

import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union







class        SCREAMING_SNAKE_CASE      (	enum.Enum ):






					"""simple docstring"""



					lowercase__       				= '''timm'''
					lowercase__       				= '''transformers'''

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	if stage_names is None:
		raise ValueError('''Stage_names must be set for transformers backbones'''				)

	if out_features is not None:
		if not isinstance(__lowerCAmelCase		,  (list,)				):
			raise ValueError(f'out_features must be a list {type(__lowerCAmelCase				)}'				)
		if any(feat not in stage_names for feat in out_features				):
			raise ValueError(f'out_features must be a subset of stage_names: {stage_names} got {out_features}'				)

	if out_indices is not None:
		if not isinstance(__lowerCAmelCase		,  (list, tuple)				):
			raise ValueError(f'out_indices must be a list or tuple, got {type(__lowerCAmelCase				)}'				)
		if any(idx >= len(__lowerCAmelCase				) for idx in out_indices				):
			raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''				)

	if out_features is not None and out_indices is not None:
		if len(__lowerCAmelCase				) != len(__lowerCAmelCase				):
			raise ValueError('''out_features and out_indices should have the same length if both are set'''				)
		if out_features != [stage_names[idx] for idx in out_indices]:
			raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  ):
	if out_indices is None and out_features is None:
		lowerCAmelCase__			:		Dict						       =			[len(__lowerCAmelCase				) - 1]
		lowerCAmelCase__			:		Optional[int]						       =			[stage_names[-1]]
	elif out_indices is None and out_features is not None:
		lowerCAmelCase__			:		List[Any]						       =			[stage_names.index(__lowerCAmelCase				) for layer in out_features]
	elif out_features is None and out_indices is not None:
		lowerCAmelCase__			:		Any						       =			[stage_names[idx] for idx in out_indices]
	return out_features, out_indices

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  ):
	verify_out_features_out_indices(out_features=__lowerCAmelCase		,  out_indices=__lowerCAmelCase		,  stage_names=__lowerCAmelCase				)
	lowerCAmelCase__			:		Tuple						       =			_align_output_features_output_indices(
	    out_features=__lowerCAmelCase		,  out_indices=__lowerCAmelCase		,  stage_names=__lowerCAmelCase				)
	# Verify that the aligned out_features and out_indices are valid
	verify_out_features_out_indices(out_features=__lowerCAmelCase		,  out_indices=__lowerCAmelCase		,  stage_names=__lowerCAmelCase				)
	return output_features, output_indices







class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					lowercase__       				= None







					def  __lowerCAmelCase				( self    :       str     ,lowercase_    :       Dict	):






						if getattr(self     ,'''_backbone'''     ,lowercase_	) is None:
							raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''	)

						# These will diagree with the defaults for the transformers models e.g. for resnet50
						# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
						# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
						lowerCAmelCase__			:		Any						       =			[stage["module"] for stage in self._backbone.feature_info.info]
						lowerCAmelCase__			:		List[str]						       =			[stage["num_chs"] for stage in self._backbone.feature_info.info]
						lowerCAmelCase__			:		Union[str, Any]						       =			self._backbone.feature_info.out_indices
						lowerCAmelCase__			:		Union[str, Any]						       =			self._backbone.feature_info.module_name()

						# We verify the out indices and out features are valid
						verify_out_features_out_indices(
						    out_features=lowercase_     ,out_indices=lowercase_     ,stage_names=self.stage_names	)
						lowerCAmelCase__			:		Union[str, Any]						       =			out_features, out_indices







					def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       Union[str, Any]	):
						lowerCAmelCase__			:		List[str]						       =			getattr(lowercase_     ,'''stage_names'''	)
						lowerCAmelCase__			:		List[str]						       =			getattr(lowercase_     ,'''out_features'''     ,lowercase_	)
						lowerCAmelCase__			:		List[Any]						       =			getattr(lowercase_     ,'''out_indices'''     ,lowercase_	)

						lowerCAmelCase__			:		Any						       =			stage_names
						lowerCAmelCase__			:		str						       =			get_aligned_output_features_output_indices(
						    out_features=lowercase_     ,out_indices=lowercase_     ,stage_names=lowercase_	)
						# Number of channels for each stage. This is set in the transformer backbone model init
						lowerCAmelCase__			:		Tuple						       =			None







					def  __lowerCAmelCase				( self    :       Optional[int]     ,lowercase_    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			config

						lowerCAmelCase__			:		str						       =			getattr(lowercase_     ,'''use_timm_backbone'''     ,lowercase_	)
						lowerCAmelCase__			:		Optional[int]						       =			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

						if self.backbone_type == BackboneType.TIMM:
							self._init_timm_backbone(lowercase_	)
						elif self.backbone_type == BackboneType.TRANSFORMERS:
							self._init_transformers_backbone(lowercase_	)
						else:
							raise ValueError(F'backbone_type {self.backbone_type} not supported.'	)







