# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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## 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.
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      [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.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## 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.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# 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 warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


logger = logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class TFTrainingArguments(TrainingArguments):
    """
    TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
    itself**.

    Using [`HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        output_dir (`str`):
            The output directory where the model predictions and checkpoints will be written.
        overwrite_output_dir (`bool`, *optional*, defaults to `False`):
            If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
            points to a checkpoint directory.
        do_train (`bool`, *optional*, defaults to `False`):
            Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
            by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_eval (`bool`, *optional*):
            Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is
            different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
            training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_predict (`bool`, *optional*, defaults to `False`):
            Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
            intended to be used by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
            The evaluation strategy to adopt during training. Possible values are:

                - `"no"`: No evaluation is done during training.
                - `"steps"`: Evaluation is done (and logged) every `eval_steps`.
                - `"epoch"`: Evaluation is done at the end of each epoch.

        per_device_train_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/TPU core/CPU for training.
        per_device_eval_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/TPU core/CPU for evaluation.
        gradient_accumulation_steps (`int`, *optional*, defaults to 1):
            Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

            <Tip warning={true}>

            When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
            evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.

            </Tip>

        learning_rate (`float`, *optional*, defaults to 5e-5):
            The initial learning rate for Adam.
        weight_decay (`float`, *optional*, defaults to 0):
            The weight decay to apply (if not zero).
        adam_beta1 (`float`, *optional*, defaults to 0.9):
            The beta1 hyperparameter for the Adam optimizer.
        adam_beta2 (`float`, *optional*, defaults to 0.999):
            The beta2 hyperparameter for the Adam optimizer.
        adam_epsilon (`float`, *optional*, defaults to 1e-8):
            The epsilon hyperparameter for the Adam optimizer.
        max_grad_norm (`float`, *optional*, defaults to 1.0):
            Maximum gradient norm (for gradient clipping).
        num_train_epochs(`float`, *optional*, defaults to 3.0):
            Total number of training epochs to perform.
        max_steps (`int`, *optional*, defaults to -1):
            If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
        warmup_ratio (`float`, *optional*, defaults to 0.0):
            Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
        warmup_steps (`int`, *optional*, defaults to 0):
            Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
        logging_dir (`str`, *optional*):
            [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
            *runs/**CURRENT_DATETIME_HOSTNAME***.
        logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
            The logging strategy to adopt during training. Possible values are:

                - `"no"`: No logging is done during training.
                - `"epoch"`: Logging is done at the end of each epoch.
                - `"steps"`: Logging is done every `logging_steps`.

        logging_first_step (`bool`, *optional*, defaults to `False`):
            Whether to log and evaluate the first `global_step` or not.
        logging_steps (`int`, *optional*, defaults to 500):
            Number of update steps between two logs if `logging_strategy="steps"`.
        save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
            The checkpoint save strategy to adopt during training. Possible values are:

                - `"no"`: No save is done during training.
                - `"epoch"`: Save is done at the end of each epoch.
                - `"steps"`: Save is done every `save_steps`.

        save_steps (`int`, *optional*, defaults to 500):
            Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
        save_total_limit (`int`, *optional*):
            If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
            `output_dir`.
        no_cuda (`bool`, *optional*, defaults to `False`):
            Whether to not use CUDA even when it is available or not.
        seed (`int`, *optional*, defaults to 42):
            Random seed that will be set at the beginning of training.
        fp16 (`bool`, *optional*, defaults to `False`):
            Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
        fp16_opt_level (`str`, *optional*, defaults to 'O1'):
            For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
            the [Apex documentation](https://nvidia.github.io/apex/amp).
        local_rank (`int`, *optional*, defaults to -1):
            During distributed training, the rank of the process.
        tpu_num_cores (`int`, *optional*):
            When training on TPU, the number of TPU cores (automatically passed by launcher script).
        debug (`bool`, *optional*, defaults to `False`):
            Whether to activate the trace to record computation graphs and profiling information or not.
        dataloader_drop_last (`bool`, *optional*, defaults to `False`):
            Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
            or not.
        eval_steps (`int`, *optional*, defaults to 1000):
            Number of update steps before two evaluations.
        past_index (`int`, *optional*, defaults to -1):
            Some models like [TransformerXL](../model_doc/transformerxl) or :doc*XLNet <../model_doc/xlnet>* can make
            use of the past hidden states for their predictions. If this argument is set to a positive int, the
            `Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model at
            the next training step under the keyword argument `mems`.
        tpu_name (`str`, *optional*):
            The name of the TPU the process is running on.
        tpu_zone (`str`, *optional*):
            The zone of the TPU the process is running on. If not specified, we will attempt to automatically detect
            from metadata.
        gcp_project (`str`, *optional*):
            Google Cloud Project name for the Cloud TPU-enabled project. If not specified, we will attempt to
            automatically detect from metadata.
        run_name (`str`, *optional*):
            A descriptor for the run. Notably used for wandb logging.
        xla (`bool`, *optional*):
            Whether to activate the XLA compilation or not.
    """

