# 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).
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      Pull Request section?
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      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?

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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).

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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.
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<!-- 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

 -->

# coding=utf-8
# Copyright 2021 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.
""" PyTorch - Flax general utilities."""


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


logger = logging.get_logger(__name__)


#####################
# PyTorch => Flax #
#####################


def load_pytorch_checkpoint_in_flax_state_dict(
    flax_model, pytorch_checkpoint_path, is_sharded, allow_missing_keys=False
):
    """Load pytorch checkpoints in a flax model"""
    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    if not is_sharded:
        pt_path = os.path.abspath(pytorch_checkpoint_path)
        logger.info(f"Loading PyTorch weights from {pt_path}")

        pt_state_dict = torch.load(pt_path, map_location="cpu")
        logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")

        flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        flax_state_dict = convert_pytorch_sharded_state_dict_to_flax(pytorch_checkpoint_path, flax_model)
    return flax_state_dict


def rename_key_and_reshape_tensor(
    pt_tuple_key: Tuple[str],
    pt_tensor: np.ndarray,
    random_flax_state_dict: Dict[str, jnp.ndarray],
    model_prefix: str,
) -> (Tuple[str], np.ndarray):
    """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""

    def is_key_or_prefix_key_in_dict(key: Tuple[str]) -> bool:
        """Checks if `key` of `(prefix,) + key` is in random_flax_state_dict"""
        return len(set(random_flax_state_dict) & {key, (model_prefix,) + key}) > 0

    # layer norm
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(renamed_pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("mean",)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("var",)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(renamed_pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        pt_tensor = pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    name = None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        name = pt_tuple_key[-2] + "_g"
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        name = pt_tuple_key[-2] + "_v"
    if name is not None:
        renamed_pt_tuple_key = pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
    # convert pytorch tensor to numpy
    # numpy currently does not support bfloat16, need to go over float32 in this case to not lose precision
    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    weight_dtypes = {k: v.dtype for k, v in pt_state_dict.items()}
    pt_state_dict = {
        k: v.numpy() if not v.dtype == torch.bfloat16 else v.float().numpy() for k, v in pt_state_dict.items()
    }

    model_prefix = flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        flax_model_params = flax_model.params["params"]
    else:
        flax_model_params = flax_model.params
    random_flax_state_dict = flatten_dict(flax_model_params)

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        flax_batch_stats = flatten_dict(flax_model.params["batch_stats"])
        random_flax_state_dict.update(flax_batch_stats)

    flax_state_dict = {}

    load_model_with_head_into_base_model = (model_prefix not in flax_model_params) and (
        model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
    )
    load_base_model_into_model_with_head = (model_prefix in flax_model_params) and (
        model_prefix not in {k.split(".")[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        pt_tuple_key = tuple(pt_key.split("."))
        is_bfloat_16 = weight_dtypes[pt_key] == torch.bfloat16

        # remove base model prefix if necessary
        has_base_model_prefix = pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            pt_tuple_key = pt_tuple_key[1:]

        # Correctly rename weight parameters
        flax_key, flax_tensor = rename_key_and_reshape_tensor(
            pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
        )

        # add model prefix if necessary
        require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            flax_key = (model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                    f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(flax_key, None)
                continue

            # also add unexpected weight so that warning is thrown
            flax_state_dict[("params",) + flax_key] = (
                jnp.asarray(flax_tensor) if not is_bfloat_16 else jnp.asarray(flax_tensor, dtype=jnp.bfloat16)
            )

        else:
            # also add unexpected weight so that warning is thrown
            flax_state_dict[flax_key] = (
                jnp.asarray(flax_tensor) if not is_bfloat_16 else jnp.asarray(flax_tensor, dtype=jnp.bfloat16)
            )

    return unflatten_dict(flax_state_dict)


############################
# Sharded Pytorch => Flax #
############################


def convert_pytorch_sharded_state_dict_to_flax(shard_filenames, flax_model):
    import torch

    # Load the index
    flax_state_dict = {}
    for shard_file in shard_filenames:
        # load using msgpack utils
        pt_state_dict = torch.load(shard_file)
        pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}

        model_prefix = flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            flax_model_params = flax_model.params["params"]

            random_flax_state_dict = flatten_dict(flax_model_params)
            random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]))
        else:
            flax_model_params = flax_model.params
            random_flax_state_dict = flatten_dict(flax_model_params)

        load_model_with_head_into_base_model = (model_prefix not in flax_model_params) and (
            model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
        )
        load_base_model_into_model_with_head = (model_prefix in flax_model_params) and (
            model_prefix not in {k.split(".")[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            pt_tuple_key = tuple(pt_key.split("."))

