import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase      		=					{
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase      		=					{
    '''microsoft/layoutlm-base-uncased''': 5_12,
    '''microsoft/layoutlm-large-uncased''': 5_12,
}

UpperCAmelCase      		=					{
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			VOCAB_FILES_NAMES
 _lowerCamelCase    :						Tuple					     =			PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase    :						Tuple					     =			PRETRAINED_INIT_CONFIGURATION
 _lowerCamelCase    :						Dict					     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase    :						Dict					     =			LayoutLMTokenizer

 def __init__(      self							:		Dict							,      lowerCamelCase							:		Tuple=None							,      lowerCamelCase							:		Optional[int]=None							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		Dict="[UNK]"							,      lowerCamelCase							:		Any="[SEP]"							,      lowerCamelCase							:		Optional[Any]="[PAD]"							,      lowerCamelCase							:		str="[CLS]"							,      lowerCamelCase							:		Any="[MASK]"							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		Union[str, Any]=None							,      **lowerCamelCase							:		Optional[Any]							,      )			->				Dict:
        super().__init__(
            lowerCamelCase							,      tokenizer_file=lowerCamelCase							,      do_lower_case=lowerCamelCase							,      unk_token=lowerCamelCase							,      sep_token=lowerCamelCase							,      pad_token=lowerCamelCase							,      cls_token=lowerCamelCase							,      mask_token=lowerCamelCase							,      tokenize_chinese_chars=lowerCamelCase							,      strip_accents=lowerCamelCase							,      **lowerCamelCase							,      )

        _UpperCamelCase  :							Any	    =	json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
        if (
            normalizer_state.get('lowercase'							,      lowerCamelCase	) != do_lower_case
            or normalizer_state.get('strip_accents'							,      lowerCamelCase	) != strip_accents
            or normalizer_state.get('handle_chinese_chars'							,      lowerCamelCase	) != tokenize_chinese_chars
        ):
               _UpperCamelCase  :							str	    =	getattr(lowerCamelCase							,      normalizer_state.pop('type'	)	)
               _UpperCamelCase  :							Optional[int]	    =	do_lower_case
               _UpperCamelCase  :							Tuple	    =	strip_accents
               _UpperCamelCase  :							int	    =	tokenize_chinese_chars
               _UpperCamelCase  :							Union[str, Any]	    =	normalizer_class(**lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	do_lower_case

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		int							,      lowerCamelCase							:		List[Any]=None	)			->				str:
        _UpperCamelCase  :							Tuple	    =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

        return output

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		List[int]							,      lowerCamelCase							:		Optional[List[int]] = None	)			->				List[int]:
        _UpperCamelCase  :							Union[str, Any]	    =	[self.sep_token_id]
        _UpperCamelCase  :							Dict	    =	[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep	) * [0]
        return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]

 def        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		Optional[str] = None	)			->				Tuple[str]:
        _UpperCamelCase  :							int	    =	self._tokenizer.model.save(lowerCamelCase							,      name=lowerCamelCase	)
        return tuple(lowerCamelCase	)







"""simple docstring"""






import math
import os

import cva
import numpy as np

__snake_case												=							255.0
def    A_ ( _lowerCAmelCase  :       float,  _lowerCAmelCase  :       float							):






     """simple docstring"""

     _a     						=					np.mean((original - contrast) ** 2							)
     if mse == 0:
          return 1_00

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(_lowerCAmelCase							)							)
def    A_ ( ):






     """simple docstring"""

     _a     						=					os.path.dirname(os.path.realpath(_lowerCAmelCase							)							)
     # Loading images (original image and compressed image)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/original_image.png'''							)							)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/compressed_image.png'''							),  1							)

     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-base.png'''							)							)
     _a     						=					cva.imread(
         os.path.join(_lowerCAmelCase,  '''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(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)

     # # Value expected: 31.53dB (Wikipedia Example)
     print('''\n-- Second Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)


if __name__ == "__main__":
   main()