


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      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= ["image_processor", "tokenizer"]
     __SCREAMING_SNAKE_CASE  			= "LayoutLMv3ImageProcessor"
     __SCREAMING_SNAKE_CASE  			= ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase)			->				Any:
            _A      :       Optional[Any]				    = 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 ,						)
                   _A      :       List[str]				    = kwargs.pop("feature_extractor")

            _A      :       Union[str, Any]				    = 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 ,						__lowerCamelCase ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = True ,						__lowerCamelCase = False ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = 0 ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = None ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = False ,						__lowerCamelCase = True ,						__lowerCamelCase = None ,						**__lowerCamelCase ,						)			->				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.")

            # first, apply the image processor
            _A      :       List[Any]				    = 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):
                          _A      :       Dict				    = [text]  # add batch dimension (as the image processor always adds a batch dimension)
                   _A      :       Tuple				    = features["words"]

            _A      :       List[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
            _A      :       Dict				    = features.pop("pixel_values")
            if return_overflowing_tokens is True:
                   _A      :       int				    = self.get_overflowing_images(__lowerCamelCase ,						encoded_inputs["overflow_to_sample_mapping"])
            _A      :       int				    = images

            return encoded_inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Tuple:
            # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
            _A      :       Optional[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  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.tokenizer.batch_decode(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				List[str]:
            return self.tokenizer.decode(*__lowerCamelCase ,						**__lowerCamelCase)


     @property
     def  _lowerCamelCase (						self)			->				Optional[Any]:
            return ["input_ids", "bbox", "attention_mask", "pixel_values"]


     @property
     def  _lowerCamelCase (						self)			->				str:
            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  _lowerCamelCase (						self)			->				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 logging


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "vit_mae"


     def __init__(						self ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=2_2_4 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=3 ,						__lowerCamelCase=True ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=8 ,						__lowerCamelCase=2_0_4_8 ,						__lowerCamelCase=0.7_5 ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						)			->				int:
            super().__init__(**__lowerCamelCase)

            _A      :       int				    = hidden_size
            _A      :       List[str]				    = num_hidden_layers
            _A      :       List[Any]				    = num_attention_heads
            _A      :       Optional[Any]				    = intermediate_size
            _A      :       Optional[int]				    = hidden_act
            _A      :       List[Any]				    = hidden_dropout_prob
            _A      :       List[Any]				    = attention_probs_dropout_prob
            _A      :       Union[str, Any]				    = initializer_range
            _A      :       str				    = layer_norm_eps
            _A      :       Any				    = image_size
            _A      :       int				    = patch_size
            _A      :       int				    = num_channels
            _A      :       Dict				    = qkv_bias
            _A      :       Tuple				    = decoder_num_attention_heads
            _A      :       Tuple				    = decoder_hidden_size
            _A      :       List[str]				    = decoder_num_hidden_layers
            _A      :       Optional[Any]				    = decoder_intermediate_size
            _A      :       List[str]				    = mask_ratio
            _A      :       Union[str, Any]				    = norm_pix_loss