            # remove base model prefix if necessary
            has_base_model_prefix = pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                pt_tuple_key = pt_tuple_key[1:]

            # Correctly rename weight parameters
            flax_key, flax_tensor = rename_key_and_reshape_tensor(
                pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
            )
            # add model prefix if necessary
            require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                flax_key = (model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
                    )

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
                    continue
                if "var" in flax_key[-1]:
                    flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(flax_key, None)
                    continue

                # also add unexpected weight so that warning is thrown
                flax_state_dict[("params",) + flax_key] = jnp.asarray(flax_tensor)

            else:
                # also add unexpected weight so that warning is thrown
                flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
    return unflatten_dict(flax_state_dict)


#####################
# Flax => PyTorch #
#####################


def load_flax_checkpoint_in_pytorch_model(model, flax_checkpoint_path):
    """Load flax checkpoints in a PyTorch model"""
    flax_checkpoint_path = os.path.abspath(flax_checkpoint_path)
    logger.info(f"Loading Flax weights from {flax_checkpoint_path}")

    # import correct flax class
    flax_cls = getattr(transformers, "Flax" + model.__class__.__name__)

    # load flax weight dict
    with open(flax_checkpoint_path, "rb") as state_f:
        try:
            flax_state_dict = from_bytes(flax_cls, state_f.read())
        except UnpicklingError:
            raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. ")

    return load_flax_weights_in_pytorch_model(model, flax_state_dict)


def load_flax_weights_in_pytorch_model(pt_model, flax_state):
    """Load flax checkpoints in a PyTorch model"""

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    # check if we have bf16 weights
    is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
    if any(is_type_bf16):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
            "before loading those in PyTorch model."
        )
        flax_state = jax.tree_util.tree_map(
            lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
        )

    flax_state_dict = flatten_dict(flax_state)
    pt_model_dict = pt_model.state_dict()

    load_model_with_head_into_base_model = (pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split(".")[0] for k in pt_model_dict.keys()}
    )
    load_base_model_into_model_with_head = (pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split(".")[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    unexpected_keys = []
    missing_keys = set(pt_model_dict.keys())

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        has_base_model_prefix = flax_key_tuple[0] == pt_model.base_model_prefix
        require_base_model_prefix = ".".join((pt_model.base_model_prefix,) + flax_key_tuple) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            flax_key_tuple = flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            flax_key_tuple = (pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(flax_key_tuple) not in pt_model_dict:
            # conv layer
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
            flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
        elif flax_key_tuple[-1] == "kernel" and ".".join(flax_key_tuple) not in pt_model_dict:
            # linear layer
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
            flax_tensor = flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            flax_key_tuple = flax_key_tuple[:-1] + ("running_mean",)
        elif "var" in flax_key_tuple[-1]:
            flax_key_tuple = flax_key_tuple[:-1] + ("running_var",)

        if "batch_stats" in flax_state:
            flax_key = ".".join(flax_key_tuple[1:])  # Remove the params/batch_stats header
        else:
            flax_key = ".".join(flax_key_tuple)

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        special_pt_names = {}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            key_components = key.split(".")
            name = None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                name = key_components[-2] + "_g"
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                name = key_components[-2] + "_v"
            if name is not None:
                key_components = key_components[:-3] + [name]
                key_to_check = ".".join(key_components)
                special_pt_names[key_to_check] = key

        if flax_key in special_pt_names:
            flax_key = special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )
            else:
                # add weight to pytorch dict
                flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
                pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
                # remove from missing keys
                missing_keys.remove(flax_key)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(flax_key)

    pt_model.load_state_dict(pt_model_dict)

    # re-transform missing_keys to list
    missing_keys = list(missing_keys)

    if len(unexpected_keys) > 0:
        logger.warning(
            "Some weights of the Flax model were not used when initializing the PyTorch model"
            f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
            f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
            " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
            f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
            " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
            " FlaxBertForSequenceClassification model)."
        )
    else:
        logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n")
    if len(missing_keys) > 0:
        logger.warning(
            f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
            f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
            " use it for predictions and inference."
        )
    else:
        logger.warning(
            f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
            "If your task is similar to the task the model of the checkpoint was trained on, "
            f"you can already use {pt_model.__class__.__name__} for predictions without further training."
        )

    return pt_model

# coding=utf-8
# Copyright 2021 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.
""" PyTorch - Flax general utilities."""


