#   Copyright (c) 2018 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 unittest
import numpy as np
from op_test import OpTest


class Segment(object):
    def __init__(self, chunk_type, start_idx, end_idx):
        self.chunk_type = chunk_type
        self.start_idx = start_idx
        self.end_idx = end_idx

    def __str__(self):
        return '(Segment: %s, %s, %s)' % (self.chunk_type, self.start_idx,
                                          self.end_idx)

    __repr__ = __str__


class TestChunkEvalOp(OpTest):
    num_sequences = 5
    batch_size = 50

    def parse_scheme(self):
        if self.scheme == 'IOB':
            self.num_tag_types = 2
        elif self.scheme == 'IOE':
            self.num_tag_types = 2

    def fill_with_chunks(self, data, chunks):
        for chunk in chunks:
            if self.scheme == 'IOB':
                data[chunk.start_idx] = chunk.chunk_type * self.num_tag_types
                data[chunk.start_idx + 1:
                     chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
                         self.num_tag_types - 1)
                data[chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
                    self.num_tag_types - 1
                ) if chunk.start_idx < chunk.end_idx else data[chunk.start_idx]
            elif self.scheme == 'IOE':
                data[chunk.start_idx:
                     chunk.end_idx] = chunk.chunk_type * self.num_tag_types
                data[chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
                    self.num_tag_types - 1)

    def rand_chunks(self, starts, num_chunks):
        if num_chunks < 0:
            num_chunks = np.random.randint(starts[-1])
        chunks = []
        # generate chunk beginnings
        chunk_begins = sorted(
            np.random.choice(
                range(starts[-1]), num_chunks, replace=False))
        seq_chunk_begins = []
        begin_idx = 0
        # divide chunks into sequences
        for i in range(len(starts) - 1):
            tmp_chunk_begins = []
            while begin_idx < len(chunk_begins) and chunk_begins[
                    begin_idx] < starts[i + 1]:
                tmp_chunk_begins.append(chunk_begins[begin_idx])
                begin_idx += 1
            seq_chunk_begins.append(tmp_chunk_begins)
        # generate chunk ends
        chunk_ends = []
        for i in range(len(seq_chunk_begins)):
            for j in range(len(seq_chunk_begins[i])):
                low = seq_chunk_begins[i][j]
                high = seq_chunk_begins[i][j + 1] if j < len(seq_chunk_begins[
                    i]) - 1 else starts[i + 1]
                chunk_ends.append(np.random.randint(low, high))
        # generate chunks
        for chunk_pos in zip(chunk_begins, chunk_ends):
            chunk_type = np.random.randint(self.num_chunk_types)
            chunks.append(Segment(chunk_type, *chunk_pos))
        return chunks

    def gen_chunks(self, infer, label, starts):
        chunks = self.rand_chunks(starts,
                                  self.num_infer_chunks + self.num_label_chunks
                                  - self.num_correct_chunks)
        correct_chunks = np.random.choice(
            range(len(chunks)), self.num_correct_chunks, replace=False)
        infer_chunks = np.random.choice(
            [x for x in range(len(chunks)) if x not in correct_chunks],
            self.num_infer_chunks - self.num_correct_chunks,
            replace=False)
        infer_chunks = sorted(correct_chunks.tolist() + infer_chunks.tolist())
        label_chunks = np.random.choice(
            [x for x in range(len(chunks)) if x not in infer_chunks],
            self.num_label_chunks - self.num_correct_chunks,
            replace=False)
        label_chunks = sorted(correct_chunks.tolist() + label_chunks.tolist())
        self.fill_with_chunks(infer, [chunks[idx] for idx in infer_chunks])
        self.fill_with_chunks(label, [chunks[idx] for idx in label_chunks])
        # exclude types in excluded_chunk_types
        if len(self.excluded_chunk_types) > 0:
            for idx in correct_chunks:
                if chunks[idx].chunk_type in self.excluded_chunk_types:
                    self.num_correct_chunks -= 1
            for idx in infer_chunks:
                if chunks[idx].chunk_type in self.excluded_chunk_types:
                    self.num_infer_chunks -= 1
            for idx in label_chunks:
                if chunks[idx].chunk_type in self.excluded_chunk_types:
                    self.num_label_chunks -= 1
        return self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks

    def set_confs(self):
        # Use the IOB scheme and labels with 2 chunk types
        self.scheme = 'IOB'
        self.num_chunk_types = 2
        self.excluded_chunk_types = []
        self.other_chunk_type = self.num_chunk_types
        self.attrs = {
            'num_chunk_types': self.num_chunk_types,
            'chunk_scheme': self.scheme,
            'excluded_chunk_types': self.excluded_chunk_types
        }
        self.parse_scheme()
        self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 4, 5, 9

    def set_data(self):
        infer = np.zeros((self.batch_size, )).astype('int64')
        infer.fill(self.num_chunk_types * self.num_tag_types)
        label = np.copy(infer)
        starts = np.random.choice(
            range(1, self.batch_size), self.num_sequences - 1,
            replace=False).tolist()
        starts.extend([0, self.batch_size])
        starts = sorted(starts)
        self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = self.gen_chunks(
            infer, label, starts)
        self.inputs = {
            'Inference': (infer, [starts]),
            'Label': (label, [starts])
        }
        precision = float(
            self.num_correct_chunks
        ) / self.num_infer_chunks if self.num_infer_chunks else 0
        recall = float(self.num_correct_chunks
                       ) / self.num_label_chunks if self.num_label_chunks else 0
        f1 = float(2 * precision * recall) / (
            precision + recall) if self.num_correct_chunks else 0
        self.outputs = {
            'Precision': np.asarray(
                [precision], dtype='float32'),
            'Recall': np.asarray(
                [recall], dtype='float32'),
            'F1-Score': np.asarray(
                [f1], dtype='float32'),
            'NumInferChunks': np.asarray(
                [self.num_infer_chunks], dtype='int64'),
            'NumLabelChunks': np.asarray(
                [self.num_label_chunks], dtype='int64'),
            'NumCorrectChunks': np.asarray(
                [self.num_correct_chunks], dtype='int64')
        }

    def setUp(self):
        self.op_type = 'chunk_eval'
        self.set_confs()
        self.set_data()

    def test_check_output(self):
        self.check_output()


class TestChunkEvalOpWithExclude(TestChunkEvalOp):
    def set_confs(self):
        # Use the IOE scheme and labels with 3 chunk types
        self.scheme = 'IOE'
        self.num_chunk_types = 3
        self.excluded_chunk_types = [1]
        self.other_chunk_type = self.num_chunk_types
        self.attrs = {
            'num_chunk_types': self.num_chunk_types,
            'chunk_scheme': self.scheme,
            'excluded_chunk_types': self.excluded_chunk_types
        }
        self.parse_scheme()
        self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 15, 18, 20


if __name__ == '__main__':
    unittest.main()