					@property
					def  __lowerCAmelCase				( self    :       List[Any]	):
						return self._out_features







					@out_features.setter
					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       List[str]	):
						lowerCAmelCase__			:		str						       =			get_aligned_output_features_output_indices(
						    out_features=lowercase_     ,out_indices=lowercase_     ,stage_names=self.stage_names	)







					@property
					def  __lowerCAmelCase				( self    :       Optional[Any]	):
						return self._out_indices







					@out_indices.setter
					def  __lowerCAmelCase				( self    :       Optional[int]     ,lowercase_    :       Union[Tuple[int], List[int]]	):
						lowerCAmelCase__			:		str						       =			get_aligned_output_features_output_indices(
						    out_features=lowercase_     ,out_indices=lowercase_     ,stage_names=self.stage_names	)







					@property
					def  __lowerCAmelCase				( self    :       int	):
						return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names	)}







					@property
					def  __lowerCAmelCase				( self    :       Optional[int]	):
						return [self.out_feature_channels[name] for name in self.out_features]







					def  __lowerCAmelCase				( self    :       List[str]     ,*lowercase_    :       List[str]     ,**lowercase_    :       str	):
						lowerCAmelCase__			:		List[str]						       =			dict(inspect.signature(self.forward	).parameters	)
						lowerCAmelCase__			:		Optional[int]						       =			{k: v for k, v in kwargs.items() if k in signature}
						return self(*lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       List[str]     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[bool] = None     ,):
						raise NotImplementedError('''This method should be implemented by the derived class.'''	)







					def  __lowerCAmelCase				( self    :       Tuple	):
						lowerCAmelCase__			:		Tuple						       =			super().to_dict()
						lowerCAmelCase__			:		Optional[int]						       =			output.pop('''_out_features'''	)
						lowerCAmelCase__			:		int						       =			output.pop('''_out_indices'''	)
						return output







class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



					@property
					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						return self._out_features







					@out_features.setter
					def  __lowerCAmelCase				( self    :       Any     ,lowercase_    :       List[str]	):
						lowerCAmelCase__			:		str						       =			get_aligned_output_features_output_indices(
						    out_features=lowercase_     ,out_indices=lowercase_     ,stage_names=self.stage_names	)







					@property
					def  __lowerCAmelCase				( self    :       Optional[int]	):
						return self._out_indices







					@out_indices.setter
					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Union[Tuple[int], List[int]]	):
						lowerCAmelCase__			:		Dict						       =			get_aligned_output_features_output_indices(
						    out_features=lowercase_     ,out_indices=lowercase_     ,stage_names=self.stage_names	)







					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						lowerCAmelCase__			:		Dict						       =			super().to_dict()
						lowerCAmelCase__			:		str						       =			output.pop('''_out_features'''	)
						lowerCAmelCase__			:		List[Any]						       =			output.pop('''_out_indices'''	)
						return output







"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase						:   Union[str, Any]							   =	{
    '''configuration_layoutlm''': ['''LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMConfig''', '''LayoutLMOnnxConfig'''],
    '''tokenization_layoutlm''': ['''LayoutLMTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Union[str, Any]							   =	['''LayoutLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   Dict							   =	[
							    '''LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LayoutLMForMaskedLM''',
							    '''LayoutLMForSequenceClassification''',
							    '''LayoutLMForTokenClassification''',
							    '''LayoutLMForQuestionAnswering''',
							    '''LayoutLMModel''',
							    '''LayoutLMPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase						:   str							   =	[
							    '''TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFLayoutLMForMaskedLM''',
							    '''TFLayoutLMForSequenceClassification''',
							    '''TFLayoutLMForTokenClassification''',
							    '''TFLayoutLMForQuestionAnswering''',
							    '''TFLayoutLMMainLayer''',
							    '''TFLayoutLMModel''',
							    '''TFLayoutLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase						:   int							   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