    framework = "tf"
    tpu_name: Optional[str] = field(
        default=None,
        metadata={"help": "Name of TPU"},
    )

    tpu_zone: Optional[str] = field(
        default=None,
        metadata={"help": "Zone of TPU"},
    )

    gcp_project: Optional[str] = field(
        default=None,
        metadata={"help": "Name of Cloud TPU-enabled project"},
    )

    poly_power: float = field(
        default=1.0,
        metadata={"help": "Power for the Polynomial decay LR scheduler."},
    )

    xla: bool = field(default=False, metadata={"help": "Whether to activate the XLA compilation or not"})

    @cached_property
    def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", int]:
        requires_backends(self, ["tf"])
        logger.info("Tensorflow: setting up strategy")

        gpus = tf.config.list_physical_devices("GPU")

        # Set to float16 at first
        if self.fp16:
            tf.keras.mixed_precision.set_global_policy("mixed_float16")

        if self.no_cuda:
            strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
        else:
            try:
                if self.tpu_name:
                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver(
                        self.tpu_name, zone=self.tpu_zone, project=self.gcp_project
                    )
                else:
                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                if self.tpu_name:
                    raise RuntimeError(f"Couldn't connect to TPU {self.tpu_name}!")
                else:
                    tpu = None

            if tpu:
                # Set to bfloat16 in case of TPU
                if self.fp16:
                    tf.keras.mixed_precision.set_global_policy("mixed_bfloat16")

                tf.config.experimental_connect_to_cluster(tpu)
                tf.tpu.experimental.initialize_tpu_system(tpu)

                strategy = tf.distribute.TPUStrategy(tpu)

            elif len(gpus) == 0:
                strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
            elif len(gpus) == 1:
                strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
            elif len(gpus) > 1:
                # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                strategy = tf.distribute.MirroredStrategy()
            else:
                raise ValueError("Cannot find the proper strategy, please check your environment properties.")

        return strategy

    @property
    def strategy(self) -> "tf.distribute.Strategy":
        """
        The strategy used for distributed training.
        """
        requires_backends(self, ["tf"])
        return self._setup_strategy

    @property
    def n_replicas(self) -> int:
        """
        The number of replicas (CPUs, GPUs or TPU cores) used in this training.
        """
        requires_backends(self, ["tf"])
        return self._setup_strategy.num_replicas_in_sync

    @property
    def should_log(self):
        """
        Whether or not the current process should produce log.
        """
        return False  # TF Logging is handled by Keras not the Trainer

    @property
    def train_batch_size(self) -> int:
        """
        The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
        """
        if self.per_gpu_train_batch_size:
            logger.warning(
                "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                "version. Using `--per_device_train_batch_size` is preferred."
            )
        per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
        return per_device_batch_size * self.n_replicas

    @property
    def eval_batch_size(self) -> int:
        """
        The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
        """
        if self.per_gpu_eval_batch_size:
            logger.warning(
                "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                "version. Using `--per_device_eval_batch_size` is preferred."
            )
        per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
        return per_device_batch_size * self.n_replicas

    @property
    def n_gpu(self) -> int:
        """
        The number of replicas (CPUs, GPUs or TPU cores) used in this training.
        """
        requires_backends(self, ["tf"])
        warnings.warn(
            "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
            FutureWarning,
        )
        return self._setup_strategy.num_replicas_in_sync

# 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 warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


logger = logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class TFTrainingArguments(TrainingArguments):
    """
    TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
    itself**.

