"""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 					UpperCAmelCase		(_UpperCAmelCase						):



				"""simple docstring"""

				_UpperCAmelCase			:Union[str, Any]							=				["image_processor", "tokenizer"]
				_UpperCAmelCase			:List[str]							=				"LayoutLMv2ImageProcessor"
				_UpperCAmelCase			:Dict							=				("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






				def __init__(     self					,    _UpperCAmelCase=None					,    _UpperCAmelCase=None					,    **_UpperCAmelCase							):
							lowercase__:     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					,    )
										lowercase__:     List[Any]      =   kwargs.pop('''feature_extractor'''							)

							lowercase__:     int      =   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					,    _UpperCAmelCase					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = None					,    _UpperCAmelCase = True					,    _UpperCAmelCase = False					,    _UpperCAmelCase = False					,    _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.'''							)

							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
							lowercase__:     Dict      =   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							):
													lowercase__:     Any      =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
										lowercase__:     str      =   features["words"]

							lowercase__:     Dict      =   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
							lowercase__:     Dict      =   features.pop('''pixel_values'''							)
							if return_overflowing_tokens is True:
										lowercase__:     List[Any]      =   self.get_overflowing_images(__lowerCamelCase					,    encoded_inputs['''overflow_to_sample_mapping''']							)
							lowercase__:     Optional[Any]      =   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
							lowercase__:     Dict      =   []
							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 _snake_case    (     self					,    *_UpperCAmelCase					,    **_UpperCAmelCase							):
							return self.tokenizer.batch_decode(*__lowerCamelCase					,    **__lowerCamelCase							)






				def _snake_case    (     self					,    *_UpperCAmelCase					,    **_UpperCAmelCase							):
							return self.tokenizer.decode(*__lowerCamelCase					,    **__lowerCamelCase							)






				@property
				def _snake_case    (     self							):
							return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






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

"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A         =				logging.get_logger(__name__)

__A         =				{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Tuple							=				"yoso"






    def __init__(     self					,    _UpperCAmelCase=50265					,    _UpperCAmelCase=768					,    _UpperCAmelCase=12					,    _UpperCAmelCase=12					,    _UpperCAmelCase=3072					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=4096					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=1e-1_2					,    _UpperCAmelCase="absolute"					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9					,    _UpperCAmelCase=64					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0					,    _UpperCAmelCase=2					,    **_UpperCAmelCase					,    ):
       super().__init__(pad_token_id=_UpperCAmelCase					,    bos_token_id=_UpperCAmelCase					,    eos_token_id=_UpperCAmelCase					,    **_UpperCAmelCase							)

       lowercase__:     int      =   vocab_size
       lowercase__:     List[str]      =   max_position_embeddings
       lowercase__:     Tuple      =   hidden_size
       lowercase__:     Optional[Any]      =   num_hidden_layers
       lowercase__:     Optional[int]      =   num_attention_heads
       lowercase__:     List[Any]      =   intermediate_size
       lowercase__:     Union[str, Any]      =   hidden_act
       lowercase__:     int      =   hidden_dropout_prob
       lowercase__:     Optional[Any]      =   attention_probs_dropout_prob
       lowercase__:     List[Any]      =   initializer_range
       lowercase__:     Optional[Any]      =   type_vocab_size
       lowercase__:     int      =   layer_norm_eps
       lowercase__:     Optional[Any]      =   position_embedding_type
       lowercase__:     int      =   use_expectation
       lowercase__:     List[str]      =   hash_code_len
       lowercase__:     Union[str, Any]      =   num_hash
       lowercase__:     Any      =   conv_window
       lowercase__:     Dict      =   use_fast_hash
       lowercase__:     Dict      =   lsh_backward
