# Copyright 2021 HuggingFace Inc.
#
# 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 itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import Speech2TextFeatureExtractor
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


global_rng = random.Random()


# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
def floats_list(shape, scale=1.0, rng=None, name=None):
    """Creates a random float32 tensor"""
    if rng is None:
        rng = global_rng

    values = []
    for batch_idx in range(shape[0]):
        values.append([])
        for _ in range(shape[1]):
            values[-1].append(rng.random() * scale)

    return values


@require_torch
@require_torchaudio
class Speech2TextFeatureExtractionTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        min_seq_length=400,
        max_seq_length=2000,
        feature_size=24,
        num_mel_bins=24,
        padding_value=0.0,
        sampling_rate=16_000,
        return_attention_mask=True,
        do_normalize=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.min_seq_length = min_seq_length
        self.max_seq_length = max_seq_length
        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        self.feature_size = feature_size
        self.num_mel_bins = num_mel_bins
        self.padding_value = padding_value
        self.sampling_rate = sampling_rate
        self.return_attention_mask = return_attention_mask
        self.do_normalize = do_normalize

    def prepare_feat_extract_dict(self):
        return {
            "feature_size": self.feature_size,
            "num_mel_bins": self.num_mel_bins,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }

    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
        def _flatten(list_of_lists):
            return list(itertools.chain(*list_of_lists))

        if equal_length:
            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
        else:
            # make sure that inputs increase in size
            speech_inputs = [
                floats_list((x, self.feature_size))
                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
            ]
        if numpify:
            speech_inputs = [np.asarray(x) for x in speech_inputs]
        return speech_inputs


@require_torch
@require_torchaudio
class Speech2TextFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
    feature_extraction_class = Speech2TextFeatureExtractor

    def setUp(self):
        self.feat_extract_tester = Speech2TextFeatureExtractionTester(self)

    def _check_zero_mean_unit_variance(self, input_vector):
        self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
        self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))

    def test_call(self):
        # Tests that all call wrap to encode_plus and batch_encode_plus
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]

        # Test feature size
        input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
        self.assertTrue(input_features.ndim == 3)
        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)

        # Test not batched input
        encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
        encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
        self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))

        # Test batched
        encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))

        # Test 2-D numpy arrays are batched.
        speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
        np_speech_inputs = np.asarray(speech_inputs)
        encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
        for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
            self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))

    def test_dither(self):
        np.random.seed(42)  # seed the dithering randn()

        # Tests that features with and without little dithering are similar, but not the same
        dict_no_dither = self.feat_extract_tester.prepare_feat_extract_dict()
        dict_no_dither["dither"] = 0.0

        dict_dither = self.feat_extract_tester.prepare_feat_extract_dict()
        dict_dither["dither"] = 1.0

        feature_extractor_no_dither = self.feature_extraction_class(**dict_no_dither)
        feature_extractor_dither = self.feature_extraction_class(**dict_dither)

        # create three inputs of length 800, 1000, and 1200
        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]

        # compute features
        input_features_no_dither = feature_extractor_no_dither(
            np_speech_inputs, padding=True, return_tensors="np", sampling_rate=dict_no_dither["sampling_rate"]
        ).input_features
        input_features_dither = feature_extractor_dither(
            np_speech_inputs, padding=True, return_tensors="np", sampling_rate=dict_dither["sampling_rate"]
        ).input_features

        # test there is a difference between features (there's added noise to input signal)
        diff = input_features_dither - input_features_no_dither

        # features are not identical
        self.assertTrue(np.abs(diff).mean() > 1e-5)
        # features are not too different
        self.assertTrue(np.abs(diff).mean() <= 1e-3)
        self.assertTrue(np.abs(diff).max() <= 5e-2)

    def test_cepstral_mean_and_variance_normalization(self):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]

        paddings = ["longest", "max_length", "do_not_pad"]
        max_lengths = [None, 16, None]
        for max_length, padding in zip(max_lengths, paddings):
            inputs = feature_extractor(
                speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True
            )
            input_features = inputs.input_features
            attention_mask = inputs.attention_mask
            fbank_feat_lengths = [np.sum(x) for x in attention_mask]

