



'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_		:     Dict        =     logging.get_logger(__name__)

A_		:     List[Any]        =     {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_		:     Optional[int]        =     {
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

A_		:     Optional[Any]        =     {
    '''junnyu/roformer_chinese_small''': 15_36,
    '''junnyu/roformer_chinese_base''': 15_36,
    '''junnyu/roformer_chinese_char_small''': 5_12,
    '''junnyu/roformer_chinese_char_base''': 5_12,
    '''junnyu/roformer_small_discriminator''': 1_28,
    '''junnyu/roformer_small_generator''': 1_28,
}


A_		:     str        =     {
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}



class 		_lowercase		(       _snake_case	):
   _UpperCAmelCase     	=			VOCAB_FILES_NAMES
   _UpperCAmelCase     	=			PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase     	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase     	=			PRETRAINED_INIT_CONFIGURATION
   _UpperCAmelCase     	=			RoFormerTokenizer

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Tuple=True							,   __lowerCAmelCase			:       List[str]="[UNK]"							,   __lowerCAmelCase			:       Optional[int]="[SEP]"							,   __lowerCAmelCase			:       Union[str, Any]="[PAD]"							,   __lowerCAmelCase			:       List[str]="[CLS]"							,   __lowerCAmelCase			:       Optional[Any]="[MASK]"							,   __lowerCAmelCase			:       int=True							,   __lowerCAmelCase			:       Dict=None							,   **__lowerCAmelCase			:       List[Any]							,   )       ->				str:


    """simple docstring"""






    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							,   )

    a						=		json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
    if (
        pre_tok_state.get("lowercase"							,   __lowerCAmelCase							) != do_lower_case
        or pre_tok_state.get("strip_accents"							,   __lowerCAmelCase							) != strip_accents
    ):
     a						=		getattr(__lowerCAmelCase							,   pre_tok_state.pop("type"							)							)
     a						=		do_lower_case
     a						=		strip_accents
     a						=		pre_tok_class(**__lowerCAmelCase							)

    a						=		do_lower_case

   def __getstate__(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.__dict__.copy()
    a						=		BertPreTokenizer()
    return state

   def __setstate__(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]							)       ->				Tuple:


    """simple docstring"""






    a						=		d
    a						=		self.__dict__["_tokenizer"].get_vocab()
    a						=		PreTokenizer.custom(JiebaPreTokenizer(__lowerCAmelCase							)							)

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       List[Any]=None							)       ->				str:


    """simple docstring"""






    a						=		[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 		A							(      self			:       int							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							)       ->				Dict:


    """simple docstring"""






    a						=		[self.sep_token_id]
    a						=		[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 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Optional[str] = None							)       ->				List[str]:


    """simple docstring"""






    a						=		self._tokenizer.model.save(__lowerCAmelCase							,   name=__lowerCAmelCase							)
    return tuple(__lowerCAmelCase							)




   def 		A							(      self			:       int							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Dict=None							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Union[str, Any]=False							,   **__lowerCAmelCase			:       str							,   )       ->				Optional[int]:


    """simple docstring"""






    a						=		BertPreTokenizer()
    return super().save_pretrained(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							)






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt



class 		_lowercase		:

   def __init__(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    a						=		""
    a						=		""
    a						=		[]
    a						=		0
    a						=		256
    a						=		0
    a						=		0
    a						=		0
    a						=		0

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Any							)       ->				int:


    """simple docstring"""






    a						=		cva.imread(__lowerCAmelCase							,   0							)
    a						=		copy.deepcopy(self.img							)
    a		,  a		,  a						=		plt.hist(self.img.ravel()							,   256							,   [0, 256]							,   label="x"							)
    a						=		np.sum(__lowerCAmelCase							)
    for i in range(len(__lowerCAmelCase							)							):
     a						=		x[i] / self.k
     self.sk += prk
     a						=		(self.L - 1) * self.sk
     if self.rem != 0:
      a						=		int(last % last							)
     a						=		int(last + 1 if self.rem >= 0.5 else last							)
     self.last_list.append(__lowerCAmelCase							)
     a						=		int(np.ma.count(self.img							) / self.img[1].size							)
     a						=		self.img[1].size
    for i in range(self.number_of_cols							):
     for j in range(self.number_of_rows							):
      a						=		self.img[j][i]
      if num != self.last_list[num]:
       a						=		self.last_list[num]
    cva.imwrite("output_data/output.jpg"							,   self.img							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    plt.hist(self.img.ravel()							,   256							,   [0, 256]							)




   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    cva.imshow("Output-Image"							,   self.img							)
    cva.imshow("Input-Image"							,   self.original_image							)
    cva.waitKey(5000							)
    cva.destroyAllWindows()


if __name__ == "__main__":
     A_		:     List[Any]        =     os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     A_		:     int        =     ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()

