



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       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : str       =						["image_processor", "tokenizer"]
   A__      : str       =						"LayoutLMv2ImageProcessor"
   A__      : str       =						("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")


   def __init__(    self:     str  ,lowerCamelCase_:     Optional[int]=None  ,lowerCamelCase_:     str=None  ,**lowerCamelCase_:     Dict							)	->      Optional[Any]:
       UpperCAmelCase_     :       str  		=					None
       if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""  ,lowerCamelCase_  ,)
           UpperCAmelCase_     :       Union[str, Any]  		=					kwargs.pop("""feature_extractor"""							)

       UpperCAmelCase_     :       Dict  		=					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__(lowerCamelCase_  ,lowerCamelCase_							)


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

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""							)

       # first, apply the image processor
       UpperCAmelCase_     :       Optional[int]  		=					self.image_processor(images=lowerCamelCase_  ,return_tensors=lowerCamelCase_							)

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

       UpperCAmelCase_     :       str  		=					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=lowerCamelCase_  ,add_special_tokens=lowerCamelCase_  ,padding=lowerCamelCase_  ,truncation=lowerCamelCase_  ,max_length=lowerCamelCase_  ,stride=lowerCamelCase_  ,pad_to_multiple_of=lowerCamelCase_  ,return_token_type_ids=lowerCamelCase_  ,return_attention_mask=lowerCamelCase_  ,return_overflowing_tokens=lowerCamelCase_  ,return_special_tokens_mask=lowerCamelCase_  ,return_offsets_mapping=lowerCamelCase_  ,return_length=lowerCamelCase_  ,verbose=lowerCamelCase_  ,return_tensors=lowerCamelCase_  ,**lowerCamelCase_  ,)

       # add pixel values
       UpperCAmelCase_     :       Optional[int]  		=					features.pop("""pixel_values"""							)
       if return_overflowing_tokens is True:
           UpperCAmelCase_     :       str  		=					self.get_overflowing_images(lowerCamelCase_  ,encoded_inputs["""overflow_to_sample_mapping"""]							)
       UpperCAmelCase_     :       Tuple  		=					images

       return encoded_inputs


   def       A__    (    self:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Optional[int]							)	->      str:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_     :       List[Any]  		=					[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]							)

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

       return images_with_overflow


   def       A__    (    self:     Optional[int]  ,*lowerCamelCase_:     Union[str, Any]  ,**lowerCamelCase_:     List[str]							)	->      Optional[Any]:
       return self.tokenizer.batch_decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Tuple  ,*lowerCamelCase_:     Dict  ,**lowerCamelCase_:     int							)	->      str:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   @property
   def       A__    (    self:     str							)	->      Optional[Any]:
       return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


   @property
   def       A__    (    self:     Any							)	->      Tuple:
       warnings.warn(
           """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,lowerCamelCase_  ,)
       return self.image_processor_class






   @property
   def       A__    (    self:     Union[str, Any]							)	->      List[Any]:
       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,lowerCamelCase_  ,)
       return self.image_processor





from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
      import detectrona







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"layoutlmv2"


   def __init__(    self:     Tuple  ,lowerCamelCase_:     Dict=30522  ,lowerCamelCase_:     str=768  ,lowerCamelCase_:     Any=12  ,lowerCamelCase_:     Tuple=12  ,lowerCamelCase_:     int=3072  ,lowerCamelCase_:     int="gelu"  ,lowerCamelCase_:     Dict=0.1  ,lowerCamelCase_:     List[Any]=0.1  ,lowerCamelCase_:     List[Any]=512  ,lowerCamelCase_:     Tuple=2  ,lowerCamelCase_:     Dict=0.0_2  ,lowerCamelCase_:     Dict=1e-12  ,lowerCamelCase_:     Dict=0  ,lowerCamelCase_:     List[str]=1024  ,lowerCamelCase_:     Optional[Any]=128  ,lowerCamelCase_:     List[Any]=32  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     Dict=256  ,lowerCamelCase_:     int=64  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Tuple=[7, 7, 256]  ,lowerCamelCase_:     Union[str, Any]=128  ,lowerCamelCase_:     Any=128  ,lowerCamelCase_:     int=True  ,lowerCamelCase_:     Union[str, Any]=True  ,lowerCamelCase_:     Optional[int]=False  ,lowerCamelCase_:     List[str]=None  ,**lowerCamelCase_:     str  ,)	->      Any:
       super().__init__(
           vocab_size=lowerCamelCase_  ,hidden_size=lowerCamelCase_  ,num_hidden_layers=lowerCamelCase_  ,num_attention_heads=lowerCamelCase_  ,intermediate_size=lowerCamelCase_  ,hidden_act=lowerCamelCase_  ,hidden_dropout_prob=lowerCamelCase_  ,attention_probs_dropout_prob=lowerCamelCase_  ,max_position_embeddings=lowerCamelCase_  ,type_vocab_size=lowerCamelCase_  ,initializer_range=lowerCamelCase_  ,layer_norm_eps=lowerCamelCase_  ,pad_token_id=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       Optional[int]  		=					max_ad_position_embeddings
       UpperCAmelCase_     :       Optional[int]  		=					max_rel_pos
       UpperCAmelCase_     :       Optional[int]  		=					rel_pos_bins
       UpperCAmelCase_     :       List[str]  		=					fast_qkv
       UpperCAmelCase_     :       int  		=					max_rel_ad_pos
       UpperCAmelCase_     :       int  		=					rel_ad_pos_bins
       UpperCAmelCase_     :       Optional[Any]  		=					convert_sync_batchnorm
       UpperCAmelCase_     :       str  		=					image_feature_pool_shape
       UpperCAmelCase_     :       List[Any]  		=					coordinate_size
       UpperCAmelCase_     :       int  		=					shape_size
       UpperCAmelCase_     :       Optional[Any]  		=					has_relative_attention_bias
       UpperCAmelCase_     :       str  		=					has_spatial_attention_bias
       UpperCAmelCase_     :       Tuple  		=					has_visual_segment_embedding
       UpperCAmelCase_     :       Optional[Any]  		=					(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )


   @classmethod
   def       A__    (    self:     Any							)	->      Dict:
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }






   def       A__    (    self:     Tuple							)	->      int:
       UpperCAmelCase_     :       Any  		=					detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
           UpperCAmelCase_     :       Optional[int]  		=					k.split("""."""							)
           UpperCAmelCase_     :       List[str]  		=					detectrona_config
           for attribute in attributes[:-1]:
               UpperCAmelCase_     :       Optional[Any]  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)
           setattr(lowerCamelCase_  ,attributes[-1]  ,lowerCamelCase_							)

       return detectrona_config
