"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase     					=     {
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['OpenAIGPTTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'OpenAIGPTDoubleHeadsModel',
				    'OpenAIGPTForSequenceClassification',
				    'OpenAIGPTLMHeadModel',
				    'OpenAIGPTModel',
				    'OpenAIGPTPreTrainedModel',
				    'load_tf_weights_in_openai_gpt',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFOpenAIGPTDoubleHeadsModel',
				    'TFOpenAIGPTForSequenceClassification',
				    'TFOpenAIGPTLMHeadModel',
				    'TFOpenAIGPTMainLayer',
				    'TFOpenAIGPTModel',
				    'TFOpenAIGPTPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
				from .tokenization_openai import OpenAIGPTTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_openai_fast import OpenAIGPTTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_openai import (
								    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    OpenAIGPTDoubleHeadsModel,
								    OpenAIGPTForSequenceClassification,
								    OpenAIGPTLMHeadModel,
								    OpenAIGPTModel,
								    OpenAIGPTPreTrainedModel,
								    load_tf_weights_in_openai_gpt,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_openai import (
								    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFOpenAIGPTDoubleHeadsModel,
								    TFOpenAIGPTForSequenceClassification,
								    TFOpenAIGPTLMHeadModel,
								    TFOpenAIGPTMainLayer,
								    TFOpenAIGPTModel,
								    TFOpenAIGPTPreTrainedModel,
								)

else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase     					=     {
    '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:
				UpperCamelCase     					=     [
				    '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

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




