# Copyright 2023 The HuggingFace Team. 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 shutil
import tempfile
import unittest

from transformers import SeamlessM4TFeatureExtractor, SeamlessM4TProcessor
from transformers.models.seamless_m4t import (
    SeamlessM4TTokenizer,
    SeamlessM4TTokenizerFast,
)
from transformers.testing_utils import require_torch

from .test_feature_extraction_seamless_m4t import floats_list


@require_torch
class SeamlessM4TProcessorTest(unittest.TestCase):
    def setUp(self):
        self.checkpoint = "facebook/hf-seamless-m4t-medium"
        self.tmpdirname = tempfile.mkdtemp()

    def get_tokenizer(self, **kwargs):
        return SeamlessM4TTokenizer.from_pretrained(self.checkpoint, **kwargs)

    def get_feature_extractor(self, **kwargs):
        return SeamlessM4TFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def test_save_load_pretrained_default(self):
        tokenizer = self.get_tokenizer()
        feature_extractor = self.get_feature_extractor()

        processor = SeamlessM4TProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)

        processor.save_pretrained(self.tmpdirname)
        processor = SeamlessM4TProcessor.from_pretrained(self.tmpdirname)

        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
        tokenizer_instance = isinstance(processor.tokenizer, (SeamlessM4TTokenizerFast, SeamlessM4TTokenizer))
        self.assertTrue(tokenizer_instance)

        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
        self.assertIsInstance(processor.feature_extractor, SeamlessM4TFeatureExtractor)

    # Copied from test.models.whisper.test_processing_whisper.WhisperProcessorTest.test_feature_extractor with Whisper->SeamlessM4T
    def test_feature_extractor(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()

        processor = SeamlessM4TProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)

        raw_speech = floats_list((3, 1000))

        input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
        input_processor = processor(audio=raw_speech, return_tensors="np")

        for key in input_feat_extract:
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)

    # Copied from test.models.whisper.test_processing_whisper.WhisperProcessorTest.test_tokenizer with Whisper->SeamlessM4T
    def test_tokenizer(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()

        processor = SeamlessM4TProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)

        input_str = "This is a test string"

        encoded_processor = processor(text=input_str)

        encoded_tok = tokenizer(input_str)

        for key in encoded_tok:
            self.assertListEqual(encoded_tok[key], encoded_processor[key])

    # Copied from test.models.whisper.test_processing_whisper.WhisperProcessorTest.test_tokenizer_decode with Whisper->SeamlessM4T
    def test_tokenizer_decode(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()

        processor = SeamlessM4TProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)

        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)

        self.assertListEqual(decoded_tok, decoded_processor)