






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Union[str, Any]      			=      ["""Speech2TextTokenizer"""]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      ["""Speech2TextFeatureExtractor"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       List[Any]      			=      [
     """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFSpeech2TextForConditionalGeneration""",
     """TFSpeech2TextModel""",
     """TFSpeech2TextPreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Speech2TextForConditionalGeneration""",
     """Speech2TextModel""",
     """Speech2TextPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 __UpperCamelCase      :       List[str]      			=      _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      :       Tuple      			=      {
    """configuration_blip_2""": [
        """BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Blip2Config""",
        """Blip2QFormerConfig""",
        """Blip2VisionConfig""",
    ],
    """processing_blip_2""": ["""Blip2Processor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[int]      			=      [
     """BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Blip2Model""",
     """Blip2QFormerModel""",
     """Blip2PreTrainedModel""",
     """Blip2ForConditionalGeneration""",
     """Blip2VisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 __UpperCamelCase      :       Dict      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)