


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					(       __snake_case    ):
   lowercase						= ["""image_processor""", """tokenizer"""]
   lowercase						= """LayoutLMv2ImageProcessor"""
   lowercase						= ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")




   def __init__(						self							:		str       ,       __magic_name__							:		Tuple=None       ,       __magic_name__							:		Union[str, Any]=None       ,       **__magic_name__							:		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."""       ,       __magic_name__       ,       )
         UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

      UpperCamelCase         =					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__(__magic_name__       ,       __magic_name__						)




   def __call__(						self							:		Tuple       ,       __magic_name__							:		str       ,       __magic_name__							:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,       __magic_name__							:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,       __magic_name__							:		Union[List[List[int]], List[List[List[int]]]] = None       ,       __magic_name__							:		Optional[Union[List[int], List[List[int]]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = None       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		int       ,       ):







      """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
      UpperCamelCase         =					self.image_processor(images=__magic_name__       ,       return_tensors=__magic_name__						)

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

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

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

      return encoded_inputs




   def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		List[Any]						):







      """simple docstring"""







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

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

      return images_with_overflow




   def   lowerCamelCase_					(						self							:		List[str]       ,       *__magic_name__							:		Optional[Any]       ,       **__magic_name__							:		Tuple						):







      """simple docstring"""







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




   def   lowerCamelCase_					(						self							:		str       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Dict						):







      """simple docstring"""







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




   @property
   def   lowerCamelCase_					(						self							:		Tuple						):







      """simple docstring"""







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




   @property
   def   lowerCamelCase_					(						self							:		str						):







      """simple docstring"""







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





   @property
   def   lowerCamelCase_					(						self							:		str						):







      """simple docstring"""







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








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