            self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
            self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
            self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])

    def test_cepstral_mean_and_variance_normalization_np(self):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]

        paddings = ["longest", "max_length", "do_not_pad"]
        max_lengths = [None, 16, None]
        for max_length, padding in zip(max_lengths, paddings):
            inputs = feature_extractor(
                speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True
            )
            input_features = inputs.input_features
            attention_mask = inputs.attention_mask
            fbank_feat_lengths = [np.sum(x) for x in attention_mask]

            self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
            self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
            self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
            self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
            self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])

    def test_cepstral_mean_and_variance_normalization_trunc_max_length(self):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        inputs = feature_extractor(
            speech_inputs,
            padding="max_length",
            max_length=4,
            truncation=True,
            return_tensors="np",
            return_attention_mask=True,
        )
        input_features = inputs.input_features
        attention_mask = inputs.attention_mask
        fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
        self._check_zero_mean_unit_variance(input_features[1])
        self._check_zero_mean_unit_variance(input_features[2])

    def test_cepstral_mean_and_variance_normalization_trunc_longest(self):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        inputs = feature_extractor(
            speech_inputs,
            padding="longest",
            max_length=4,
            truncation=True,
            return_tensors="np",
            return_attention_mask=True,
        )
        input_features = inputs.input_features
        attention_mask = inputs.attention_mask
        fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
        self._check_zero_mean_unit_variance(input_features[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape, (3, 4, 24))

        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        inputs = feature_extractor(
            speech_inputs,
            padding="longest",
            max_length=16,
            truncation=True,
            return_tensors="np",
            return_attention_mask=True,
        )
        input_features = inputs.input_features
        attention_mask = inputs.attention_mask
        fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)

        self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
        self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
        self._check_zero_mean_unit_variance(input_features[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertEqual(input_features.shape, (3, 6, 24))

    def test_double_precision_pad(self):
        import torch

        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
        py_speech_inputs = np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
            np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
            self.assertTrue(np_processed.input_features.dtype == np.float32)
            pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
            self.assertTrue(pt_processed.input_features.dtype == torch.float32)

    def _load_datasamples(self, num_samples):
        from datasets import load_dataset

        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        # automatic decoding with librispeech
        speech_samples = ds.sort("id")[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]

    def test_integration(self):
        # fmt: off
        expected = np.array([
            -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
            -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
            -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
        ])
        # fmt: on

        input_speech = self._load_datasamples(1)
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        input_features = feature_extractor(input_speech, return_tensors="pt").input_features
        self.assertEqual(input_features.shape, (1, 584, 24))
        self.assertTrue(np.allclose(input_features[0, 0, :30], expected, atol=1e-4))

    def test_feat_extract_from_and_save_pretrained(self):
        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
            check_json_file_has_correct_format(saved_file)
            feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)

        dict_first = feat_extract_first.to_dict()
        dict_second = feat_extract_second.to_dict()
        self.assertDictEqual(dict_first, dict_second)

    def test_feat_extract_to_json_file(self):
        feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)

        with tempfile.TemporaryDirectory() as tmpdirname:
            json_file_path = os.path.join(tmpdirname, "feat_extract.json")
            feat_extract_first.to_json_file(json_file_path)
            feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)

        dict_first = feat_extract_first.to_dict()
        dict_second = feat_extract_second.to_dict()
        self.assertEqual(dict_first, dict_second)


# exact same tests than before, except that we simulate that torchaudio is not available
@require_torch
@unittest.mock.patch(
    "transformers.models.speech_to_text.feature_extraction_speech_to_text.is_speech_available", lambda: False
)
class Speech2TextFeatureExtractionWithoutTorchaudioTest(Speech2TextFeatureExtractionTest):
    def test_using_audio_utils(self):
        # Tests that it uses audio_utils instead of torchaudio
        feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())

        self.assertTrue(hasattr(feat_extract, "window"))
        self.assertTrue(hasattr(feat_extract, "mel_filters"))

        from transformers.models.speech_to_text.feature_extraction_speech_to_text import is_speech_available

        self.assertFalse(is_speech_available())