






"""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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Union[str, Any]    =				["""image_processor""", """tokenizer"""]
	_A       :				Optional[Any]    =				"""LayoutLMv2ImageProcessor"""
	_A       :				Dict    =				("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     lowerCAmelCase_     ,     )
			A_		:						Dict	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						Union[str, Any]	    =   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     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = True     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		# 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
		A_		:						List[Any]	    =   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_							):
				A_		:						List[Any]	    =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
			A_		:						Optional[Any]	    =   features["""words"""]

		A_		:						Optional[int]	    =   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
		A_		:						Dict	    =   features.pop("""pixel_values"""							)
		if return_overflowing_tokens is True:
			A_		:						Optional[Any]	    =   self.get_overflowing_images(lowerCAmelCase_     ,     encoded_inputs["""overflow_to_sample_mapping"""]							)
		A_		:						List[str]	    =   images

		return encoded_inputs





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
		A_		:						str	    =   []
		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    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





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





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









"""simple docstring"""
import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				FunnelTokenizer
	_A       :				Union[str, Any]    =				FunnelTokenizerFast
	_A       :				List[str]    =				True
	_A       :				List[Any]    =				True





	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						Tuple	    =   [
		    """<unk>""",
		    """<cls>""",
		    """<sep>""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		A_		:						str	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""							) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   """UNwant\u00E9d,running"""
		A_		:						int	    =   """unwanted, running"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.tokenizer_class(self.vocab_file							)

		A_		:						Any	    =   tokenizer.tokenize("""UNwant\u00E9d,running"""							)
		self.assertListEqual(lowerCAmelCase_     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     [7, 4, 5, 10, 8, 9]							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_tokenizers(do_lower_case=lowerCAmelCase_							)
		for tokenizer in tokenizers:
			A_		:						Any	    =   tokenizer("""UNwant\u00E9d,running"""							)
			A_		:						Dict	    =   len(inputs["""input_ids"""]							) - 1
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len							)

			A_		:						Tuple	    =   tokenizer("""UNwant\u00E9d,running"""     ,     """UNwant\u00E9d,running"""							)
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len + [1] * sentence_len							)