    Using [`HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        output_dir (`str`):
            The output directory where the model predictions and checkpoints will be written.
        overwrite_output_dir (`bool`, *optional*, defaults to `False`):
            If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
            points to a checkpoint directory.
        do_train (`bool`, *optional*, defaults to `False`):
            Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
            by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_eval (`bool`, *optional*):
            Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is
            different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
            training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        do_predict (`bool`, *optional*, defaults to `False`):
            Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
            intended to be used by your training/evaluation scripts instead. See the [example
            scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
        evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
            The evaluation strategy to adopt during training. Possible values are:

                - `"no"`: No evaluation is done during training.
                - `"steps"`: Evaluation is done (and logged) every `eval_steps`.
                - `"epoch"`: Evaluation is done at the end of each epoch.

        per_device_train_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/TPU core/CPU for training.
        per_device_eval_batch_size (`int`, *optional*, defaults to 8):
            The batch size per GPU/TPU core/CPU for evaluation.
        gradient_accumulation_steps (`int`, *optional*, defaults to 1):
            Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

            <Tip warning={true}>

            When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
            evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.

            </Tip>

        learning_rate (`float`, *optional*, defaults to 5e-5):
            The initial learning rate for Adam.
        weight_decay (`float`, *optional*, defaults to 0):
            The weight decay to apply (if not zero).
        adam_beta1 (`float`, *optional*, defaults to 0.9):
            The beta1 hyperparameter for the Adam optimizer.
        adam_beta2 (`float`, *optional*, defaults to 0.999):
            The beta2 hyperparameter for the Adam optimizer.
        adam_epsilon (`float`, *optional*, defaults to 1e-8):
            The epsilon hyperparameter for the Adam optimizer.
        max_grad_norm (`float`, *optional*, defaults to 1.0):
            Maximum gradient norm (for gradient clipping).
        num_train_epochs(`float`, *optional*, defaults to 3.0):
            Total number of training epochs to perform.
        max_steps (`int`, *optional*, defaults to -1):
            If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
        warmup_ratio (`float`, *optional*, defaults to 0.0):
            Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
        warmup_steps (`int`, *optional*, defaults to 0):
            Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
        logging_dir (`str`, *optional*):
            [TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
            *runs/**CURRENT_DATETIME_HOSTNAME***.
        logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
            The logging strategy to adopt during training. Possible values are:

                - `"no"`: No logging is done during training.
                - `"epoch"`: Logging is done at the end of each epoch.
                - `"steps"`: Logging is done every `logging_steps`.

        logging_first_step (`bool`, *optional*, defaults to `False`):
            Whether to log and evaluate the first `global_step` or not.
        logging_steps (`int`, *optional*, defaults to 500):
            Number of update steps between two logs if `logging_strategy="steps"`.
        save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
            The checkpoint save strategy to adopt during training. Possible values are:

                - `"no"`: No save is done during training.
                - `"epoch"`: Save is done at the end of each epoch.
                - `"steps"`: Save is done every `save_steps`.

        save_steps (`int`, *optional*, defaults to 500):
            Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
        save_total_limit (`int`, *optional*):
            If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
            `output_dir`.
        no_cuda (`bool`, *optional*, defaults to `False`):
            Whether to not use CUDA even when it is available or not.
        seed (`int`, *optional*, defaults to 42):
            Random seed that will be set at the beginning of training.
        fp16 (`bool`, *optional*, defaults to `False`):
            Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.
        fp16_opt_level (`str`, *optional*, defaults to 'O1'):
            For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
            the [Apex documentation](https://nvidia.github.io/apex/amp).
        local_rank (`int`, *optional*, defaults to -1):
            During distributed training, the rank of the process.
        tpu_num_cores (`int`, *optional*):
            When training on TPU, the number of TPU cores (automatically passed by launcher script).
        debug (`bool`, *optional*, defaults to `False`):
            Whether to activate the trace to record computation graphs and profiling information or not.
        dataloader_drop_last (`bool`, *optional*, defaults to `False`):
            Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
            or not.
        eval_steps (`int`, *optional*, defaults to 1000):
            Number of update steps before two evaluations.
        past_index (`int`, *optional*, defaults to -1):
            Some models like [TransformerXL](../model_doc/transformerxl) or :doc*XLNet <../model_doc/xlnet>* can make
            use of the past hidden states for their predictions. If this argument is set to a positive int, the
            `Trainer` will use the corresponding output (usually index 2) as the past state and feed it to the model at
            the next training step under the keyword argument `mems`.
        tpu_name (`str`, *optional*):
            The name of the TPU the process is running on.
        tpu_zone (`str`, *optional*):
            The zone of the TPU the process is running on. If not specified, we will attempt to automatically detect
            from metadata.
        gcp_project (`str`, *optional*):
            Google Cloud Project name for the Cloud TPU-enabled project. If not specified, we will attempt to
            automatically detect from metadata.
        run_name (`str`, *optional*):
            A descriptor for the run. Notably used for wandb logging.
        xla (`bool`, *optional*):
            Whether to activate the XLA compilation or not.
    """

