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


A__           =							logging.get_logger(__name__)

A__           =							{
    """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   _lowerCAmelCase		(     _A				):
							_UpperCAmelCase						    =     'vivit'







							def __init__(				self     :    str	,					__snake_case     :    Dict=224	,					__snake_case     :    Union[str, Any]=32	,					__snake_case     :    int=[2, 16, 16]	,					__snake_case     :    Dict=3	,					__snake_case     :    Dict=768	,					__snake_case     :    Dict=12	,					__snake_case     :    Tuple=12	,					__snake_case     :    Any=3072	,					__snake_case     :    List[str]="gelu_fast"	,					__snake_case     :    Optional[Any]=0.0	,					__snake_case     :    Tuple=0.0	,					__snake_case     :    Dict=0.0_2	,					__snake_case     :    Optional[Any]=1e-0_6	,					__snake_case     :    Union[str, Any]=True	,					**__snake_case     :    str	,					):
										lowerCamelCase						:Union[str, Any]						  =     hidden_size
										lowerCamelCase						:str						  =     num_hidden_layers
										lowerCamelCase						:int						  =     num_attention_heads
										lowerCamelCase						:str						  =     intermediate_size
										lowerCamelCase						:Tuple						  =     hidden_act
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:Optional[int]						  =     attention_probs_dropout_prob
										lowerCamelCase						:int						  =     initializer_range
										lowerCamelCase						:List[Any]						  =     layer_norm_eps

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

										super().__init__(**__lowerCamelCase		)





from typing import TYPE_CHECKING

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


A__           =							{
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							["""LayoutLMv2FeatureExtractor"""]
			A__           =							["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A__           =							[
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			A__           =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



