# What does this PR do?

Add float16 support to mask2former module and derivatives
Some operations and conversions were fixed in order to propagate the chosen user _dtype_ (ex: float32, float16) for input and embeddings. In this way, we can pass in fp16 tensors for inference.

## Who can review?

Similar PRs were reviewed by: @alaradirik, @amyeroberts, @LysandreJik

Add mask2former fp16 support. # What does this PR do?

Add float16 support to mask2former module and derivatives
Some operations and conversions were fixed in order to propagate the chosen user _dtype_ (ex: float32, float16) for input and embeddings. In this way, we can pass in fp16 tensors for inference.

## Who can review?

Similar PRs were reviewed by: @alaradirik, @amyeroberts, @LysandreJik

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_import_structure = {
    "configuration_bark": [
        "BARK_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BarkCoarseConfig",
        "BarkConfig",
        "BarkFineConfig",
        "BarkSemanticConfig",
    ],
    "processing_bark": ["BarkProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_bark"] = [
        "BARK_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BarkFineModel",
        "BarkSemanticModel",
        "BarkCoarseModel",
        "BarkModel",
        "BarkPreTrainedModel",
        "BarkCausalModel",
    ]

if TYPE_CHECKING:
    from .configuration_bark import (
        BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BarkCoarseConfig,
        BarkConfig,
        BarkFineConfig,
        BarkSemanticConfig,
    )
    from .processing_bark import BarkProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bark import (
            BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
            BarkCausalModel,
            BarkCoarseModel,
            BarkFineModel,
            BarkModel,
            BarkPreTrainedModel,
            BarkSemanticModel,
        )

else:
    import sys

    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_import_structure = {
    "configuration_bark": [
        "BARK_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BarkCoarseConfig",
        "BarkConfig",
        "BarkFineConfig",
        "BarkSemanticConfig",
    ],
    "processing_bark": ["BarkProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_bark"] = [
        "BARK_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BarkFineModel",
        "BarkSemanticModel",
        "BarkCoarseModel",
        "BarkModel",
        "BarkPreTrainedModel",
        "BarkCausalModel",
    ]

if TYPE_CHECKING:
    from .configuration_bark import (
        BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BarkCoarseConfig,
        BarkConfig,
        BarkFineConfig,
        BarkSemanticConfig,
    )
    from .processing_bark import BarkProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bark import (
            BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
            BarkCausalModel,
            BarkCoarseModel,
            BarkFineModel,
            BarkModel,
            BarkPreTrainedModel,
            BarkSemanticModel,
        )

else:
    import sys

    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