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


logger = logging.get_logger(__name__)


#####################
# PyTorch => Flax #
#####################


def load_pytorch_checkpoint_in_flax_state_dict(
    flax_model, pytorch_checkpoint_path, is_sharded, allow_missing_keys=False
):
    """Load pytorch checkpoints in a flax model"""
    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    if not is_sharded:
        pt_path = os.path.abspath(pytorch_checkpoint_path)
        logger.info(f"Loading PyTorch weights from {pt_path}")

        pt_state_dict = torch.load(pt_path, map_location="cpu")
        logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")

        flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        flax_state_dict = convert_pytorch_sharded_state_dict_to_flax(pytorch_checkpoint_path, flax_model)
    return flax_state_dict


def rename_key_and_reshape_tensor(
    pt_tuple_key: Tuple[str],
    pt_tensor: np.ndarray,
    random_flax_state_dict: Dict[str, jnp.ndarray],
    model_prefix: str,
) -> (Tuple[str], np.ndarray):
    """Rename PT weight names to corresponding Flax weight names and reshape tensor if necessary"""

    def is_key_or_prefix_key_in_dict(key: Tuple[str]) -> bool:
        """Checks if `key` of `(prefix,) + key` is in random_flax_state_dict"""
        return len(set(random_flax_state_dict) & {key, (model_prefix,) + key}) > 0

    # layer norm
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("scale",)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(renamed_pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("mean",)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("var",)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("embedding",)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(renamed_pt_tuple_key):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        pt_tensor = pt_tensor.transpose(2, 3, 1, 0)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(pt_tuple_key):
        pt_tensor = pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    renamed_pt_tuple_key = pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    name = None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        name = pt_tuple_key[-2] + "_g"
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        name = pt_tuple_key[-2] + "_v"
    if name is not None:
        renamed_pt_tuple_key = pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor


def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
    # convert pytorch tensor to numpy
    # numpy currently does not support bfloat16, need to go over float32 in this case to not lose precision
    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    weight_dtypes = {k: v.dtype for k, v in pt_state_dict.items()}
    pt_state_dict = {
        k: v.numpy() if not v.dtype == torch.bfloat16 else v.float().numpy() for k, v in pt_state_dict.items()
    }

    model_prefix = flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        flax_model_params = flax_model.params["params"]
    else:
        flax_model_params = flax_model.params
    random_flax_state_dict = flatten_dict(flax_model_params)

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        flax_batch_stats = flatten_dict(flax_model.params["batch_stats"])
        random_flax_state_dict.update(flax_batch_stats)

    flax_state_dict = {}

    load_model_with_head_into_base_model = (model_prefix not in flax_model_params) and (
        model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
    )
    load_base_model_into_model_with_head = (model_prefix in flax_model_params) and (
        model_prefix not in {k.split(".")[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        pt_tuple_key = tuple(pt_key.split("."))
        is_bfloat_16 = weight_dtypes[pt_key] == torch.bfloat16

        # remove base model prefix if necessary
        has_base_model_prefix = pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            pt_tuple_key = pt_tuple_key[1:]

        # Correctly rename weight parameters
        flax_key, flax_tensor = rename_key_and_reshape_tensor(
            pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
        )

        # add model prefix if necessary
        require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            flax_key = (model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                    f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(flax_key, None)
                continue

            # also add unexpected weight so that warning is thrown
            flax_state_dict[("params",) + flax_key] = (
                jnp.asarray(flax_tensor) if not is_bfloat_16 else jnp.asarray(flax_tensor, dtype=jnp.bfloat16)
            )

        else:
            # also add unexpected weight so that warning is thrown
            flax_state_dict[flax_key] = (
                jnp.asarray(flax_tensor) if not is_bfloat_16 else jnp.asarray(flax_tensor, dtype=jnp.bfloat16)
            )

    return unflatten_dict(flax_state_dict)


############################
# Sharded Pytorch => Flax #
############################


def convert_pytorch_sharded_state_dict_to_flax(shard_filenames, flax_model):
    import torch

    # Load the index
    flax_state_dict = {}
    for shard_file in shard_filenames:
        # load using msgpack utils
        pt_state_dict = torch.load(shard_file)
        pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}

        model_prefix = flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            flax_model_params = flax_model.params["params"]

            random_flax_state_dict = flatten_dict(flax_model_params)
            random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]))
        else:
            flax_model_params = flax_model.params
            random_flax_state_dict = flatten_dict(flax_model_params)

        load_model_with_head_into_base_model = (model_prefix not in flax_model_params) and (
            model_prefix in {k.split(".")[0] for k in pt_state_dict.keys()}
        )
        load_base_model_into_model_with_head = (model_prefix in flax_model_params) and (
            model_prefix not in {k.split(".")[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            pt_tuple_key = tuple(pt_key.split("."))

