


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,
)


snake_case       :					Optional[int]        =  {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					List[str]        =  ['''T5Tokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					str        =  ['''T5TokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					Dict        =  [
         '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''T5EncoderModel''',
         '''T5ForConditionalGeneration''',
         '''T5Model''',
         '''T5PreTrainedModel''',
         '''load_tf_weights_in_t5''',
         '''T5ForQuestionAnswering''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					Optional[Any]        =  [
         '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFT5EncoderModel''',
         '''TFT5ForConditionalGeneration''',
         '''TFT5Model''',
         '''TFT5PreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					Optional[Any]        =  [
         '''FlaxT5EncoderModel''',
         '''FlaxT5ForConditionalGeneration''',
         '''FlaxT5Model''',
         '''FlaxT5PreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta import TaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_ta_fast import TaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ta import (
              T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TaEncoderModel,
              TaForConditionalGeneration,
              TaForQuestionAnswering,
              TaModel,
              TaPreTrainedModel,
              load_tf_weights_in_ta,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_ta import (
              TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTaEncoderModel,
              TFTaForConditionalGeneration,
              TFTaModel,
              TFTaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_ta import (
              FlaxTaEncoderModel,
              FlaxTaForConditionalGeneration,
              FlaxTaModel,
              FlaxTaPreTrainedModel,
          )


else:
     import sys

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









import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


snake_case       :					Tuple        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):




 def __init__(	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       warnings.warn(
           '''The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
           ''' Please use ConvNextImageProcessor instead.'''      ,						_lowerCamelCase      ,						)
       super().__init__(*_lowerCamelCase      ,						**_lowerCamelCase					)





