"""simple docstring"""




from typing import TYPE_CHECKING

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


_a   :	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:
     _a   :	Tuple 					=     ['MvpTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Optional[Any] 					=     [
         '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

     _a   :	Any 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  *a__       ,  **a__						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""       ,  a__       ,  )
       super().__init__(*a__       ,  **a__						)