            # remove base model prefix if necessary
            has_base_model_prefix = pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                pt_tuple_key = pt_tuple_key[1:]

            # Correctly rename weight parameters
            flax_key, flax_tensor = rename_key_and_reshape_tensor(
                pt_tuple_key, pt_tensor, random_flax_state_dict, model_prefix
            )
            # add model prefix if necessary
            require_base_model_prefix = (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                flax_key = (model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."
                    )

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
                    continue
                if "var" in flax_key[-1]:
                    flax_state_dict[("batch_stats",) + flax_key] = jnp.asarray(flax_tensor)
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(flax_key, None)
                    continue

                # also add unexpected weight so that warning is thrown
                flax_state_dict[("params",) + flax_key] = jnp.asarray(flax_tensor)

            else:
                # also add unexpected weight so that warning is thrown
                flax_state_dict[flax_key] = jnp.asarray(flax_tensor)
    return unflatten_dict(flax_state_dict)


#####################
# Flax => PyTorch #
#####################


def load_flax_checkpoint_in_pytorch_model(model, flax_checkpoint_path):
    """Load flax checkpoints in a PyTorch model"""
    flax_checkpoint_path = os.path.abspath(flax_checkpoint_path)
    logger.info(f"Loading Flax weights from {flax_checkpoint_path}")

    # import correct flax class
    flax_cls = getattr(transformers, "Flax" + model.__class__.__name__)

    # load flax weight dict
    with open(flax_checkpoint_path, "rb") as state_f:
        try:
            flax_state_dict = from_bytes(flax_cls, state_f.read())
        except UnpicklingError:
            raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. ")

    return load_flax_weights_in_pytorch_model(model, flax_state_dict)


def load_flax_weights_in_pytorch_model(pt_model, flax_state):
    """Load flax checkpoints in a PyTorch model"""

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    # check if we have bf16 weights
    is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
    if any(is_type_bf16):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
            "before loading those in PyTorch model."
        )
        flax_state = jax.tree_util.tree_map(
            lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
        )

    flax_state_dict = flatten_dict(flax_state)
    pt_model_dict = pt_model.state_dict()

    load_model_with_head_into_base_model = (pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split(".")[0] for k in pt_model_dict.keys()}
    )
    load_base_model_into_model_with_head = (pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split(".")[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    unexpected_keys = []
    missing_keys = set(pt_model_dict.keys())

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        has_base_model_prefix = flax_key_tuple[0] == pt_model.base_model_prefix
        require_base_model_prefix = ".".join((pt_model.base_model_prefix,) + flax_key_tuple) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            flax_key_tuple = flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            flax_key_tuple = (pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(flax_key_tuple) not in pt_model_dict:
            # conv layer
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
            flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
        elif flax_key_tuple[-1] == "kernel" and ".".join(flax_key_tuple) not in pt_model_dict:
            # linear layer
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
            flax_tensor = flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            flax_key_tuple = flax_key_tuple[:-1] + ("running_mean",)
        elif "var" in flax_key_tuple[-1]:
            flax_key_tuple = flax_key_tuple[:-1] + ("running_var",)

        if "batch_stats" in flax_state:
            flax_key = ".".join(flax_key_tuple[1:])  # Remove the params/batch_stats header
        else:
            flax_key = ".".join(flax_key_tuple)

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        special_pt_names = {}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            key_components = key.split(".")
            name = None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                name = key_components[-2] + "_g"
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                name = key_components[-2] + "_v"
            if name is not None:
                key_components = key_components[:-3] + [name]
                key_to_check = ".".join(key_components)
                special_pt_names[key_to_check] = key

        if flax_key in special_pt_names:
            flax_key = special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )
            else:
                # add weight to pytorch dict
                flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
                pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
                # remove from missing keys
                missing_keys.remove(flax_key)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(flax_key)

    pt_model.load_state_dict(pt_model_dict)

    # re-transform missing_keys to list
    missing_keys = list(missing_keys)

    if len(unexpected_keys) > 0:
        logger.warning(
            "Some weights of the Flax model were not used when initializing the PyTorch model"
            f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
            f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
            " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
            f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
            " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
            " FlaxBertForSequenceClassification model)."
        )
    else:
        logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n")
    if len(missing_keys) > 0:
        logger.warning(
            f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
            f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
            " use it for predictions and inference."
        )
    else:
        logger.warning(
            f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
            "If your task is similar to the task the model of the checkpoint was trained on, "
            f"you can already use {pt_model.__class__.__name__} for predictions without further training."
        )

    return pt_model
