





"""simple docstring"""


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


lowerCamelCase__      =      logging.get_logger(__name__)

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



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]		   =    "videomae"

  def __init__(						self      :				str    ,      __a      :				Any=224    ,      __a      :				Dict=16    ,      __a      :				List[Any]=3    ,      __a      :				List[str]=16    ,      __a      :				int=2    ,      __a      :				Tuple=768    ,      __a      :				Any=12    ,      __a      :				Tuple=12    ,      __a      :				List[Any]=3072    ,      __a      :				Any="gelu"    ,      __a      :				Optional[Any]=0.0    ,      __a      :				Union[str, Any]=0.0    ,      __a      :				List[str]=0.02    ,      __a      :				List[str]=1e-1_2    ,      __a      :				Optional[int]=True    ,      __a      :				int=True    ,      __a      :				List[Any]=6    ,      __a      :				List[Any]=384    ,      __a      :				int=4    ,      __a      :				Dict=1536    ,      __a      :				Dict=True    ,      **__a      :				int    ,      )						-> Optional[Any]:
        super().__init__(**__a		)

        _UpperCamelCase					:		Union[str, Any]  =				image_size
        _UpperCamelCase					:		List[str]  =				patch_size
        _UpperCamelCase					:		Dict  =				num_channels
        _UpperCamelCase					:		Tuple  =				num_frames
        _UpperCamelCase					:		List[Any]  =				tubelet_size

        _UpperCamelCase					:		Tuple  =				hidden_size
        _UpperCamelCase					:		Union[str, Any]  =				num_hidden_layers
        _UpperCamelCase					:		Union[str, Any]  =				num_attention_heads
        _UpperCamelCase					:		int  =				intermediate_size
        _UpperCamelCase					:		Union[str, Any]  =				hidden_act
        _UpperCamelCase					:		Tuple  =				hidden_dropout_prob
        _UpperCamelCase					:		Union[str, Any]  =				attention_probs_dropout_prob
        _UpperCamelCase					:		Dict  =				initializer_range
        _UpperCamelCase					:		List[str]  =				layer_norm_eps
        _UpperCamelCase					:		List[str]  =				qkv_bias
        _UpperCamelCase					:		Tuple  =				use_mean_pooling

        _UpperCamelCase					:		Union[str, Any]  =				decoder_num_attention_heads
        _UpperCamelCase					:		Optional[int]  =				decoder_hidden_size
        _UpperCamelCase					:		Dict  =				decoder_num_hidden_layers
        _UpperCamelCase					:		List[Any]  =				decoder_intermediate_size
        _UpperCamelCase					:		List[str]  =				norm_pix_loss








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:    List[Any]     =						{
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    Any     =						['OpenAIGPTTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    int     =						[
       'OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'OpenAIGPTDoubleHeadsModel',
       'OpenAIGPTForSequenceClassification',
       'OpenAIGPTLMHeadModel',
       'OpenAIGPTModel',
       'OpenAIGPTPreTrainedModel',
       'load_tf_weights_in_openai_gpt',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase				:    List[str]     =						[
       'TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFOpenAIGPTDoubleHeadsModel',
       'TFOpenAIGPTForSequenceClassification',
       'TFOpenAIGPTLMHeadModel',
       'TFOpenAIGPTMainLayer',
       'TFOpenAIGPTModel',
       'TFOpenAIGPTPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
   from .tokenization_openai import OpenAIGPTTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_openai_fast import OpenAIGPTTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_openai import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
          OpenAIGPTPreTrainedModel,
          load_tf_weights_in_openai_gpt,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTMainLayer,
          TFOpenAIGPTModel,
          TFOpenAIGPTPreTrainedModel,
      )

else:
   import sys

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




