






"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCamelCase_					=      logging.get_logger(__name__)


lowerCamelCase_					=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_					=      {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_					=      {
    '''RUCAIBox/mvp''': 1024,
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				MvpTokenizer





	def __init__(      self					: Dict	,	lowerCAmelCase_					: Any=None	,	lowerCAmelCase_					: List[str]=None	,	lowerCAmelCase_					: Optional[Any]=None	,	lowerCAmelCase_					: Tuple="replace"	,	lowerCAmelCase_					: List[Any]="<s>"	,	lowerCAmelCase_					: List[str]="</s>"	,	lowerCAmelCase_					: Dict="</s>"	,	lowerCAmelCase_					: Optional[Any]="<s>"	,	lowerCAmelCase_					: List[str]="<unk>"	,	lowerCAmelCase_					: Optional[int]="<pad>"	,	lowerCAmelCase_					: Optional[int]="<mask>"	,	lowerCAmelCase_					: List[str]=False	,	lowerCAmelCase_					: Union[str, Any]=True	,	**lowerCAmelCase_					: int	,	)						->			Optional[Any]:
				super().__init__(
				    lowerCAmelCase_	,	lowerCAmelCase_	,	tokenizer_file=lowerCAmelCase_	,	errors=lowerCAmelCase_	,	bos_token=lowerCAmelCase_	,	eos_token=lowerCAmelCase_	,	sep_token=lowerCAmelCase_	,	cls_token=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	pad_token=lowerCAmelCase_	,	mask_token=lowerCAmelCase_	,	add_prefix_space=lowerCAmelCase_	,	trim_offsets=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				UpperCAmelCase_     : str			     =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
				if pre_tok_state.get("add_prefix_space"	,	lowerCAmelCase_						) != add_prefix_space:
							UpperCAmelCase_     : Tuple			     =					getattr(lowerCAmelCase_	,	pre_tok_state.pop("type"						)						)
							UpperCAmelCase_     : Any			     =					add_prefix_space
							UpperCAmelCase_     : str			     =					pre_tok_class(**lowerCAmelCase_						)

				UpperCAmelCase_     : List[Any]			     =					add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				UpperCAmelCase_     : Optional[Any]			     =					"post_processor"
				UpperCAmelCase_     : str			     =					getattr(self.backend_tokenizer	,	lowerCAmelCase_	,	lowerCAmelCase_						)
				if tokenizer_component_instance:
							UpperCAmelCase_     : Optional[Any]			     =					json.loads(tokenizer_component_instance.__getstate__()						)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
										UpperCAmelCase_     : str			     =					tuple(state["sep"]						)
							if "cls" in state:
										UpperCAmelCase_     : Optional[Any]			     =					tuple(state["cls"]						)

							UpperCAmelCase_     : List[Any]			     =					False

							if state.get("add_prefix_space"	,	lowerCAmelCase_						) != add_prefix_space:
										UpperCAmelCase_     : List[str]			     =					add_prefix_space
										UpperCAmelCase_     : Dict			     =					True

							if state.get("trim_offsets"	,	lowerCAmelCase_						) != trim_offsets:
										UpperCAmelCase_     : Any			     =					trim_offsets
										UpperCAmelCase_     : Union[str, Any]			     =					True

							if changes_to_apply:
										UpperCAmelCase_     : Optional[Any]			     =					getattr(lowerCAmelCase_	,	state.pop("type"						)						)
										UpperCAmelCase_     : Union[str, Any]			     =					component_class(**lowerCAmelCase_						)
										setattr(self.backend_tokenizer	,	lowerCAmelCase_	,	lowerCAmelCase_						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			str:
				if self._mask_token is None:
							if self.verbose:
										logger.error("Using mask_token, but it is not set yet."						)
							return None
				return str(self._mask_token						)





	@mask_token.setter
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Union[str, Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Optional[Any]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else value
				UpperCAmelCase_     : List[str]			     =					value





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	*lowerCAmelCase_					: List[Any]	,	**lowerCAmelCase_					: str						)						->			BatchEncoding:
				UpperCAmelCase_     : Optional[int]			     =					kwargs.get("is_split_into_words"	,	lowerCAmelCase_						)

				if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."						)

				return super()._batch_encode_plus(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: Optional[int]						)						->			BatchEncoding:
				UpperCAmelCase_     : List[Any]			     =					kwargs.get("is_split_into_words"	,	lowerCAmelCase_						)

				if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs."						)

				return super()._encode_plus(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				UpperCAmelCase_     : Union[str, Any]			     =					self._tokenizer.model.save(lowerCAmelCase_	,	name=lowerCAmelCase_						)
				return tuple(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[int]=None						)						->			Optional[int]:
				UpperCAmelCase_     : List[str]			     =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : int			     =					[self.sep_token_id]
				UpperCAmelCase_     : Tuple			     =					[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]











"""simple docstring"""
import copy
import os

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







class 			UpperCamelCase_       :





	def __init__(      self					: str						)						->			Dict:
				UpperCAmelCase_     : List[Any]			     =					""
				UpperCAmelCase_     : int			     =					""
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : int			     =					0
				UpperCAmelCase_     : List[Any]			     =					256
				UpperCAmelCase_     : Dict			     =					0
				UpperCAmelCase_     : List[Any]			     =					0
				UpperCAmelCase_     : str			     =					0
				UpperCAmelCase_     : List[str]			     =					0





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





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Union[str, Any]:
				plt.hist(self.img.ravel()	,	256	,	[0, 256]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			str:
				cva.imshow("Output-Image"	,	self.img						)
				cva.imshow("Input-Image"	,	self.original_image						)
				cva.waitKey(5_000						)
				cva.destroyAllWindows()


if __name__ == "__main__":
			lowerCamelCase_					=      os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
			lowerCamelCase_					=      ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()



