




'''simple docstring'''

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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			["""image_processor""", """tokenizer"""]
			__SCREAMING_SNAKE_CASE    =			"""LayoutLMv3ImageProcessor"""
			__SCREAMING_SNAKE_CASE    =			("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")





			def __init__(      self						:    str      ,		a_						:    Dict=None      ,		a_						:    Dict=None      ,		**a_						:    Optional[int]							):



						"""simple docstring"""




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

						__snake_case			=		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__(a_      ,		a_							)





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



						"""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
						__snake_case			=		self.image_processor(images=a_      ,		return_tensors=a_							)

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

						__snake_case			=		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=a_      ,		add_special_tokens=a_      ,		padding=a_      ,		truncation=a_      ,		max_length=a_      ,		stride=a_      ,		pad_to_multiple_of=a_      ,		return_token_type_ids=a_      ,		return_attention_mask=a_      ,		return_overflowing_tokens=a_      ,		return_special_tokens_mask=a_      ,		return_offsets_mapping=a_      ,		return_length=a_      ,		verbose=a_      ,		return_tensors=a_      ,		**a_      ,		)

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

						return encoded_inputs





			def 							A (      self						:    List[Any]      ,		a_						:    int      ,		a_						:    int							):



						"""simple docstring"""




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

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

						return images_with_overflow





			def 							A (      self						:    str      ,		*a_						:    str      ,		**a_						:    List[str]							):



						"""simple docstring"""




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





			def 							A (      self						:    str      ,		*a_						:    int      ,		**a_						:    Tuple							):



						"""simple docstring"""




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





			@property
			def 							A (      self						:    Dict							):



						"""simple docstring"""




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





			@property
			def 							A (      self						:    int							):



						"""simple docstring"""




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






			@property
			def 							A (      self						:    Dict							):



						"""simple docstring"""




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







'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


