'''simple docstring'''




from typing import TYPE_CHECKING

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


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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case  =			[
     '''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:
 snake_case  =			[
     '''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

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






'''simple docstring'''




from typing import TYPE_CHECKING

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


snake_case  =			{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case  =			[
     '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''AlignModel''',
     '''AlignPreTrainedModel''',
     '''AlignTextModel''',
     '''AlignVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_align import (
     ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
     AlignConfig,
     AlignTextConfig,
     AlignVisionConfig,
 )
 from .processing_align import AlignProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_align import (
      ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlignModel,
      AlignPreTrainedModel,
      AlignTextModel,
      AlignVisionModel,
  )

else:
 import sys

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





