




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 						lowercase							(   UpperCamelCase__		):
     _a							 =						["image_processor", "tokenizer"]
     _a							 =						"LayoutLMv3ImageProcessor"
     _a							 =						("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")

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

     def __call__(  self		,     _a		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = True		,     _a = False		,     _a = None		,     _a = None		,     _a = 0		,     _a = None		,     _a = None		,     _a = None		,     _a = False		,     _a = False		,     _a = False		,     _a = False		,     _a = True		,     _a = None		,     **_a		,     )						->							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."""				)

           # first, apply the image processor
           _A							:     Dict			=						self.image_processor(images=_a		,     return_tensors=_a				)

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

           _A							:     Optional[Any]			=						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=_a		,     add_special_tokens=_a		,     padding=_a		,     truncation=_a		,     max_length=_a		,     stride=_a		,     pad_to_multiple_of=_a		,     return_token_type_ids=_a		,     return_attention_mask=_a		,     return_overflowing_tokens=_a		,     return_special_tokens_mask=_a		,     return_offsets_mapping=_a		,     return_length=_a		,     verbose=_a		,     return_tensors=_a		,     **_a		,     )

           # add pixel values
           _A							:     Tuple			=						features.pop("""pixel_values"""				)
           if return_overflowing_tokens is True:
                 _A							:     Union[str, Any]			=						self.get_overflowing_images(_a		,     encoded_inputs["""overflow_to_sample_mapping"""]				)
           _A							:     Union[str, Any]			=						images

           return encoded_inputs

     def 					a__	(  self		,     _a		,     _a				)						->							Optional[int]:
           # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
           _A							:     List[str]			=						[]
           for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]				)

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

           return images_with_overflow

     def 					a__	(  self		,     *_a		,     **_a				)						->							Optional[int]:
           return self.tokenizer.batch_decode(*_a		,     **_a				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							List[Any]:
           return self.tokenizer.decode(*_a		,     **_a				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           return ["input_ids", "bbox", "attention_mask", "pixel_values"]

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



     @property
     def 					a__	(  self				)						->							int:
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,     _a		,     )
           return self.image_processor







from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

