





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase					:		List[Any]		=							{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		List[Any]		=							['MvpTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		List[str]		=							[
       'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MvpForCausalLM',
       'MvpForConditionalGeneration',
       'MvpForQuestionAnswering',
       'MvpForSequenceClassification',
       'MvpModel',
       'MvpPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
   from .tokenization_mvp import MvpTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mvp_fast import MvpTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mvp import (
          MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
          MvpForCausalLM,
          MvpForConditionalGeneration,
          MvpForQuestionAnswering,
          MvpForSequenceClassification,
          MvpModel,
          MvpPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase					:		List[str]		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


