# What does this PR do?

This PR adds the UnivNet GAN vocoder model ([paper](https://arxiv.org/pdf/2106.07889.pdf), [code](https://github.com/mindslab-ai/univnet)) to `transformers`, which is the vocoder used in the Tortoise TTS text-to-speech model ([paper](https://arxiv.org/pdf/2305.07243.pdf), [code](https://github.com/neonbjb/tortoise-tts)) which is currently being integrated into `diffusers`. See [this issue](https://github.com/huggingface/diffusers/issues/3891) in `diffusers`.

![univnet_model_architecture](https://github.com/huggingface/transformers/assets/58458699/8a33190a-cd52-4e81-a6ed-2fc921b9f86f)

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sanchit-gandhi 
@susnato 

Add UnivNet Vocoder Model for Tortoise TTS Diffusers Integration. # What does this PR do?

This PR adds the UnivNet GAN vocoder model ([paper](https://arxiv.org/pdf/2106.07889.pdf), [code](https://github.com/mindslab-ai/univnet)) to `transformers`, which is the vocoder used in the Tortoise TTS text-to-speech model ([paper](https://arxiv.org/pdf/2305.07243.pdf), [code](https://github.com/neonbjb/tortoise-tts)) which is currently being integrated into `diffusers`. See [this issue](https://github.com/huggingface/diffusers/issues/3891) in `diffusers`.

![univnet_model_architecture](https://github.com/huggingface/transformers/assets/58458699/8a33190a-cd52-4e81-a6ed-2fc921b9f86f)

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@sanchit-gandhi 
@susnato 

# coding=utf-8
# Copyright 2020 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 os
import pickle
import unittest
from collections import Counter, OrderedDict

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class TransfoXLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = TransfoXLTokenizer
    test_rust_tokenizer = False
    test_seq2seq = False

    def setUp(self):
        super().setUp()

        vocab_tokens = [
            "<unk>",
            "[CLS]",
            "[SEP]",
            "want",
            "unwanted",
            "wa",
            "un",
            "running",
            ",",
            "low",
            "l",
        ]
        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

        saved_dict = {
            "eos_idx": 0,
            "min_freq": 0,
            "vocab_file": None,
            "counter": Counter(["welcome home"]),
            "sym2idx": OrderedDict([("<eos>", 0), ("welcome", 1), ("home", 2)]),
            "delimiter": None,
            "idx2sym": ["<eos>", "welcome", "home"],
            "max_size": None,
            "lower_case": False,
            "special": ["<eos>"],
        }
        self.pretrained_vocab_file = os.path.join(
            self.tmpdirname, "mock_folder", VOCAB_FILES_NAMES["pretrained_vocab_file"]
        )
        os.makedirs(os.path.dirname(self.pretrained_vocab_file), exist_ok=True)
        with open(self.pretrained_vocab_file, "wb") as f:
            pickle.dump(saved_dict, f)

    def get_tokenizer(self, **kwargs):
        kwargs["lower_case"] = True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **kwargs)

    def get_input_output_texts(self, tokenizer):
        input_text = "<unk> UNwanted , running"
        output_text = "<unk> unwanted, running"
        return input_text, output_text

    def test_full_tokenizer(self):
        tokenizer = TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=True)

        tokens = tokenizer.tokenize("<unk> UNwanted , running")
        self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])

        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [0, 4, 8, 7])

    def test_full_tokenizer_lower(self):
        tokenizer = TransfoXLTokenizer(lower_case=True)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "), ["hello", "!", "how", "are", "you", "?"]
        )

    def test_full_tokenizer_no_lower(self):
        tokenizer = TransfoXLTokenizer(lower_case=False)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
        )

    def test_full_tokenizer_moses_numbers(self):
        tokenizer = TransfoXLTokenizer(lower_case=False)
        text_in = "Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
        tokens_out = [
            "Hello",
            "(",
            "bracket",
            ")",
            "and",
            "side",
            "@-@",
            "scrolled",
            "[",
            "and",
            "]",
            "Henry",
            "'s",
            "$",
            "5",
            "@,@",
            "000",
            "with",
            "3",
            "@.@",
            "34",
            "m",
            ".",
            "What",
            "'s",
            "up",
            "!",
            "?",
        ]

        self.assertListEqual(tokenizer.tokenize(text_in), tokens_out)

        self.assertEqual(tokenizer.convert_tokens_to_string(tokens_out), text_in)

    def test_move_added_token(self):
        tokenizer = self.get_tokenizer()
        original_len = len(tokenizer)

        tokenizer.add_tokens(["new1", "new2"])
        tokenizer.move_added_token("new1", 1)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(tokenizer), original_len + 2)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode("new1"), [1])
        self.assertEqual(tokenizer.decode([1]), "new1")

    def test_from_pretrained_vocab_file(self):
        tokenizer = TransfoXLTokenizer.from_pretrained(os.path.join(self.tmpdirname, "mock_folder"))
        sentence = "welcome home"
        self.assertEqual(tokenizer.decode(tokenizer.encode(sentence)), sentence)

