This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
"""Convert LUKE checkpoint."""

import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def convert_luke_checkpoint(checkpoint_path, metadata_path, entity_vocab_path, pytorch_dump_folder_path, model_size):
    # Load configuration defined in the metadata file
    with open(metadata_path) as metadata_file:
        metadata = json.load(metadata_file)
    config = LukeConfig(use_entity_aware_attention=True, **metadata["model_config"])

    # Load in the weights from the checkpoint_path
    state_dict = torch.load(checkpoint_path, map_location="cpu")

    # Load the entity vocab file
    entity_vocab = load_entity_vocab(entity_vocab_path)

    tokenizer = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])

    # Add special tokens to the token vocabulary for downstream tasks
    entity_token_1 = AddedToken("<ent>", lstrip=False, rstrip=False)
    entity_token_2 = AddedToken("<ent2>", lstrip=False, rstrip=False)
    tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_1, entity_token_2]})
    config.vocab_size += 2

    print(f"Saving tokenizer to {pytorch_dump_folder_path}")
    tokenizer.save_pretrained(pytorch_dump_folder_path)
    with open(os.path.join(pytorch_dump_folder_path, LukeTokenizer.vocab_files_names["entity_vocab_file"]), "w") as f:
        json.dump(entity_vocab, f)

    tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path)

    # Initialize the embeddings of the special tokens
    word_emb = state_dict["embeddings.word_embeddings.weight"]
    ent_emb = word_emb[tokenizer.convert_tokens_to_ids(["@"])[0]].unsqueeze(0)
    ent2_emb = word_emb[tokenizer.convert_tokens_to_ids(["#"])[0]].unsqueeze(0)
    state_dict["embeddings.word_embeddings.weight"] = torch.cat([word_emb, ent_emb, ent2_emb])

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers):
        for matrix_name in ["query.weight", "query.bias"]:
            prefix = f"encoder.layer.{layer_index}.attention.self."
            state_dict[prefix + "w2e_" + matrix_name] = state_dict[prefix + matrix_name]
            state_dict[prefix + "e2w_" + matrix_name] = state_dict[prefix + matrix_name]
            state_dict[prefix + "e2e_" + matrix_name] = state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    entity_emb = state_dict["entity_embeddings.entity_embeddings.weight"]
    entity_emb[entity_vocab["[MASK2]"]] = entity_emb[entity_vocab["[MASK]"]]

    model = LukeModel(config=config).eval()

    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    if not (len(missing_keys) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(f"Missing keys {', '.join(missing_keys)}. Expected only missing embeddings.position_ids")
    if not (all(key.startswith("entity_predictions") or key.startswith("lm_head") for key in unexpected_keys)):
        raise ValueError(
            "Unexpected keys"
            f" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions') or key.startswith('lm_head'))])}"
        )

    # Check outputs
    tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path, task="entity_classification")

    text = (
        "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
        " new world number one avoid a humiliating second- round exit at Wimbledon ."
    )
    span = (39, 42)
    encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")

    outputs = model(**encoding)

    # Verify word hidden states
    if model_size == "large":
        expected_shape = torch.Size((1, 42, 1024))
        expected_slice = torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]
        )
    else:  # base
        expected_shape = torch.Size((1, 42, 768))
        expected_slice = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"
        )
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        expected_shape = torch.Size((1, 1, 1024))
        expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]])
    else:  # base
        expected_shape = torch.Size((1, 1, 768))
        expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]])

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
            f" {expected_shape}"
        )
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print("Saving PyTorch model to {}".format(pytorch_dump_folder_path))
    model.save_pretrained(pytorch_dump_folder_path)


def load_entity_vocab(entity_vocab_path):
    entity_vocab = {}
    with open(entity_vocab_path, "r", encoding="utf-8") as f:
        for index, line in enumerate(f):
            title, _ = line.rstrip().split("\t")
            entity_vocab[title] = index

    return entity_vocab


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
    parser.add_argument(
        "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
    )
    parser.add_argument(
        "--entity_vocab_path",
        default=None,
        type=str,
        help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
    )
    parser.add_argument(
        "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
    )
    args = parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
"""Convert LUKE checkpoint."""

