# Copyright 2025 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
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import itertools
import os
import random
import tempfile
import unittest
from typing import Optional, Sequence

import numpy as np
from parameterized import parameterized

from transformers.models.gemma3n import Gemma3nAudioFeatureExtractor
from transformers.testing_utils import (
    check_json_file_has_correct_format,
    require_torch,
)
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    pass

global_rng = random.Random()

MAX_LENGTH_FOR_TESTING = 512


def floats_list(shape, scale=1.0, rng=None):
    """Creates a random float32 tensor"""
    if rng is None:
        rng = global_rng

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

    return values


class Gemma3nAudioFeatureExtractionTester:
    def __init__(
        self,
        parent,
        batch_size=7,
        min_seq_length=400,
        max_seq_length=2000,
        feature_size: int = 128,
        sampling_rate: int = 16_000,
        padding_value: float = 0.0,
        return_attention_mask: bool = False,
        # ignore hop_length / frame_length for now, as ms -> length conversion causes issues with serialization tests
        # frame_length_ms: float = 32.0,
        # hop_length: float = 10.0,
        min_frequency: float = 125.0,
        max_frequency: float = 7600.0,
        preemphasis: float = 0.97,
        preemphasis_htk_flavor: bool = True,
        fft_overdrive: bool = True,
        dither: float = 0.0,
        input_scale_factor: float = 1.0,
        mel_floor: float = 1e-5,
        per_bin_mean: Optional[Sequence[float]] = None,
        per_bin_stddev: Optional[Sequence[float]] = None,
    ):
        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.sampling_rate = sampling_rate
        self.padding_value = padding_value
        self.return_attention_mask = return_attention_mask
        # ignore hop_length / frame_length for now, as ms -> length conversion causes issues with serialization tests
        # self.frame_length_ms = frame_length_ms
        # self.hop_length = hop_length
        self.min_frequency = min_frequency
        self.max_frequency = max_frequency
        self.preemphasis = preemphasis
        self.preemphasis_htk_flavor = preemphasis_htk_flavor
        self.fft_overdrive = fft_overdrive
        self.dither = dither
        self.input_scale_factor = input_scale_factor
        self.mel_floor = mel_floor
        self.per_bin_mean = per_bin_mean
        self.per_bin_stddev = per_bin_stddev

    def prepare_feat_extract_dict(self):
        return {
            "feature_size": self.feature_size,
            "sampling_rate": self.sampling_rate,
            "padding_value": self.padding_value,
            "return_attention_mask": self.return_attention_mask,
            "min_frequency": self.min_frequency,
            "max_frequency": self.max_frequency,
            "preemphasis": self.preemphasis,
            "preemphasis_htk_flavor": self.preemphasis_htk_flavor,
            "fft_overdrive": self.fft_overdrive,
            "dither": self.dither,
            "input_scale_factor": self.input_scale_factor,
            "mel_floor": self.mel_floor,
            "per_bin_mean": self.per_bin_mean,
            "per_bin_stddev": self.per_bin_stddev,
        }

    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


class Gemma3nAudioFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
    feature_extraction_class = Gemma3nAudioFeatureExtractor

    def setUp(self):
        self.feat_extract_tester = Gemma3nAudioFeatureExtractionTester(self)

    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()
        mel_1 = feat_extract_first.mel_filters
        mel_2 = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(mel_1, mel_2))
        self.assertEqual(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()
        mel_1 = feat_extract_first.mel_filters
        mel_2 = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(mel_1, mel_2))
        self.assertEqual(dict_first, dict_second)

    def test_feat_extract_from_pretrained_kwargs(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, feature_size=2 * self.feat_extract_dict["feature_size"]
            )

        mel_1 = feat_extract_first.mel_filters
        mel_2 = feat_extract_second.mel_filters
        self.assertTrue(2 * mel_1.shape[1] == mel_2.shape[1])

    @parameterized.expand(
        [
            ([floats_list((1, x))[0] for x in range(800, 1400, 200)],),
            ([floats_list((1, x))[0] for x in (800, 800, 800)],),
            ([floats_list((1, x))[0] for x in range(200, (MAX_LENGTH_FOR_TESTING + 500), 200)], True),
        ]
    )
    def test_call(self, audio_inputs, test_truncation=False):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs]

        input_features = feature_extractor(np_audio_inputs, padding="max_length", return_tensors="np").input_features
        self.assertTrue(input_features.ndim == 3)
        # input_features.shape should be (batch, num_frames, n_mels) ~= (batch, num_frames, feature_size)
        # 480_000 is the max_length that inputs are padded to. we use that to calculate num_frames
        expected_num_frames = (480_000 - feature_extractor.frame_length) // (feature_extractor.hop_length) + 1
        self.assertTrue(
            input_features.shape[-2] == expected_num_frames,
            f"no match: {input_features.shape[-1]} vs {expected_num_frames}",
        )
        self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)

        encoded_sequences_1 = feature_extractor(audio_inputs, return_tensors="np").input_features
        encoded_sequences_2 = feature_extractor(np_audio_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))

        if test_truncation:
            audio_inputs_truncated = [x[:MAX_LENGTH_FOR_TESTING] for x in audio_inputs]
            np_audio_inputs_truncated = [np.asarray(audio_input) for audio_input in audio_inputs_truncated]

            encoded_sequences_1 = feature_extractor(
                audio_inputs_truncated, max_length=MAX_LENGTH_FOR_TESTING, return_tensors="np"
            ).input_features
            encoded_sequences_2 = feature_extractor(
                np_audio_inputs_truncated, max_length=MAX_LENGTH_FOR_TESTING, 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"] = 0.00003  # approx. 1/32k

        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-6)
        # features are not too different
        self.assertTrue(np.abs(diff).mean() <= 1e-4)
        self.assertTrue(np.abs(diff).max() <= 5e-3)

    @require_torch
    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)