




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 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										=["""image_processor""", """tokenizer"""]
				__a							: Tuple										="""LayoutLMv3ImageProcessor"""
				__a							: int										=("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")



				def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_						):
											lowerCAmelCase   =		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_       , )
																		lowerCAmelCase   =		kwargs.pop('''feature_extractor'''						)

											lowerCAmelCase   =		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       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = False       , UpperCAmelCase_ = False       , UpperCAmelCase_ = False       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											# verify input
											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
											lowerCAmelCase   =		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_						):
																									lowerCAmelCase   =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
																		lowerCAmelCase   =		features['''words''']

											lowerCAmelCase   =		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
											lowerCAmelCase   =		features.pop('''pixel_values'''						)
											if return_overflowing_tokens is True:
																		lowerCAmelCase   =		self.get_overflowing_images(UpperCAmelCase_       , encoded_inputs['''overflow_to_sample_mapping''']						)
											lowerCAmelCase   =		images

											return encoded_inputs



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											lowerCAmelCase   =		[]
											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       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.batch_decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											return self.tokenizer.decode(*UpperCAmelCase_       , **UpperCAmelCase_						)



				@property
				def       __snake_case		( self						):
											return ["input_ids", "bbox", "attention_mask", "pixel_values"]



				@property
				def       __snake_case		( self						):
											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       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor









from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										="""maskformer-swin"""

				__a							: Optional[int]										={
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__( self       , UpperCAmelCase_=2_24       , UpperCAmelCase_=4       , UpperCAmelCase_=3       , UpperCAmelCase_=96       , UpperCAmelCase_=[2, 2, 6, 2]       , UpperCAmelCase_=[3, 6, 12, 24]       , UpperCAmelCase_=7       , UpperCAmelCase_=4.0       , UpperCAmelCase_=True       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.1       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=False       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)

											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		patch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		embed_dim
											lowerCAmelCase   =		depths
											lowerCAmelCase   =		len(UpperCAmelCase_						)
											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		window_size
											lowerCAmelCase   =		mlp_ratio
											lowerCAmelCase   =		qkv_bias
											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		drop_path_rate
											lowerCAmelCase   =		hidden_act
											lowerCAmelCase   =		use_absolute_embeddings
											lowerCAmelCase   =		layer_norm_eps
											lowerCAmelCase   =		initializer_range
											# 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
											lowerCAmelCase   =		int(embed_dim * 2 ** (len(UpperCAmelCase_						) - 1)						)
											lowerCAmelCase   =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       , len(UpperCAmelCase_						) + 1						)]
											lowerCAmelCase   ,     lowerCAmelCase   =		get_aligned_output_features_output_indices(
											    out_features=UpperCAmelCase_       , out_indices=UpperCAmelCase_       , stage_names=self.stage_names						)



