




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			(       UpperCamelCase__       ):




    """simple docstring"""

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


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

         lowerCAmelCase__            =					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__(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def __call__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Union[List[List[int]], List[List[List[int]]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[List[int], List[List[int]]]] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[bool, str, PaddingStrategy] = False  ,      SCREAMING_SNAKE_CASE__	:	Union[bool, str, TruncationStrategy] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[int] = None  ,      SCREAMING_SNAKE_CASE__	:	int = 0  ,      SCREAMING_SNAKE_CASE__	:	Optional[int] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			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
         lowerCAmelCase__            =					self.image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  )

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

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

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

         return encoded_inputs


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			Union[str, Any]:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         lowerCAmelCase__            =					[]
         for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]  )

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

         return images_with_overflow


    def   a (			self	:	int  ,      *SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      **SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			Dict:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      *SCREAMING_SNAKE_CASE__	:	int  ,      **SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			List[Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


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


    @property
    def   a (			self	:	Union[str, Any]  ) ->			int:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,      SCREAMING_SNAKE_CASE__  ,      )
         return self.image_processor_class


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





from collections import OrderedDict
from typing import Mapping

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         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      _UpperCAmelCase      (							A__  ):




     @property
     def 		snake_case_		(					self):
         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),
             ])
