'''simple docstring'''


from typing import TYPE_CHECKING

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


snake_case__	:     int      =    {
    '''configuration_led''': ['''LED_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LEDConfig'''],
    '''tokenization_led''': ['''LEDTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     Optional[int]      =    ['''LEDTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     List[str]      =    [
     '''LED_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LEDForConditionalGeneration''',
     '''LEDForQuestionAnswering''',
     '''LEDForSequenceClassification''',
     '''LEDModel''',
     '''LEDPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     Optional[Any]      =    ['''TFLEDForConditionalGeneration''', '''TFLEDModel''', '''TFLEDPreTrainedModel''']


if TYPE_CHECKING:
 from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
 from .tokenization_led import LEDTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_led_fast import LEDTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_led import (
      LED_PRETRAINED_MODEL_ARCHIVE_LIST,
      LEDForConditionalGeneration,
      LEDForQuestionAnswering,
      LEDForSequenceClassification,
      LEDModel,
      LEDPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
 import sys

 snake_case__	:     Dict      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
				from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


