
"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__   					=		logging.get_logger(__name__)

lowerCamelCase__   					=		{
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}




class  A__       (	_lowerCamelCase):
		A_						:       List[Any]				   =	'vivit'

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=2_24       ,		_SCREAMING_SNAKE_CASE=32       ,		_SCREAMING_SNAKE_CASE=[2, 16, 16]       ,		_SCREAMING_SNAKE_CASE=3       ,		_SCREAMING_SNAKE_CASE=7_68       ,		_SCREAMING_SNAKE_CASE=12       ,		_SCREAMING_SNAKE_CASE=12       ,		_SCREAMING_SNAKE_CASE=30_72       ,		_SCREAMING_SNAKE_CASE="gelu_fast"       ,		_SCREAMING_SNAKE_CASE=0.0       ,		_SCREAMING_SNAKE_CASE=0.0       ,		_SCREAMING_SNAKE_CASE=0.02       ,		_SCREAMING_SNAKE_CASE=1E-06       ,		_SCREAMING_SNAKE_CASE=True       ,		**_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						Dict									=      hidden_size
			__lowerCAmelCase			:						str									=      num_hidden_layers
			__lowerCAmelCase			:						Dict									=      num_attention_heads
			__lowerCAmelCase			:						Tuple									=      intermediate_size
			__lowerCAmelCase			:						Dict									=      hidden_act
			__lowerCAmelCase			:						List[str]									=      hidden_dropout_prob
			__lowerCAmelCase			:						Union[str, Any]									=      attention_probs_dropout_prob
			__lowerCAmelCase			:						Union[str, Any]									=      initializer_range
			__lowerCAmelCase			:						Optional[int]									=      layer_norm_eps

			__lowerCAmelCase			:						Optional[Any]									=      image_size
			__lowerCAmelCase			:						int									=      num_frames
			__lowerCAmelCase			:						Dict									=      tubelet_size
			__lowerCAmelCase			:						Union[str, Any]									=      num_channels
			__lowerCAmelCase			:						List[str]									=      qkv_bias

			super().__init__(**_SCREAMING_SNAKE_CASE )


from typing import TYPE_CHECKING

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


lowerCamelCase           =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''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

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