




'''simple docstring'''







from typing import TYPE_CHECKING

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


A     ={
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =['PerceiverFeatureExtractor']
   A     =['PerceiverImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A     =[
       'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PerceiverForImageClassificationConvProcessing',
       'PerceiverForImageClassificationFourier',
       'PerceiverForImageClassificationLearned',
       'PerceiverForMaskedLM',
       'PerceiverForMultimodalAutoencoding',
       'PerceiverForOpticalFlow',
       'PerceiverForSequenceClassification',
       'PerceiverLayer',
       'PerceiverModel',
       'PerceiverPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
   from .tokenization_perceiver import PerceiverTokenizer

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_perceiver import PerceiverFeatureExtractor
      from .image_processing_perceiver import PerceiverImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_perceiver import (
          PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
          PerceiverForImageClassificationConvProcessing,
          PerceiverForImageClassificationFourier,
          PerceiverForImageClassificationLearned,
          PerceiverForMaskedLM,
          PerceiverForMultimodalAutoencoding,
          PerceiverForOpticalFlow,
          PerceiverForSequenceClassification,
          PerceiverLayer,
          PerceiverModel,
          PerceiverPreTrainedModel,
      )

else:
   import sys

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








"""simple docstring"""




from typing import TYPE_CHECKING

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


lowercase_			     =  {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XGLMForCausalLM",
				    "XGLMModel",
				    "XGLMPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FlaxXGLMForCausalLM",
				    "FlaxXGLMModel",
				    "FlaxXGLMPreTrainedModel",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFXGLMForCausalLM",
				    "TFXGLMModel",
				    "TFXGLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm import XGLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm_fast import XGLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xglm import (
								    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXGLMForCausalLM,
								    TFXGLMModel,
								    TFXGLMPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure)






