






"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCamelCase_       			=   {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''LayoutLMv3TokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''LayoutLMv3ForQuestionAnswering''',
			    '''LayoutLMv3ForSequenceClassification''',
			    '''LayoutLMv3ForTokenClassification''',
			    '''LayoutLMv3Model''',
			    '''LayoutLMv3PreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFLayoutLMv3ForQuestionAnswering''',
			    '''TFLayoutLMv3ForSequenceClassification''',
			    '''TFLayoutLMv3ForTokenClassification''',
			    '''TFLayoutLMv3Model''',
			    '''TFLayoutLMv3PreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''LayoutLMv3FeatureExtractor''']
			lowerCamelCase_       			=   ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
			from .configuration_layoutlmva import (
			    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    LayoutLMvaConfig,
			    LayoutLMvaOnnxConfig,
			)
			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_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_layoutlmva import (
						    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMvaForQuestionAnswering,
						    TFLayoutLMvaForSequenceClassification,
						    TFLayoutLMvaForTokenClassification,
						    TFLayoutLMvaModel,
						    TFLayoutLMvaPreTrainedModel,
						)

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

else:
			import sys

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











"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''levit'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				int=224			,			lowerCAmelCase_      :				Union[str, Any]=3			,			lowerCAmelCase_      :				Tuple=3			,			lowerCAmelCase_      :				int=2			,			lowerCAmelCase_      :				str=1			,			lowerCAmelCase_      :				Optional[Any]=16			,			lowerCAmelCase_      :				Tuple=[128, 256, 384]			,			lowerCAmelCase_      :				Optional[int]=[4, 8, 12]			,			lowerCAmelCase_      :				str=[4, 4, 4]			,			lowerCAmelCase_      :				Dict=[16, 16, 16]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[int]=[2, 2, 2]			,			lowerCAmelCase_      :				Any=[2, 2, 2]			,			lowerCAmelCase_      :				int=0.0_2			,			**lowerCAmelCase_      :				List[Any]			,			)		->    List[str]:
				super().__init__(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      image_size
				UpperCAmelCase_  :				List[Any]      =      num_channels
				UpperCAmelCase_  :				str      =      kernel_size
				UpperCAmelCase_  :				List[Any]      =      stride
				UpperCAmelCase_  :				List[str]      =      padding
				UpperCAmelCase_  :				Any      =      hidden_sizes
				UpperCAmelCase_  :				List[str]      =      num_attention_heads
				UpperCAmelCase_  :				List[str]      =      depths
				UpperCAmelCase_  :				int      =      key_dim
				UpperCAmelCase_  :				List[str]      =      drop_path_rate
				UpperCAmelCase_  :				str      =      patch_size
				UpperCAmelCase_  :				Tuple      =      attention_ratio
				UpperCAmelCase_  :				Optional[int]      =      mlp_ratio
				UpperCAmelCase_  :				Union[str, Any]      =      initializer_range
				UpperCAmelCase_  :				List[Any]      =      [
				    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
				    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
				]







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       version.parse('''1.11'''  )





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    float:
				return 1e-4



