import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase	: int    		=  logging.get_logger(__name__)
__UpperCamelCase	: Any    		=  {'vocab_file': 'spiece.model'}

__UpperCamelCase	: str    		=  {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

__UpperCamelCase	: List[Any]    		=  {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

__UpperCamelCase	: Dict    		=  '▁'





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


						def __init__( self			:       List[Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       str=False					,	UpperCamelCase__			:       Optional[int]="[CLS]"					,	UpperCamelCase__			:       str="[SEP]"					,	UpperCamelCase__			:       Optional[int]="<unk>"					,	UpperCamelCase__			:       str="[SEP]"					,	UpperCamelCase__			:       Optional[int]="<pad>"					,	UpperCamelCase__			:       Optional[Any]="[CLS]"					,	UpperCamelCase__			:       List[str]="[MASK]"					,	UpperCamelCase__			:       Optional[Dict[str, Any]] = None					,	**UpperCamelCase__			:       List[Any]					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							(
												    AddedToken(UpperCamelCase__					,	lstrip=UpperCamelCase__					,	rstrip=UpperCamelCase__					,	normalized=UpperCamelCase__		)
												    if isinstance(UpperCamelCase__					,	UpperCamelCase__		)
												    else mask_token
												)

												SCREAMING_SNAKE_CASE							:					str           =							{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    do_lower_case=UpperCamelCase__					,	remove_space=UpperCamelCase__					,	keep_accents=UpperCamelCase__					,	bos_token=UpperCamelCase__					,	eos_token=UpperCamelCase__					,	unk_token=UpperCamelCase__					,	sep_token=UpperCamelCase__					,	pad_token=UpperCamelCase__					,	cls_token=UpperCamelCase__					,	mask_token=UpperCamelCase__					,	sp_model_kwargs=self.sp_model_kwargs					,	**UpperCamelCase__					,	)

												SCREAMING_SNAKE_CASE							:					str           =							do_lower_case
												SCREAMING_SNAKE_CASE							:					List[str]           =							remove_space
												SCREAMING_SNAKE_CASE							:					Tuple           =							keep_accents
												SCREAMING_SNAKE_CASE							:					Tuple           =							vocab_file

												SCREAMING_SNAKE_CASE							:					List[str]           =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(UpperCamelCase__		)


						@property
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												return len(self.sp_model		)


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							{self.convert_ids_to_tokens(UpperCamelCase__		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab


						def __getstate__( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.__dict__.copy()
												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												return state


						def __setstate__( self			:       List[Any]					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							d

												# for backward compatibility
												if not hasattr(self					,	'''sp_model_kwargs'''		):
																		SCREAMING_SNAKE_CASE							:					str           =							{}

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(self.vocab_file		)


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if self.remove_space:
																		SCREAMING_SNAKE_CASE							:					str           =							''' '''.join(inputs.strip().split()		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							inputs
												SCREAMING_SNAKE_CASE							:					List[str]           =							outputs.replace('''``'''					,	'''"'''		).replace('''\'\''''					,	'''"'''		)

												if not self.keep_accents:
																		SCREAMING_SNAKE_CASE							:					Any           =							unicodedata.normalize('''NFKD'''					,	UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					int           =							''''''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase__		)]		)
												if self.do_lower_case:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							outputs.lower()

												return outputs


						def  __A ( self			:       Any					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.preprocess_text(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.sp_model.encode(UpperCamelCase__					,	out_type=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					str           =							[]
												for piece in pieces:
																		if len(UpperCamelCase__		) > 1 and piece[-1] == str(''','''		) and piece[-2].isdigit():
																								SCREAMING_SNAKE_CASE							:					Any           =							self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__					,	''''''		)		)
																								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																														if len(cur_pieces[0]		) == 1:
																																				SCREAMING_SNAKE_CASE							:					Tuple           =							cur_pieces[1:]
																														else:
																																				SCREAMING_SNAKE_CASE							:					List[Any]           =							cur_pieces[0][1:]
																								cur_pieces.append(piece[-1]		)
																								new_pieces.extend(UpperCamelCase__		)
																		else:
																								new_pieces.append(UpperCamelCase__		)

												return new_pieces


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return self.sp_model.PieceToId(UpperCamelCase__		)


