt = entity_map[subject_id]["name"] + "的" + predicate
                    inverse_negative = entity_map[object_id]["name"] + "的" + predicate
                else:
                    prompt = predicate + " of " + entity_map[subject_id]["name"]
                    inverse_negative = predicate + " of " + entity_map[object_id]["name"]

                if entity_map[subject_id]["name"] not in subject_golden:
                    subject_golden.append(entity_map[subject_id]["name"])
                result = {
                    "text": entity_map[object_id]["name"],
                    "start": entity_map[object_id]["start"],
                    "end": entity_map[object_id]["end"],
                }

                inverse_relation.append(inverse_negative)
                predicates.append(predicate)

                if prompt not in relation_example_map.keys():
                    relation_example_map[prompt] = {"content": text, "result_list": [result], "prompt": prompt}
                else:
                    relation_example_map[prompt]["result_list"].append(result)

                if predicate not in predicate_set:
                    predicate_set.append(predicate)
                relation_prompt.append(prompt)

            for v in relation_example_map.values():
                relation_example.append(v)

            relation_examples.append(relation_example)
            relation_prompts.append(relation_prompt)
            subject_goldens.append(subject_golden)
            inverse_relation_list.append(inverse_relation)
            predicate_list.append(predicates)
            pbar.update(1)

    logger.info("Adding negative samples for first stage prompt...")
    positive_examples, negative_examples = add_entity_negative_example(
        entity_examples, texts, entity_prompts, entity_label_set, negative_ratio
    )
    if len(positive_examples) == 0:
        all_entity_examples = []
    else:
        all_entity_examples = positive_examples + negative_examples

    all_relation_examples = []
    if len(predicate_set) != 0:
        logger.info("Adding negative samples for second stage prompt...")
        if is_train:

            positive_examples = []
            negative_examples = []
            per_n_ratio = negative_ratio // 3

            with tqdm(total=len(texts)) as pbar:
                for i, text in enumerate(texts):
                    negative_example = []
                    collects = []
                    num_positive = len(relation_examples[i])

                    # 1. inverse_relation_list
                    redundants1 = inverse_relation_list[i]

                    # 2. entity_name_set ^ subject_goldens[i]
                    redundants2 = []
                    if len(predicate_list[i]) != 0:
                        nonentity_list = list(set(entity_name_set) ^ set(subject_goldens[i]))
                        nonentity_list.sort()

                        if schema_lang == "ch":
                            redundants2 = [
                                nonentity + "的" + predicate_list[i][random.randrange(len(predicate_list[i]))]
                                for nonentity in nonentity_list
                            ]
                        else:
                            redundants2 = [
                                predicate_list[i][random.randrange(len(predicate_list[i]))] + " of " + nonentity
                                for nonentity in nonentity_list
                            ]

                    # 3. entity_label_set ^ entity_prompts[i]
                    redundants3 = []
                    if len(subject_goldens[i]) != 0:
                        non_ent_label_list = list(set(entity_label_set) ^ set(entity_prompts[i]))
                        non_ent_label_list.sort()

                        if schema_lang == "ch":
                            redundants3 = [
                                subject_goldens[i][random.randrange(len(subject_goldens[i]))] + "的" + non_ent_label
                                for non_ent_label in non_ent_label_list
                            ]
                        else:
                            redundants3 = [
                                non_ent_label + " of " + subject_goldens[i][random.randrange(len(subject_goldens[i]))]
                                for non_ent_label in non_ent_label_list
                            ]

                    redundants_list = [redundants1, redundants2, redundants3]

                    for redundants in redundants_list:
                        added, rest = add_relation_negative_example(
                            redundants,
                            texts[i],
                            num_positive,
                            per_n_ratio,
                        )
                        negative_example.extend(added)
                        collects.extend(rest)

                    num_sup = num_positive * negative_ratio - len(negative_example)
                    if num_sup > 0 and collects:
                        if num_sup > len(collects):
                            idxs = [k for k in range(len(collects))]
                        else:
                            idxs = random.sample(range(0, len(collects)), num_sup)
                        for idx in idxs:
                            negative_example.append(collects[idx])

