




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__				    = ['''image_processor''', '''tokenizer''']
    UpperCAmelCase__				    = '''LayoutLMv2ImageProcessor'''
    UpperCAmelCase__				    = ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


    def __init__(     self :      Any   ,					UpperCAmelCase__ :      Any=None   ,					UpperCAmelCase__ :      Optional[int]=None   ,					**UpperCAmelCase__ :      Optional[int])		->Optional[Any]:





           '''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.'''   ,					UpperCAmelCase__   ,					)
                  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__(UpperCAmelCase__   ,					UpperCAmelCase__)


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





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

           return encoded_inputs


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Tuple)		->Union[str, Any]:





           '''simple docstring'''

           A__ 					=       []
           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 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					*UpperCAmelCase__ :      List[Any]   ,					**UpperCAmelCase__ :      Optional[int])		->int:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					*UpperCAmelCase__ :      Tuple   ,					**UpperCAmelCase__ :      Dict)		->int:





           '''simple docstring'''

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


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->str:





           '''simple docstring'''

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


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Union[str, Any]:





           '''simple docstring'''

           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 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

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






'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			'''roberta'''
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int=50_265			,    lowerCAmelCase_							:			int=768			,    lowerCAmelCase_							:			Optional[Any]=12			,    lowerCAmelCase_							:			Tuple=12			,    lowerCAmelCase_							:			Optional[int]=3_072			,    lowerCAmelCase_							:			List[str]="gelu"			,    lowerCAmelCase_							:			List[str]=0.1			,    lowerCAmelCase_							:			Tuple=0.1			,    lowerCAmelCase_							:			List[Any]=512			,    lowerCAmelCase_							:			Union[str, Any]=2			,    lowerCAmelCase_							:			List[Any]=0.02			,    lowerCAmelCase_							:			Optional[int]=1E-12			,    lowerCAmelCase_							:			str=1			,    lowerCAmelCase_							:			Dict=0			,    lowerCAmelCase_							:			Any=2			,    lowerCAmelCase_							:			List[str]="absolute"			,    lowerCAmelCase_							:			Any=True			,    lowerCAmelCase_							:			Dict=None			,    **lowerCAmelCase_							:			Any			,    )      ->		int:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)

						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



