




'''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  A		(			SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				A				     =    ["image_processor", "tokenizer"]
				A				     =    "LayoutLMv2ImageProcessor"
				A				     =    ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
				def __init__(self			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  **_UpperCAmelCase      )     ->	Optional[Any]:
							__UpperCamelCase				:      List[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."			,  _UpperCAmelCase			,  )
										__UpperCamelCase				:      Dict									=       kwargs.pop("feature_extractor"      )

							__UpperCamelCase				:      Dict									=       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__(_UpperCAmelCase			,  _UpperCAmelCase      )
				def __call__(self			,  _UpperCAmelCase			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = True			,  _UpperCAmelCase = False			,  _UpperCAmelCase = False			,  _UpperCAmelCase = None			,  _UpperCAmelCase = 0			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = None			,  _UpperCAmelCase = False			,  _UpperCAmelCase = False			,  _UpperCAmelCase = False			,  _UpperCAmelCase = False			,  _UpperCAmelCase = True			,  _UpperCAmelCase = None			,  **_UpperCAmelCase			,  )     ->	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
							__UpperCamelCase				:      str									=       self.image_processor(images=_UpperCAmelCase			,  return_tensors=_UpperCAmelCase      )

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

							__UpperCamelCase				:      List[str]									=       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=_UpperCAmelCase			,  add_special_tokens=_UpperCAmelCase			,  padding=_UpperCAmelCase			,  truncation=_UpperCAmelCase			,  max_length=_UpperCAmelCase			,  stride=_UpperCAmelCase			,  pad_to_multiple_of=_UpperCAmelCase			,  return_token_type_ids=_UpperCAmelCase			,  return_attention_mask=_UpperCAmelCase			,  return_overflowing_tokens=_UpperCAmelCase			,  return_special_tokens_mask=_UpperCAmelCase			,  return_offsets_mapping=_UpperCAmelCase			,  return_length=_UpperCAmelCase			,  verbose=_UpperCAmelCase			,  return_tensors=_UpperCAmelCase			,  **_UpperCAmelCase			,  )

							# add pixel values
							__UpperCamelCase				:      List[str]									=       features.pop("pixel_values"      )
							if return_overflowing_tokens is True:
										__UpperCamelCase				:      List[Any]									=       self.get_overflowing_images(_UpperCAmelCase			,  encoded_inputs["overflow_to_sample_mapping"]      )
							__UpperCamelCase				:      Any									=       images

							return encoded_inputs
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[Any]:
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							__UpperCamelCase				:      Tuple									=       []
							for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]      )

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

							return images_with_overflow
				def 	a_	(self			,  *_UpperCAmelCase			,  **_UpperCAmelCase      )     ->	Tuple:
							return self.tokenizer.batch_decode(*_UpperCAmelCase			,  **_UpperCAmelCase      )
				def 	a_	(self			,  *_UpperCAmelCase			,  **_UpperCAmelCase      )     ->	Dict:
							return self.tokenizer.decode(*_UpperCAmelCase			,  **_UpperCAmelCase      )
				@property
				def 	a_	(self      )     ->	Optional[Any]:
							return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
				@property
				def 	a_	(self      )     ->	List[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,  _UpperCAmelCase			,  )
							return self.image_processor_class





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









'''simple docstring'''






import math
import os

import cva
import numpy as np

_lowerCAmelCase				     = 255.0

def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
			__UpperCamelCase				:      Dict									=       np.mean((original - contrast) ** 2						)
			if mse == 0:
						return 100

			return 20 * math.logaa(PIXEL_MAX / math.sqrt(snake_case__						)						)

def       __lowerCAmelCase (   ):
			__UpperCamelCase				:      Tuple									=       os.path.dirname(os.path.realpath(snake_case__						)						)
			# Loading images (original image and compressed image)
			__UpperCamelCase				:      Tuple									=       cva.imread(os.path.join(snake_case__ ,				"image_data/original_image.png"						)						)
			__UpperCamelCase				:      List[Any]									=       cva.imread(os.path.join(snake_case__ ,				"image_data/compressed_image.png"						) ,				1						)

			__UpperCamelCase				:      Dict									=       cva.imread(os.path.join(snake_case__ ,				"image_data/PSNR-example-base.png"						)						)
			__UpperCamelCase				:      Optional[Any]									=       cva.imread(
			    os.path.join(snake_case__ ,				"image_data/PSNR-example-comp-10.jpg"						) ,				1						)

			# Value expected: 29.73dB
			print("-- First Test --"						)
			print(F"PSNR value is {peak_signal_to_noise_ratio(snake_case__ ,				snake_case__						)} dB"						)

			# # Value expected: 31.53dB (Wikipedia Example)
			print("\n-- Second Test --"						)
			print(F"PSNR value is {peak_signal_to_noise_ratio(snake_case__ ,				snake_case__						)} dB"						)


if __name__ == "__main__":
				main()



