


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					['''image_processor''', '''tokenizer''']
					A__     =					'''LayoutLMv3ImageProcessor'''
					A__     =					('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')

					def __init__(self       :		Dict ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		str=None ,   **_UpperCAmelCase       :		str						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						None
							if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead.""" ,   _UpperCAmelCase ,   )
									lowercase__	     =						kwargs.pop("""feature_extractor"""						)

							lowercase__	     =						image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""						)
							if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""						)

							super().__init__(_UpperCAmelCase ,   _UpperCAmelCase						)

					def __call__(self       :		str ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,   _UpperCAmelCase       :		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None ,   _UpperCAmelCase       :		Union[List[List[int]], List[List[List[int]]]] = None ,   _UpperCAmelCase       :		Optional[Union[List[int], List[List[int]]]] = None ,   _UpperCAmelCase       :		bool = True ,   _UpperCAmelCase       :		Union[bool, str, PaddingStrategy] = False ,   _UpperCAmelCase       :		Union[bool, str, TruncationStrategy] = None ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		int = 0 ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[bool] = None ,   _UpperCAmelCase       :		Optional[bool] = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = True ,   _UpperCAmelCase       :		Optional[Union[str, TensorType]] = None ,   **_UpperCAmelCase       :		Optional[Any] ,   )		->  BatchEncoding:




							"""simple docstring"""


							if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""						)

							if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""						)

							# first, apply the image processor
							lowercase__	     =						self.image_processor(images=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase						)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											lowercase__	     =						[text]  # add batch dimension (as the image processor always adds a batch dimension)
									lowercase__	     =						features["""words"""]

							lowercase__	     =						self.tokenizer(
							    text=text if text is not None else features["""words"""] ,   text_pair=text_pair if text_pair is not None else None ,   boxes=boxes if boxes is not None else features["""boxes"""] ,   word_labels=_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   padding=_UpperCAmelCase ,   truncation=_UpperCAmelCase ,   max_length=_UpperCAmelCase ,   stride=_UpperCAmelCase ,   pad_to_multiple_of=_UpperCAmelCase ,   return_token_type_ids=_UpperCAmelCase ,   return_attention_mask=_UpperCAmelCase ,   return_overflowing_tokens=_UpperCAmelCase ,   return_special_tokens_mask=_UpperCAmelCase ,   return_offsets_mapping=_UpperCAmelCase ,   return_length=_UpperCAmelCase ,   verbose=_UpperCAmelCase ,   return_tensors=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

							# add pixel values
							lowercase__	     =						features.pop("""pixel_values"""						)
							if return_overflowing_tokens is True:
									lowercase__	     =						self.get_overflowing_images(_UpperCAmelCase ,   encoded_inputs["""overflow_to_sample_mapping"""]						)
							lowercase__	     =						images

							return encoded_inputs

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Dict						)		->  str:




							"""simple docstring"""


							lowercase__	     =						[]
							for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]						)

							if len(_UpperCAmelCase						) != len(_UpperCAmelCase						):
									raise ValueError(
									    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
									    f''' {len(_UpperCAmelCase						)} and {len(_UpperCAmelCase						)}'''						)

							return images_with_overflow

					def     lowerCamelCase__	(self       :		int ,   *_UpperCAmelCase       :		Optional[int] ,   **_UpperCAmelCase       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							return self.tokenizer.batch_decode(*_UpperCAmelCase ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str] ,   *_UpperCAmelCase       :		List[Any] ,   **_UpperCAmelCase       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							return self.tokenizer.decode(*_UpperCAmelCase ,   **_UpperCAmelCase						)

					@property
					def     lowerCamelCase__	(self       :		Tuple						)		->  Optional[int]:




							"""simple docstring"""


							return ["input_ids", "bbox", "attention_mask", "pixel_values"]

					@property
					def     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,   _UpperCAmelCase ,   )
							return self.image_processor_class


					@property
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[Any]:




							"""simple docstring"""


							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,   _UpperCAmelCase ,   )
							return self.image_processor





from typing import TYPE_CHECKING

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


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