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       Tuple		):





												'''simple docstring'''





												return self.sp_model.IdToPiece(UpperCamelCase__		)


						def  __A ( self			:       List[Any]					,	UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							[]
												SCREAMING_SNAKE_CASE							:					List[str]           =							''''''
												SCREAMING_SNAKE_CASE							:					str           =							False
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								if not prev_is_special:
																														out_string += " "
																								out_string += self.sp_model.decode(UpperCamelCase__		) + token
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
																								SCREAMING_SNAKE_CASE							:					List[str]           =							[]
																		else:
																								current_sub_tokens.append(UpperCamelCase__		)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							False
												out_string += self.sp_model.decode(UpperCamelCase__		)
												return out_string.strip()


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





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


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None					,	UpperCamelCase__			:       bool = False		):





												'''simple docstring'''





												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=UpperCamelCase__					,	token_ids_a=UpperCamelCase__					,	already_has_special_tokens=UpperCamelCase__		)

												if token_ids_a is not None:
																		return [1] + ([0] * len(UpperCamelCase__		)) + [1] + ([0] * len(UpperCamelCase__		)) + [1]
												return [1] + ([0] * len(UpperCamelCase__		)) + [1]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[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			:       Dict					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





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

												if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		) and os.path.isfile(self.vocab_file		):
																		copyfile(self.vocab_file					,	UpperCamelCase__		)
												elif not os.path.isfile(self.vocab_file		):
																		with open(UpperCamelCase__					,	'''wb'''		) as fi:
																								SCREAMING_SNAKE_CASE							:					int           =							self.sp_model.serialized_model_proto()
																								fi.write(UpperCamelCase__		)

												return (out_vocab_file,)






from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							analyze_text(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							list(''' ''' + ascii_lowercase		)
						# what is our total sum of probabilities.
						SCREAMING_SNAKE_CASE							:					Tuple           =							sum(single_char_strings.values()		)

						# one length string
						SCREAMING_SNAKE_CASE							:					Tuple           =							0
						# for each alpha we go in our dict and if it is in it we calculate entropy
						for ch in my_alphas:
												if ch in single_char_strings:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							single_char_strings[ch]
																		SCREAMING_SNAKE_CASE							:					List[str]           =							my_str / all_sum
																		my_fir_sum += prob * math.loga(_lowercase		)  # entropy formula.

    # print entropy
						print(f"""{round(-1 * my_fir_sum		):.1f}"""		)

						# two len string
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							sum(two_char_strings.values()		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							0
						# for each alpha (two in size) calculate entropy.
						for cha in my_alphas:
												for cha in my_alphas:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							cha + cha
																		if sequence in two_char_strings:
																								SCREAMING_SNAKE_CASE							:					Any           =							two_char_strings[sequence]
																								SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		) / all_sum
																								my_sec_sum += prob * math.loga(_lowercase		)

    # print second entropy
						print(f"""{round(-1 * my_sec_sum		):.1f}"""		)

						# print the difference between them
						print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)		):.1f}"""		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							Counter()  # type: ignore
						SCREAMING_SNAKE_CASE							:					Any           =							Counter()  # type: ignore
						single_char_strings[text[-1]] += 1

						# first case when we have space at start.
						two_char_strings[" " + text[0]] += 1
						for i in range(0	,						len(_lowercase		) - 1		):
												single_char_strings[text[i]] += 1
												two_char_strings[text[i : i + 2]] += 1
						return single_char_strings, two_char_strings
def    A			(					):
						import doctest

						doctest.testmod()
						# text = (
						#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
						#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
						#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
						#     "beloved evident through no service elderly is. Blind there if every no so "
						#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
						#     "of message cottage windows do besides against uncivil.  Delightful "
						#     "unreserved impossible few estimating men favourable see entreaties. She "
						#     "propriety immediate was improving. He or entrance humoured likewise "
						#     "moderate. Much nor game son say feel. Fat make met can must form into "
						#     "gate. Me we offending prevailed discovery. "
						# )

						# calculate_prob(text)


if __name__ == "__main__":
					main()