                    positive_examples.extend(relation_examples[i])
                    negative_examples.extend(negative_example)
                    pbar.update(1)
            all_relation_examples = positive_examples + negative_examples
        else:
            relation_examples = add_full_negative_example(
                relation_examples, texts, relation_prompts, predicate_set, subject_goldens, schema_lang=schema_lang
            )
            all_relation_examples = [r for relation_example in relation_examples for r in relation_example]
    return all_entity_examples, all_relation_examples, entity_cls_examples


def get_dynamic_max_length(examples, default_max_length: int, dynamic_max_length: List[int]) -> int:
    """get max_length by examples which you can change it by examples in batch"""
    cur_length = len(examples[0]["input_ids"])
    max_length = default_max_length
    for max_length_option in sorted(dynamic_max_length):
        if cur_length <= max_length_option:
            max_length = max_length_option
            break
    return max_length


def convert_example(
    example, tokenizer, max_seq_len, multilingual=False, dynamic_max_length: Optional[List[int]] = None
):
    """
    example: {
        title
        prompt
        content
        result_list
    }
    """
    if dynamic_max_length is not None:
        temp_encoded_inputs = tokenizer(
            text=[example["prompt"]],
            text_pair=[example["content"]],
            truncation=True,
            max_seq_len=max_seq_len,
            return_attention_mask=True,
            return_position_ids=True,
            return_dict=False,
            return_offsets_mapping=True,
        )
        max_length = get_dynamic_max_length(
            examples=temp_encoded_inputs, default_max_length=max_seq_len, dynamic_max_length=dynamic_max_length
        )
        # always pad to max_length
        encoded_inputs = tokenizer(
            text=[example["prompt"]],
            text_pair=[example["content"]],
            truncation=True,
            max_seq_len=max_length,
            pad_to_max_seq_len=True,
            return_token_type_ids=True,
            return_attention_mask=True,
            return_position_ids=True,
            return_dict=False,
            return_offsets_mapping=True,
        )
        start_ids = [0.0 for x in range(max_length)]
        end_ids = [0.0 for x in range(max_length)]
    else:
        encoded_inputs = tokenizer(
            text=[example["prompt"]],
            text_pair=[example["content"]],
            truncation=True,
            max_seq_len=max_seq_len,
            pad_to_max_seq_len=True,
            return_token_type_ids=True,
            return_attention_mask=True,
            return_position_ids=True,
            return_dict=False,
            return_offsets_mapping=True,
        )
        start_ids = [0.0 for x in range(max_seq_len)]
        end_ids = [0.0 for x in range(max_seq_len)]

    encoded_inputs = encoded_inputs[0]
    offset_mapping = [list(x) for x in encoded_inputs["offset_mapping"]]
    bias = 0
    for index in range(1, len(offset_mapping)):
        mapping = offset_mapping[index]
        if mapping[0] == 0 and mapping[1] == 0 and bias == 0:
            bias = offset_mapping[index - 1][1] + 1  # Includes [SEP] token
        if mapping[0] == 0 and mapping[1] == 0:
            continue
        offset_mapping[index][0] += bias
        offset_mapping[index][1] += bias
    for item in example["result_list"]:
        start = map_offset(item["start"] + bias, offset_mapping)
        end = map_offset(item["end"] - 1 + bias, offset_mapping)
        start_ids[start] = 1.0
        end_ids[end] = 1.0
    if multilingual:
        tokenized_output = {
            "input_ids": encoded_inputs["input_ids"],
            "position_ids": encoded_inputs["position_ids"],
            "start_positions": start_ids,
            "end_positions": end_ids,
        }
    else:
        tokenized_output = {
            "input_ids": encoded_inputs["input_ids"],
            "token_type_ids": encoded_inputs["token_type_ids"],
            "position_ids": encoded_inputs["position_ids"],
            "attention_mask": encoded_inputs["attention_mask"],
            "start_positions": start_ids,
            "end_positions": end_ids,
        }
    return tokenized_output
