




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_camembert import CamembertTokenizer
else:
		lowercase__							:	Optional[int]											=		None


lowercase__							:	str											=		logging.get_logger(__name__)

lowercase__							:	Tuple											=		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__							:	Any											=		{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

lowercase__							:	Union[str, Any]											=		{
    'camembert-base': 512,
}

lowercase__							:	Tuple											=		'▁'
class 						UpperCAmelCase  (							snake_case_     ):







						'''simple docstring'''





						lowerCAmelCase_							=  VOCAB_FILES_NAMES
						lowerCAmelCase_							=  PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_							=  ['''input_ids''', '''attention_mask''']
						lowerCAmelCase_							=  CamembertTokenizer






						def __init__(		self				:	Tuple			,      __lowercase				:	Union[str, Any]=None			,      __lowercase				:	int=None			,      __lowercase				:	Union[str, Any]="<s>"			,      __lowercase				:	Tuple="</s>"			,      __lowercase				:	List[Any]="</s>"			,      __lowercase				:	Dict="<s>"			,      __lowercase				:	Optional[Any]="<unk>"			,      __lowercase				:	Dict="<pad>"			,      __lowercase				:	Union[str, Any]="<mask>"			,      __lowercase				:	List[str]=["<s>NOTUSED", "</s>NOTUSED"]			,      **__lowercase				:	str			,      ):






							"""simple docstring"""






							snake_case_						       =	AddedToken(A_			,      lstrip=A_			,      rstrip=A_		) if isinstance(A_			,      A_		) else mask_token

							super().__init__(
							    A_			,      tokenizer_file=A_			,      bos_token=A_			,      eos_token=A_			,      sep_token=A_			,      cls_token=A_			,      unk_token=A_			,      pad_token=A_			,      mask_token=A_			,      additional_special_tokens=A_			,      **A_			,      )

							snake_case_						       =	vocab_file
							snake_case_						       =	False if not self.vocab_file else True






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[str] = None		):






							"""simple docstring"""






							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							snake_case_						       =	[self.cls_token_id]
							snake_case_						       =	[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	Tuple			,      __lowercase				:	Optional[int] = None		):






							"""simple docstring"""






							snake_case_						       =	[self.sep_token_id]
							snake_case_						       =	[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 + sep + token_ids_a + sep		) * [0]






						def 			snake_case__		(		self				:	int			,      __lowercase				:	List[str]			,      __lowercase				:	Any = None		):






							"""simple docstring"""






							if not self.can_save_slow_tokenizer:
								raise ValueError(
								    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
								    "tokenizer."		)

							if not os.path.isdir(A_		):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
								return
							snake_case_						       =	os.path.join(
							    A_			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(A_		):
								copyfile(self.vocab_file			,      A_		)

							return (out_vocab_file,)


'''simple docstring'''
import copy
import os

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


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






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


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




