


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a   :   int												=   logging.get_logger(__name__)

_a   :   Optional[Any]												=   {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	"convbert"
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=3_0522       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=768       ,   SCREAMING_SNAKE_CASE_       :      Any=12       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=12       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=3072       ,   SCREAMING_SNAKE_CASE_       :      Dict="gelu"       ,   SCREAMING_SNAKE_CASE_       :      int=0.1       ,   SCREAMING_SNAKE_CASE_       :      str=0.1       ,   SCREAMING_SNAKE_CASE_       :      str=512       ,   SCREAMING_SNAKE_CASE_       :      List[str]=2       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      List[str]=1e-12       ,   SCREAMING_SNAKE_CASE_       :      Any=1       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=0       ,   SCREAMING_SNAKE_CASE_       :      str=2       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]=768       ,   SCREAMING_SNAKE_CASE_       :      Tuple=2       ,   SCREAMING_SNAKE_CASE_       :      Any=9       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=1       ,   SCREAMING_SNAKE_CASE_       :      Tuple=None       ,   **SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   ) ->							int:
        super().__init__(
            pad_token_id=SCREAMING_SNAKE_CASE_       ,   bos_token_id=SCREAMING_SNAKE_CASE_       ,   eos_token_id=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )

        __snake_case           =						vocab_size
        __snake_case           =						hidden_size
        __snake_case           =						num_hidden_layers
        __snake_case           =						num_attention_heads
        __snake_case           =						intermediate_size
        __snake_case           =						hidden_act
        __snake_case           =						hidden_dropout_prob
        __snake_case           =						attention_probs_dropout_prob
        __snake_case           =						max_position_embeddings
        __snake_case           =						type_vocab_size
        __snake_case           =						initializer_range
        __snake_case           =						layer_norm_eps
        __snake_case           =						embedding_size
        __snake_case           =						head_ratio
        __snake_case           =						conv_kernel_size
        __snake_case           =						num_groups
        __snake_case           =						classifier_dropout




class 			_lowercase (	__lowercase							):
   @property
   def a  (						self       :      List[Any]      ) ->							Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             __snake_case           =						{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             __snake_case           =						{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]      )








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	:  Union[str, Any]            =     {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  Optional[int]            =     [
	    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TimesformerModel",
	    "TimesformerForVideoClassification",
	    "TimesformerPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	A	:  List[str]            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




