

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
   from ...processing_utils import ProcessorMixin
   from ...utils import TensorType


_lowerCamelCase      :					Union[str, Any]					    =      logging.get_logger(__name__)

_lowerCamelCase      :					Tuple					    =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """layoutlmv3"""

       def __init__(							self ,   lowercase__=5_0_2_6_5 ,   lowercase__=7_6_8 ,   lowercase__=1_2 ,   lowercase__=1_2 ,   lowercase__=3_0_7_2 ,   lowercase__="gelu" ,   lowercase__=0.1 ,   lowercase__=0.1 ,   lowercase__=5_1_2 ,   lowercase__=2 ,   lowercase__=0.02 ,   lowercase__=1E-5 ,   lowercase__=1 ,   lowercase__=0 ,   lowercase__=2 ,   lowercase__=1_0_2_4 ,   lowercase__=1_2_8 ,   lowercase__=1_2_8 ,   lowercase__=True ,   lowercase__=3_2 ,   lowercase__=1_2_8 ,   lowercase__=6_4 ,   lowercase__=2_5_6 ,   lowercase__=True ,   lowercase__=True ,   lowercase__=True ,   lowercase__=2_2_4 ,   lowercase__=3 ,   lowercase__=1_6 ,   lowercase__=None ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(
                 vocab_size=lowercase__ ,   hidden_size=lowercase__ ,   num_hidden_layers=lowercase__ ,   num_attention_heads=lowercase__ ,   intermediate_size=lowercase__ ,   hidden_act=lowercase__ ,   hidden_dropout_prob=lowercase__ ,   attention_probs_dropout_prob=lowercase__ ,   max_position_embeddings=lowercase__ ,   type_vocab_size=lowercase__ ,   initializer_range=lowercase__ ,   layer_norm_eps=lowercase__ ,   pad_token_id=lowercase__ ,   bos_token_id=lowercase__ ,   eos_token_id=lowercase__ ,   **lowercase__ ,   )
             __A    =max_ad_position_embeddings
             __A    =coordinate_size
             __A    =shape_size
             __A    =has_relative_attention_bias
             __A    =rel_pos_bins
             __A    =max_rel_pos
             __A    =has_spatial_attention_bias
             __A    =rel_ad_pos_bins
             __A    =max_rel_ad_pos
             __A    =text_embed
             __A    =visual_embed
             __A    =input_size
             __A    =num_channels
             __A    =patch_size
             __A    =classifier_dropout





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    version.parse("""1.12"""			)

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             if self.task in ["question-answering", "sequence-classification"]:
                   return OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                           ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                           ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                       ]     )
             else:
                   return OrderedDict(
                       [
                           ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                           ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                           ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                           ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                       ]     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1E-5

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1_2


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = -1 ,   lowercase__ = -1 ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = 3 ,   lowercase__ = 4_0 ,   lowercase__ = 4_0 ,   ):







             '''simple docstring'''



             setattr(processor.image_processor ,   '''apply_ocr''' ,   lowercase__     )

             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             __A    =compute_effective_axis_dimension(
                 lowercase__ ,   fixed_dimension=OnnxConfig.default_fixed_batch ,   num_token_to_add=0     )
             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             __A    =processor.tokenizer.num_special_tokens_to_add(lowercase__     )
             __A    =compute_effective_axis_dimension(
                 lowercase__ ,   fixed_dimension=OnnxConfig.default_fixed_sequence ,   num_token_to_add=lowercase__     )
             # Generate dummy inputs according to compute batch and sequence
             __A    =[[''' '''.join([processor.tokenizer.unk_token]     ) * seq_length]] * batch_size

             # Generate dummy bounding boxes
             __A    =[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
             __A    =self._generate_dummy_images(lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__     )

             __A    =dict(
                 processor(
                     lowercase__ ,   text=lowercase__ ,   boxes=lowercase__ ,   return_tensors=lowercase__ ,   )     )

             return inputs





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__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""image_processor""", """tokenizer"""]
       lowercase_						=    """LayoutLMv2ImageProcessor"""
       lowercase_						=    ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   **lowercase__     ):







             '''simple docstring'''



             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.''' ,   lowercase__ ,   )
                   __A    =kwargs.pop('''feature_extractor'''     )

             __A    =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__(lowercase__ ,   lowercase__     )

       def __call__(							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = True ,   lowercase__ = False ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 0 ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = False ,   lowercase__ = True ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''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.'''     )

             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
             __A    =self.image_processor(images=lowercase__ ,   return_tensors=lowercase__     )

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

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

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

             return encoded_inputs

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



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

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

             return images_with_overflow

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   *lowercase__ ,   **lowercase__     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