    framework = "tf"
    tpu_name: Optional[str] = field(
        default=None,
        metadata={"help": "Name of TPU"},
    )

    tpu_zone: Optional[str] = field(
        default=None,
        metadata={"help": "Zone of TPU"},
    )

    gcp_project: Optional[str] = field(
        default=None,
        metadata={"help": "Name of Cloud TPU-enabled project"},
    )

    poly_power: float = field(
        default=1.0,
        metadata={"help": "Power for the Polynomial decay LR scheduler."},
    )

    xla: bool = field(default=False, metadata={"help": "Whether to activate the XLA compilation or not"})

    @cached_property
    def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", int]:
        requires_backends(self, ["tf"])
        logger.info("Tensorflow: setting up strategy")

        gpus = tf.config.list_physical_devices("GPU")

        # Set to float16 at first
        if self.fp16:
            tf.keras.mixed_precision.set_global_policy("mixed_float16")

        if self.no_cuda:
            strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
        else:
            try:
                if self.tpu_name:
                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver(
                        self.tpu_name, zone=self.tpu_zone, project=self.gcp_project
                    )
                else:
                    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                if self.tpu_name:
                    raise RuntimeError(f"Couldn't connect to TPU {self.tpu_name}!")
                else:
                    tpu = None

            if tpu:
                # Set to bfloat16 in case of TPU
                if self.fp16:
                    tf.keras.mixed_precision.set_global_policy("mixed_bfloat16")

                tf.config.experimental_connect_to_cluster(tpu)
                tf.tpu.experimental.initialize_tpu_system(tpu)

                strategy = tf.distribute.TPUStrategy(tpu)

            elif len(gpus) == 0:
                strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
            elif len(gpus) == 1:
                strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
            elif len(gpus) > 1:
                # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                strategy = tf.distribute.MirroredStrategy()
            else:
                raise ValueError("Cannot find the proper strategy, please check your environment properties.")

        return strategy

    @property
    def strategy(self) -> "tf.distribute.Strategy":
        """
        The strategy used for distributed training.
        """
        requires_backends(self, ["tf"])
        return self._setup_strategy

    @property
    def n_replicas(self) -> int:
        """
        The number of replicas (CPUs, GPUs or TPU cores) used in this training.
        """
        requires_backends(self, ["tf"])
        return self._setup_strategy.num_replicas_in_sync

    @property
    def should_log(self):
        """
        Whether or not the current process should produce log.
        """
        return False  # TF Logging is handled by Keras not the Trainer

    @property
    def train_batch_size(self) -> int:
        """
        The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
        """
        if self.per_gpu_train_batch_size:
            logger.warning(
                "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                "version. Using `--per_device_train_batch_size` is preferred."
            )
        per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
        return per_device_batch_size * self.n_replicas

    @property
    def eval_batch_size(self) -> int:
        """
        The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
        """
        if self.per_gpu_eval_batch_size:
            logger.warning(
                "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                "version. Using `--per_device_eval_batch_size` is preferred."
            )
        per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
        return per_device_batch_size * self.n_replicas

    @property
    def n_gpu(self) -> int:
        """
        The number of replicas (CPUs, GPUs or TPU cores) used in this training.
        """
        requires_backends(self, ["tf"])
        warnings.warn(
            "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
            FutureWarning,
        )
        return self._setup_strategy.num_replicas_in_sync
