






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[Any]									=							"dpt"







			def __init__(     self:  Any				,     UpperCAmelCase_:  List[Any]=768				,     UpperCAmelCase_:  int=12				,     UpperCAmelCase_:  Tuple=12				,     UpperCAmelCase_:  Union[str, Any]=3_072				,     UpperCAmelCase_:  List[Any]="gelu"				,     UpperCAmelCase_:  Optional[int]=0.0				,     UpperCAmelCase_:  List[Any]=0.0				,     UpperCAmelCase_:  str=0.02				,     UpperCAmelCase_:  Union[str, Any]=1E-12				,     UpperCAmelCase_:  Optional[int]=384				,     UpperCAmelCase_:  List[str]=16				,     UpperCAmelCase_:  List[str]=3				,     UpperCAmelCase_:  List[str]=False				,     UpperCAmelCase_:  Any=True				,     UpperCAmelCase_:  Optional[int]=[2, 5, 8, 11]				,     UpperCAmelCase_:  str="project"				,     UpperCAmelCase_:  Tuple=[4, 2, 1, 0.5]				,     UpperCAmelCase_:  Dict=[96, 192, 384, 768]				,     UpperCAmelCase_:  List[Any]=256				,     UpperCAmelCase_:  Any=-1				,     UpperCAmelCase_:  Optional[Any]=False				,     UpperCAmelCase_:  str=True				,     UpperCAmelCase_:  str=0.4				,     UpperCAmelCase_:  List[str]=255				,     UpperCAmelCase_:  Any=0.1				,     UpperCAmelCase_:  Optional[Any]=[1, 1_024, 24, 24]				,     UpperCAmelCase_:  Optional[Any]=[0, 1]				,     UpperCAmelCase_:  Any=None				,     **UpperCAmelCase_:  str				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  hidden_size
									_SCREAMING_SNAKE_CASE     				=  is_hybrid

									if self.is_hybrid:
															if backbone_config is None:
																					logger.info("""Initializing the config with a `BiT` backbone."""					)
																					_SCREAMING_SNAKE_CASE     				=  {
																					    """global_padding""": """same""",
																					    """layer_type""": """bottleneck""",
																					    """depths""": [3, 4, 9],
																					    """out_features""": ["""stage1""", """stage2""", """stage3"""],
																					    """embedding_dynamic_padding""": True,
																					}
																					_SCREAMING_SNAKE_CASE     				=  BitConfig(**UpperCAmelCase_					)
															elif isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					logger.info("""Initializing the config with a `BiT` backbone."""					)
																					_SCREAMING_SNAKE_CASE     				=  BitConfig(**UpperCAmelCase_					)
															elif isinstance(UpperCAmelCase_				,     UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  backbone_config
															else:
																					raise ValueError(
																					    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'					)

															_SCREAMING_SNAKE_CASE     				=  backbone_featmap_shape
															_SCREAMING_SNAKE_CASE     				=  neck_ignore_stages

															if readout_type != "project":
																					raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""					)
									else:
															_SCREAMING_SNAKE_CASE     				=  None
															_SCREAMING_SNAKE_CASE     				=  None
															_SCREAMING_SNAKE_CASE     				=  []

									_SCREAMING_SNAKE_CASE     				=  num_hidden_layers
									_SCREAMING_SNAKE_CASE     				=  num_attention_heads
									_SCREAMING_SNAKE_CASE     				=  intermediate_size
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  hidden_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  patch_size
									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  qkv_bias
									_SCREAMING_SNAKE_CASE     				=  backbone_out_indices
									if readout_type not in ["ignore", "add", "project"]:
															raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""					)
									_SCREAMING_SNAKE_CASE     				=  readout_type
									_SCREAMING_SNAKE_CASE     				=  reassemble_factors
									_SCREAMING_SNAKE_CASE     				=  neck_hidden_sizes
									_SCREAMING_SNAKE_CASE     				=  fusion_hidden_size
									_SCREAMING_SNAKE_CASE     				=  head_in_index
									_SCREAMING_SNAKE_CASE     				=  use_batch_norm_in_fusion_residual
									# auxiliary head attributes (semantic segmentation)
									_SCREAMING_SNAKE_CASE     				=  use_auxiliary_head
									_SCREAMING_SNAKE_CASE     				=  auxiliary_loss_weight
									_SCREAMING_SNAKE_CASE     				=  semantic_loss_ignore_index
									_SCREAMING_SNAKE_CASE     				=  semantic_classifier_dropout







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  copy.deepcopy(self.__dict__					)

									if output["backbone_config"] is not None:
															_SCREAMING_SNAKE_CASE     				=  self.backbone_config.to_dict()

									_SCREAMING_SNAKE_CASE     				=  self.__class__.model_type
									return output









from typing import TYPE_CHECKING

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


UpperCamelCase			    =     {
    '''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:
		UpperCamelCase			    =     ['''LayoutLMv3TokenizerFast''']

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

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

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

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

