




'''simple docstring'''





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

import sentencepiece as spm

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


if is_torch_available():
			import torch


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


__UpperCAmelCase       =		logging.get_logger(__name__)
__UpperCAmelCase       =		{"vocab_file": "spiece.model"}

__UpperCAmelCase       =		{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

__UpperCAmelCase       =		{
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}
class 		SCREAMING_SNAKE_CASE			(     snake_case							):




				'''simple docstring'''





				__UpperCamelCase     				=   VOCAB_FILES_NAMES
				__UpperCamelCase     				=   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase     				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase     				=   ["input_ids", "attention_mask"]







				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__=False  ,						SCREAMING_SNAKE_CASE__=False  ,						SCREAMING_SNAKE_CASE__=False  ,						SCREAMING_SNAKE_CASE__=None  ,						SCREAMING_SNAKE_CASE__=None  ,						SCREAMING_SNAKE_CASE__=None  ,						SCREAMING_SNAKE_CASE__=None  ,						SCREAMING_SNAKE_CASE__ = None  ,						**SCREAMING_SNAKE_CASE__  ,						):
							'''simple docstring'''







							snake_case:       List[str]        =       {} if sp_model_kwargs is None else sp_model_kwargs

							snake_case:       Optional[int]        =       kwargs.get('name_or_path'				)
							if name_or_path is None:
										logger.warning(
										    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
										    ' you are testing the model, this can safely be ignored'				)
										snake_case:       Tuple        =       'None'

							# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
							snake_case:       Tuple        =       '<|endoftext|>' if eos_token is None else eos_token
							snake_case:       Optional[int]        =       '<unk>' if unk_token is None else unk_token
							if "gpt-sw3-7b" in name_or_path:
										snake_case:       Optional[Any]        =       unk_token if pad_token is None else pad_token
										snake_case:       Optional[int]        =       eos_token if bos_token is None else bos_token
							else:
										snake_case:       Optional[int]        =       '<pad>' if pad_token is None else pad_token
										snake_case:       Optional[Any]        =       '<s>' if bos_token is None else bos_token

							super().__init__(
							    do_lower_case=SCREAMING_SNAKE_CASE__  ,						remove_space=SCREAMING_SNAKE_CASE__  ,						keep_accents=SCREAMING_SNAKE_CASE__  ,						bos_token=SCREAMING_SNAKE_CASE__  ,						eos_token=SCREAMING_SNAKE_CASE__  ,						unk_token=SCREAMING_SNAKE_CASE__  ,						pad_token=SCREAMING_SNAKE_CASE__  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**SCREAMING_SNAKE_CASE__  ,						)

							snake_case:       Optional[Any]        =       do_lower_case
							snake_case:       Optional[Any]        =       remove_space
							snake_case:       Dict        =       keep_accents
							snake_case:       List[Any]        =       vocab_file

							snake_case:       Optional[Any]        =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
							self.sp_model.Load(SCREAMING_SNAKE_CASE__				)

							# Used for whitespace normalization in input texts
							# fmt : off
							snake_case:       str        =       {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
							# fmt : on

							# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
							snake_case:       Optional[Any]        =       re.compile(
							    F"""[{''.join(map(SCREAMING_SNAKE_CASE__  ,						list(range(0  ,						9				)				) + list(range(11  ,						32				)				) + list(range(1_27  ,						1_60				)				) + [1_60, 1_73, 82_03]				)				)}]"""				)







				def __getstate__(   self				):
							'''simple docstring'''







							snake_case:       Optional[int]        =       self.__dict__.copy()
							snake_case:       Dict        =       None
							return state







				def __setstate__(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Tuple        =       d

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

							snake_case:       Any        =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
							self.sp_model.Load(self.vocab_file				)







				@property
				# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							return len(self.sp_model				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Dict        =       self.non_printing_characters_re.sub(''  ,						SCREAMING_SNAKE_CASE__				)

							# Normalize whitespaces
							snake_case:       Any        =       ''.join([char if char not in self.whitespaces else ' ' for char in text]				)

							# NFC Unicode normalization
							snake_case:       str        =       unicodedata.normalize('NFC'  ,						SCREAMING_SNAKE_CASE__				)
							return text







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Optional[Any]        =       self.preprocess_text(SCREAMING_SNAKE_CASE__				)
							return self.sp_model.encode(SCREAMING_SNAKE_CASE__  ,						out_type=SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__				)







				@staticmethod
				def        _UpperCamelCase							(   SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return out_string







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       str        =       []
							snake_case:       Optional[Any]        =       ''
							snake_case:       Optional[int]        =       False
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
													if not prev_is_special:
																out_string += " "

													out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__				) + token
													snake_case:       List[str]        =       True
													snake_case:       str        =       []
										else:
													current_sub_tokens.append(SCREAMING_SNAKE_CASE__				)
													snake_case:       Optional[int]        =       False
							out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__				)

							return out_string







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       str        =       {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ = None				):
							'''simple docstring'''







							if not os.path.isdir(SCREAMING_SNAKE_CASE__				):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
										return
							snake_case:       List[str]        =       os.path.join(
							    SCREAMING_SNAKE_CASE__  ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

							return (out_vocab_file,)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ = False				):
							'''simple docstring'''







							if isinstance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
										snake_case:       Optional[Any]        =       self.preprocess_text(SCREAMING_SNAKE_CASE__				)
										snake_case:       Optional[int]        =       self.sp_model.encode(SCREAMING_SNAKE_CASE__				)
							else:
										snake_case:       str        =       [self.preprocess_text(SCREAMING_SNAKE_CASE__				) for t in text]
										snake_case:       Optional[int]        =       self.sp_model.encode(SCREAMING_SNAKE_CASE__				)

							if return_tensors is True or return_tensors == "pt":
										snake_case:       str        =       torch.tensor(SCREAMING_SNAKE_CASE__				)

							return token_ids







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return self.sp_model.decode(SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Union[str, Any]        =       [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
							snake_case:       Any        =       (
							    F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(SCREAMING_SNAKE_CASE__				) + F"""{self.bos_token}Bot:"""
							)
							return self.encode(text=SCREAMING_SNAKE_CASE__				)





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     int       =       len(lowercase__						)
				# We need to create solution object to save path.
				_lowerCamelCase	:     Tuple       =       [[0 for _ in range(lowercase__						)] for _ in range(lowercase__						)]
				_lowerCamelCase	:     Optional[Any]       =       run_maze(lowercase__				,       0				,       0				,       lowercase__						)
				if solved:
								print('\n'.join(str(lowercase__						) for row in solutions						)						)
				else:
								print('No solution exists!'						)
				return solved







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Optional[int]       =       len(lowercase__						)
				# Final check point.
				if i == j == (size - 1):
								_lowerCamelCase	:     Optional[Any]       =       1
								return True

				_lowerCamelCase	:     List[str]       =       (not i < 0) and (not j < 0)  # Check lower bounds
				_lowerCamelCase	:     List[str]       =       (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								_lowerCamelCase	:     List[Any]       =       (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												_lowerCamelCase	:     Union[str, Any]       =       1

												# check for directions
												if (
												    run_maze(lowercase__				,       i + 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j + 1				,       lowercase__						)
												    or run_maze(lowercase__				,       i - 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j - 1				,       lowercase__						)
												):
																return True

												_lowerCamelCase	:     Dict       =       0
												return False
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()