# coding=utf-8
# Copyright 2020 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 os
import pickle
import unittest
from collections import Counter, OrderedDict

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class TransfoXLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = TransfoXLTokenizer
    test_rust_tokenizer = False
    test_seq2seq = False

    def setUp(self):
        super().setUp()

        vocab_tokens = [
            "<unk>",
            "[CLS]",
            "[SEP]",
            "want",
            "unwanted",
            "wa",
            "un",
            "running",
            ",",
            "low",
            "l",
        ]
        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
            vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

        saved_dict = {
            "eos_idx": 0,
            "min_freq": 0,
            "vocab_file": None,
            "counter": Counter(["welcome home"]),
            "sym2idx": OrderedDict([("<eos>", 0), ("welcome", 1), ("home", 2)]),
            "delimiter": None,
            "idx2sym": ["<eos>", "welcome", "home"],
            "max_size": None,
            "lower_case": False,
            "special": ["<eos>"],
        }
        self.pretrained_vocab_file = os.path.join(
            self.tmpdirname, "mock_folder", VOCAB_FILES_NAMES["pretrained_vocab_file"]
        )
        os.makedirs(os.path.dirname(self.pretrained_vocab_file), exist_ok=True)
        with open(self.pretrained_vocab_file, "wb") as f:
            pickle.dump(saved_dict, f)

    def get_tokenizer(self, **kwargs):
        kwargs["lower_case"] = True
        return TransfoXLTokenizer.from_pretrained(self.tmpdirname, **kwargs)

    def get_input_output_texts(self, tokenizer):
        input_text = "<unk> UNwanted , running"
        output_text = "<unk> unwanted, running"
        return input_text, output_text

    def test_full_tokenizer(self):
        tokenizer = TransfoXLTokenizer(vocab_file=self.vocab_file, lower_case=True)

        tokens = tokenizer.tokenize("<unk> UNwanted , running")
        self.assertListEqual(tokens, ["<unk>", "unwanted", ",", "running"])

        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [0, 4, 8, 7])

    def test_full_tokenizer_lower(self):
        tokenizer = TransfoXLTokenizer(lower_case=True)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "), ["hello", "!", "how", "are", "you", "?"]
        )

    def test_full_tokenizer_no_lower(self):
        tokenizer = TransfoXLTokenizer(lower_case=False)

        self.assertListEqual(
            tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "), ["HeLLo", "!", "how", "Are", "yoU", "?"]
        )

    def test_full_tokenizer_moses_numbers(self):
        tokenizer = TransfoXLTokenizer(lower_case=False)
        text_in = "Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
        tokens_out = [
            "Hello",
            "(",
            "bracket",
            ")",
            "and",
            "side",
            "@-@",
            "scrolled",
            "[",
            "and",
            "]",
            "Henry",
            "'s",
            "$",
            "5",
            "@,@",
            "000",
            "with",
            "3",
            "@.@",
            "34",
            "m",
            ".",
            "What",
            "'s",
            "up",
            "!",
            "?",
        ]

        self.assertListEqual(tokenizer.tokenize(text_in), tokens_out)

        self.assertEqual(tokenizer.convert_tokens_to_string(tokens_out), text_in)

    def test_move_added_token(self):
        tokenizer = self.get_tokenizer()
        original_len = len(tokenizer)

        tokenizer.add_tokens(["new1", "new2"])
        tokenizer.move_added_token("new1", 1)

        # Check that moved token is not copied (duplicate)
        self.assertEqual(len(tokenizer), original_len + 2)
        # Check that token is moved to specified id
        self.assertEqual(tokenizer.encode("new1"), [1])
        self.assertEqual(tokenizer.decode([1]), "new1")

    def test_from_pretrained_vocab_file(self):
        tokenizer = TransfoXLTokenizer.from_pretrained(os.path.join(self.tmpdirname, "mock_folder"))
        sentence = "welcome home"
        self.assertEqual(tokenizer.decode(tokenizer.encode(sentence)), sentence)
