
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__ :		Tuple					=logging.get_logger(__name__)

A__ :		Any					={
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''videomae'''
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     List[str]=2_24					, lowerCamelCase      :     Any=16					, lowerCamelCase      :     str=3					, lowerCamelCase      :     List[str]=16					, lowerCamelCase      :     Tuple=2					, lowerCamelCase      :     List[Any]=7_68					, lowerCamelCase      :     Any=12					, lowerCamelCase      :     Any=12					, lowerCamelCase      :     Union[str, Any]=30_72					, lowerCamelCase      :     Tuple="gelu"					, lowerCamelCase      :     Tuple=0.0					, lowerCamelCase      :     str=0.0					, lowerCamelCase      :     int=0.02					, lowerCamelCase      :     List[Any]=1e-1_2					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[int]=6					, lowerCamelCase      :     Union[str, Any]=3_84					, lowerCamelCase      :     Union[str, Any]=4					, lowerCamelCase      :     List[Any]=15_36					, lowerCamelCase      :     Union[str, Any]=True					, **lowerCamelCase      :     Optional[Any]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)

   __A		:  Optional[Any]       				=		image_size
   __A		:  Tuple       				=		patch_size
   __A		:  List[str]       				=		num_channels
   __A		:  List[Any]       				=		num_frames
   __A		:  List[str]       				=		tubelet_size

   __A		:  Any       				=		hidden_size
   __A		:  str       				=		num_hidden_layers
   __A		:  Union[str, Any]       				=		num_attention_heads
   __A		:  str       				=		intermediate_size
   __A		:  Union[str, Any]       				=		hidden_act
   __A		:  Optional[int]       				=		hidden_dropout_prob
   __A		:  str       				=		attention_probs_dropout_prob
   __A		:  Any       				=		initializer_range
   __A		:  Union[str, Any]       				=		layer_norm_eps
   __A		:  Any       				=		qkv_bias
   __A		:  str       				=		use_mean_pooling

   __A		:  Tuple       				=		decoder_num_attention_heads
   __A		:  List[str]       				=		decoder_hidden_size
   __A		:  List[str]       				=		decoder_num_hidden_layers
   __A		:  Dict       				=		decoder_intermediate_size
   __A		:  str       				=		norm_pix_loss





'''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,
)


A__ :		str					={'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Optional[int]					=['T5Tokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=[
         '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:
     A__ :		List[str]					=[
         'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFT5EncoderModel',
         'TFT5ForConditionalGeneration',
         'TFT5Model',
         'TFT5PreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, 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

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



