# coding=utf-8
# Copyright 2021-2023 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.
"""Tests for the SpeechT5 feature extractors."""

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, is_speech_available
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import SpeechT5FeatureExtractor

if is_torch_available():
    import torch


global_rng = random.Random()


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
class SpeechT5FeatureExtractionTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        min_seq_length=400,
        max_seq_length=2000,
        feature_size=1,
        padding_value=0.0,
        sampling_rate=16000,
        do_normalize=True,
        num_mel_bins=80,
        hop_length=16,
        win_length=64,
        win_function="hann_window",
        fmin=80,
        fmax=7600,
        mel_floor=1e-10,
        return_attention_mask=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.padding_value = padding_value
        self.sampling_rate = sampling_rate
        self.do_normalize = do_normalize
        self.num_mel_bins = num_mel_bins
        self.hop_length = hop_length
        self.win_length = win_length
        self.win_function = win_function
        self.fmin = fmin
        self.fmax = fmax
        self.mel_floor = mel_floor
        self.return_attention_mask = return_attention_mask

    def prepare_feat_extract_dict(self):
        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "do_normalize": self.do_normalize,
            "num_mel_bins": self.num_mel_bins,
            "hop_length": self.hop_length,
            "win_length": self.win_length,
            "win_function": self.win_function,
            "fmin": self.fmin,
            "fmax": self.fmax,
            "mel_floor": self.mel_floor,
            "return_attention_mask": self.return_attention_mask,
        }

    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.batch_size, self.max_seq_length))
        else:
            # make sure that inputs increase in size
            speech_inputs = [
                _flatten(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

    def prepare_inputs_for_target(self, equal_length=False, numpify=False):
        if equal_length:
            speech_inputs = [floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)]
        else:
            # make sure that inputs increase in size
            speech_inputs = [
                floats_list((x, self.num_mel_bins))
                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
class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
    feature_extraction_class = SpeechT5FeatureExtractor if is_speech_available() else None

    def setUp(self):
        self.feat_extract_tester = SpeechT5FeatureExtractionTester(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
        feat_extract = 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 not batched input
        encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
        encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values
        self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))

        # Test batched
        encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
        encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
        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_zero_mean_unit_variance_normalization_np(self):
        feat_extract = 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, 1600, None]
        for max_length, padding in zip(max_lengths, paddings):
            processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np")
            input_values = processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:800])
            self.assertTrue(input_values[0][800:].sum() < 1e-6)
            self._check_zero_mean_unit_variance(input_values[1][:1000])
            self.assertTrue(input_values[0][1000:].sum() < 1e-6)
            self._check_zero_mean_unit_variance(input_values[2][:1200])

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

        paddings = ["longest", "max_length", "do_not_pad"]
        max_lengths = [None, 1600, None]

        for max_length, padding in zip(max_lengths, paddings):
            processed = feat_extract(speech_inputs, max_length=max_length, padding=padding)
            input_values = processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:800])
            self._check_zero_mean_unit_variance(input_values[1][:1000])
            self._check_zero_mean_unit_variance(input_values[2][:1200])

    def test_zero_mean_unit_variance_normalization_trunc_np_max_length(self):
        feat_extract = 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)]
        processed = feat_extract(
            speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
        )
        input_values = processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])

    def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
        feat_extract = 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)]
        processed = feat_extract(
            speech_inputs, truncation=True, max_length=1000, padding="longest", return_tensors="np"
        )
        input_values = processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800])
        self._check_zero_mean_unit_variance(input_values[1, :1000])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 1000))

        speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
        processed = feat_extract(
            speech_inputs, truncation=True, max_length=2000, padding="longest", return_tensors="np"
        )
        input_values = processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :800])
        self._check_zero_mean_unit_variance(input_values[1, :1000])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 1200))

    def test_double_precision_pad(self):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        np_speech_inputs = np.random.rand(100).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_values": inputs}], return_tensors="np")
            self.assertTrue(np_processed.input_values.dtype == np.float32)
            pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
            self.assertTrue(pt_processed.input_values.dtype == torch.float32)

    def test_call_target(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 8000, 14000, and 2000
        speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
        np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]

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

        # Test not batched input
        encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_values
        encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_values
        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_values
        encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_values
        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_batch_feature_target(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        self.assertTrue(all(len(x) == len(y) for x, y in zip(speech_inputs, processed_features[input_name])))

        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target(equal_length=True)
        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="np")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins)
        )

    @require_torch
    def test_batch_feature_target_pt(self):
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target(equal_length=True)
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs}, tensor_type="pt")

        batch_features_input = processed_features[input_name]

        if len(batch_features_input.shape) < 3:
            batch_features_input = batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins)
        )

    @require_torch
    def test_padding_accepts_tensors_target_pt(self):
        feat_extract = self.feature_extraction_class(**self.feat_extract_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        input_name = feat_extract.model_input_names[0]

        processed_features = BatchFeature({input_name: speech_inputs})

        feat_extract.feature_size = feat_extract.num_mel_bins  # hack!

        input_np = feat_extract.pad(processed_features, padding="longest", return_tensors="np")[input_name]
        input_pt = feat_extract.pad(processed_features, padding="longest", return_tensors="pt")[input_name]

        self.assertTrue(abs(input_np.astype(np.float32).sum() - input_pt.numpy().astype(np.float32).sum()) < 1e-2)

    def test_attention_mask_target(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        input_lenghts = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})

        feat_extract.feature_size = feat_extract.num_mel_bins  # hack!

        processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
        self.assertIn("attention_mask", processed)
        self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)

    def test_attention_mask_with_truncation_target(self):
        feat_dict = self.feat_extract_dict
        feat_dict["return_attention_mask"] = True
        feat_extract = self.feature_extraction_class(**feat_dict)
        speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
        input_lenghts = [len(x) for x in speech_inputs]
        input_name = feat_extract.model_input_names[0]

        processed = BatchFeature({input_name: speech_inputs})
        max_length = min(input_lenghts)

        feat_extract.feature_size = feat_extract.num_mel_bins  # hack!

        processed_pad = feat_extract.pad(
            processed, padding="max_length", max_length=max_length, truncation=True, return_tensors="np"
        )
        self.assertIn("attention_mask", processed_pad)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape), [processed_pad[input_name].shape[0], max_length]
        )
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1).tolist(), [max_length for x in speech_inputs]
        )

    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").select(range(num_samples))[:num_samples]["audio"]

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

    def test_integration(self):
        # fmt: off
        EXPECTED_INPUT_VALUES = torch.tensor(
            [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
             3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
             2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
             4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
             7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
             4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]
        )
        # fmt: on

        input_speech = self._load_datasamples(1)
        feature_extractor = SpeechT5FeatureExtractor()
        input_values = feature_extractor(input_speech, return_tensors="pt").input_values
        self.assertTrue(torch.allclose(input_values[0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))

    def test_integration_target(self):
        # fmt: off
        EXPECTED_INPUT_VALUES = torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]
        )
        # fmt: on

        input_speech = self._load_datasamples(1)
        feature_extractor = SpeechT5FeatureExtractor()
        input_values = feature_extractor(audio_target=input_speech, return_tensors="pt").input_values
        self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))
