
"""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_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  Optional[int]						   =		'''LayoutLMv2ImageProcessor'''
		__lowercase							:  List[str]						   =		('''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__							,  )
						__SCREAMING_SNAKE_CASE            = kwargs.pop("""feature_extractor""")

				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = 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__):
								__SCREAMING_SNAKE_CASE            = [text]  # add batch dimension (as the image processor always adds a batch dimension)
						__SCREAMING_SNAKE_CASE            = features["""words"""]

				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = features.pop("""pixel_values""")
				if return_overflowing_tokens is True:
						__SCREAMING_SNAKE_CASE            = self.get_overflowing_images(lowerCAmelCase__							,  encoded_inputs["""overflow_to_sample_mapping"""])
				__SCREAMING_SNAKE_CASE            = images

				return encoded_inputs




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




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




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




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






"""simple docstring"""





__magic_name__       =			"Tobias Carryer"

from time import time
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=int(time())):  # noqa: B008
				__SCREAMING_SNAKE_CASE            = multiplier
				__SCREAMING_SNAKE_CASE            = increment
				__SCREAMING_SNAKE_CASE            = modulo
				__SCREAMING_SNAKE_CASE            = seed




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = (self.multiplier * self.seed + self.increment) % self.modulo
				return self.seed


if __name__ == "__main__":
							# Show the LCG in action.
							__magic_name__       =			LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
							while True:
														print(lcg.next_number())




