Lightstar/venv/Lib/site-packages/transformers/models/gpt2/__init__.py

# 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.

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_import_structure = {
    "configuration_gpt2": ["GPT2Config", "GPT2OnnxConfig"],
    "tokenization_gpt2": ["GPT2Tokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_gpt2"] = [
        "GPT2DoubleHeadsModel",
        "GPT2ForQuestionAnswering",
        "GPT2ForSequenceClassification",
        "GPT2ForTokenClassification",
        "GPT2LMHeadModel",
        "GPT2Model",
        "GPT2PreTrainedModel",
        "load_tf_weights_in_gpt2",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_tf_gpt2"] = [
        "TFGPT2DoubleHeadsModel",
        "TFGPT2ForSequenceClassification",
        "TFGPT2LMHeadModel",
        "TFGPT2MainLayer",
        "TFGPT2Model",
        "TFGPT2PreTrainedModel",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["tokenization_gpt2_tf"] = ["TFGPT2Tokenizer"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

if TYPE_CHECKING:
    from .configuration_gpt2 import GPT2Config, GPT2OnnxConfig
    from .tokenization_gpt2 import GPT2Tokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt2_fast import GPT2TokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt2 import (
            GPT2DoubleHeadsModel,
            GPT2ForQuestionAnswering,
            GPT2ForSequenceClassification,
            GPT2ForTokenClassification,
            GPT2LMHeadModel,
            GPT2Model,
            GPT2PreTrainedModel,
            load_tf_weights_in_gpt2,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpt2 import (
            TFGPT2DoubleHeadsModel,
            TFGPT2ForSequenceClassification,
            TFGPT2LMHeadModel,
            TFGPT2MainLayer,
            TFGPT2Model,
            TFGPT2PreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt2_tf import TFGPT2Tokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel

else:
    import sys

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