import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken


@torch.no_grad()
def convert_luke_checkpoint(checkpoint_path, metadata_path, entity_vocab_path, pytorch_dump_folder_path, model_size):
    # Load configuration defined in the metadata file
    with open(metadata_path) as metadata_file:
        metadata = json.load(metadata_file)
    config = LukeConfig(use_entity_aware_attention=True, **metadata["model_config"])

    # Load in the weights from the checkpoint_path
    state_dict = torch.load(checkpoint_path, map_location="cpu")

    # Load the entity vocab file
    entity_vocab = load_entity_vocab(entity_vocab_path)

    tokenizer = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"])

    # Add special tokens to the token vocabulary for downstream tasks
    entity_token_1 = AddedToken("<ent>", lstrip=False, rstrip=False)
    entity_token_2 = AddedToken("<ent2>", lstrip=False, rstrip=False)
    tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_1, entity_token_2]})
    config.vocab_size += 2

    print(f"Saving tokenizer to {pytorch_dump_folder_path}")
    tokenizer.save_pretrained(pytorch_dump_folder_path)
    with open(os.path.join(pytorch_dump_folder_path, LukeTokenizer.vocab_files_names["entity_vocab_file"]), "w") as f:
        json.dump(entity_vocab, f)

    tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path)

    # Initialize the embeddings of the special tokens
    word_emb = state_dict["embeddings.word_embeddings.weight"]
    ent_emb = word_emb[tokenizer.convert_tokens_to_ids(["@"])[0]].unsqueeze(0)
    ent2_emb = word_emb[tokenizer.convert_tokens_to_ids(["#"])[0]].unsqueeze(0)
    state_dict["embeddings.word_embeddings.weight"] = torch.cat([word_emb, ent_emb, ent2_emb])

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers):
        for matrix_name in ["query.weight", "query.bias"]:
            prefix = f"encoder.layer.{layer_index}.attention.self."
            state_dict[prefix + "w2e_" + matrix_name] = state_dict[prefix + matrix_name]
            state_dict[prefix + "e2w_" + matrix_name] = state_dict[prefix + matrix_name]
            state_dict[prefix + "e2e_" + matrix_name] = state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    entity_emb = state_dict["entity_embeddings.entity_embeddings.weight"]
    entity_emb[entity_vocab["[MASK2]"]] = entity_emb[entity_vocab["[MASK]"]]

    model = LukeModel(config=config).eval()

    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    if not (len(missing_keys) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(f"Missing keys {', '.join(missing_keys)}. Expected only missing embeddings.position_ids")
    if not (all(key.startswith("entity_predictions") or key.startswith("lm_head") for key in unexpected_keys)):
        raise ValueError(
            "Unexpected keys"
            f" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions') or key.startswith('lm_head'))])}"
        )

    # Check outputs
    tokenizer = LukeTokenizer.from_pretrained(pytorch_dump_folder_path, task="entity_classification")

    text = (
        "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
        " new world number one avoid a humiliating second- round exit at Wimbledon ."
    )
    span = (39, 42)
    encoding = tokenizer(text, entity_spans=[span], add_prefix_space=True, return_tensors="pt")

    outputs = model(**encoding)

    # Verify word hidden states
    if model_size == "large":
        expected_shape = torch.Size((1, 42, 1024))
        expected_slice = torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]
        )
    else:  # base
        expected_shape = torch.Size((1, 42, 768))
        expected_slice = torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]])

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"
        )
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        expected_shape = torch.Size((1, 1, 1024))
        expected_slice = torch.tensor([[0.0466, -0.0106, -0.0179]])
    else:  # base
        expected_shape = torch.Size((1, 1, 768))
        expected_slice = torch.tensor([[0.1457, 0.1044, 0.0174]])

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
            f" {expected_shape}"
        )
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3], expected_slice, atol=1e-4):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print("Saving PyTorch model to {}".format(pytorch_dump_folder_path))
    model.save_pretrained(pytorch_dump_folder_path)


def load_entity_vocab(entity_vocab_path):
    entity_vocab = {}
    with open(entity_vocab_path, "r", encoding="utf-8") as f:
        for index, line in enumerate(f):
            title, _ = line.rstrip().split("\t")
            entity_vocab[title] = index

    return entity_vocab


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
    parser.add_argument(
        "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
    )
    parser.add_argument(
        "--entity_vocab_path",
        default=None,
        type=str,
        help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
    )
    parser.add_argument(
        "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
    )
    args = parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )
