






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


__a   :							str						= logging.get_logger(__name__)
__a   :							Tuple						= {"vocab_file": "spiece.model"}

__a   :							Optional[Any]						= {
    "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",
    }
}

__a   :							int						= {
    "AI-Sweden/gpt-sw3-126m": 2048,
    "AI-Sweden/gpt-sw3-350m": 2048,
    "AI-Sweden/gpt-sw3-1.6b": 2048,
    "AI-Sweden/gpt-sw3-6.7b": 2048,
    "AI-Sweden/gpt-sw3-20b": 2048,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]




     def __init__(					self							:      str			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      int=False			,						UpperCamelCase_							:      Tuple=False			,						UpperCamelCase_							:      Tuple=False			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      Union[str, Any]=None			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Optional[Dict[str, Any]] = None			,						**UpperCamelCase_							:      Any			,						):


         """simple docstring"""



         __A       =						{} if sp_model_kwargs is None else sp_model_kwargs

         __A       =						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"""					)
             __A       =						"""None"""

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

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

         __A       =						do_lower_case
         __A       =						remove_space
         __A       =						keep_accents
         __A       =						vocab_file

         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(UpperCamelCase_					)

         # Used for whitespace normalization in input texts
         # fmt : off
         __A       =						{""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         __A       =						re.compile(
             F"[{''.join(map(UpperCamelCase_			,						list(range(0			,						9					)					) + list(range(11			,						32					)					) + list(range(127			,						160					)					) + [160, 173, 8_203]					)					)}]"					)




     def __getstate__(					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.__dict__.copy()
         __A       =						None
         return state




     def __setstate__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						d

         # for backward compatibility
         if not hasattr(self			,						"""sp_model_kwargs"""					):
             __A       =						{}

         __A       =						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 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         return len(self.sp_model					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						self.non_printing_characters_re.sub(""""""			,						UpperCamelCase_					)

         # Normalize whitespaces
         __A       =						"""""".join([char if char not in self.whitespaces else """ """ for char in text]					)

         # NFC Unicode normalization
         __A       =						unicodedata.normalize("""NFC"""			,						UpperCamelCase_					)
         return text




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      str			,						**UpperCamelCase_							:      str					):


         """simple docstring"""



         __A       =						self.preprocess_text(UpperCamelCase_					)
         return self.sp_model.encode(UpperCamelCase_			,						out_type=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         return self.sp_model.PieceToId(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         return self.sp_model.IdToPiece(UpperCamelCase_					)




     @staticmethod
     def 		lowerCAmelCase_  (					UpperCamelCase_							:      str					):


         """simple docstring"""



         return out_string




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         __A       =						[]
         __A       =						""""""
         __A       =						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(UpperCamelCase_					) + token
                 __A       =						True
                 __A       =						[]
             else:
                 current_sub_tokens.append(UpperCamelCase_					)
                 __A       =						False
         out_string += self.sp_model.decode(UpperCamelCase_					)

         return out_string




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						{self.convert_ids_to_tokens(UpperCamelCase_					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						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
         __A       =						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:
                 __A       =						self.sp_model.serialized_model_proto()
                 fi.write(UpperCamelCase_					)

         return (out_vocab_file,)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Union[str, List[str]]			,						UpperCamelCase_							:      Union[str, bool] = False					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						UpperCamelCase_					):
             __A       =						self.preprocess_text(UpperCamelCase_					)
             __A       =						self.sp_model.encode(UpperCamelCase_					)
         else:
             __A       =						[self.preprocess_text(UpperCamelCase_					) for t in text]
             __A       =						self.sp_model.encode(UpperCamelCase_					)

         if return_tensors is True or return_tensors == "pt":
             __A       =						torch.tensor(UpperCamelCase_					)

         return token_ids




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      Union[int, List[int]]					):


         """simple docstring"""



         return self.sp_model.decode(UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      "Conversation"					):


         """simple docstring"""



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












from __future__ import annotations

from PIL import Image

# Define glider example
__a   :							Tuple						= [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__a   :							Optional[Any]						= [[0, 1, 0], [0, 1, 0], [0, 1, 0]]



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]     )    ->   list[list[int]]:





    """simple docstring"""

    __A       =						[]
    for i in range(len(__lowercase     )     ):
        __A       =						[]
        for j in range(len(cells[i]     )     ):
            # Get the number of live neighbours
            __A       =						0
            if i > 0 and j > 0:
                neighbour_count += cells[i - 1][j - 1]
            if i > 0:
                neighbour_count += cells[i - 1][j]
            if i > 0 and j < len(cells[i]     ) - 1:
                neighbour_count += cells[i - 1][j + 1]
            if j > 0:
                neighbour_count += cells[i][j - 1]
            if j < len(cells[i]     ) - 1:
                neighbour_count += cells[i][j + 1]
            if i < len(__lowercase     ) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(__lowercase     ) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(__lowercase     ) - 1 and j < len(cells[i]     ) - 1:
                neighbour_count += cells[i + 1][j + 1]

            # Rules of the game of life (excerpt from Wikipedia):
            # 1. Any live cell with two or three live neighbours survives.
            # 2. Any dead cell with three live neighbours becomes a live cell.
            # 3. All other live cells die in the next generation.
            #    Similarly, all other dead cells stay dead.
            __A       =						cells[i][j] == 1
            if (
                (alive and 2 <= neighbour_count <= 3)
                or not alive
                and neighbour_count == 3
            ):
                next_generation_row.append(1     )
            else:
                next_generation_row.append(0     )

        next_generation.append(__lowercase     )
    return next_generation



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[list[int]]      ,					__lowercase :      int     )    ->   list[Image.Image]:





    """simple docstring"""

    __A       =						[]
    for _ in range(__lowercase     ):
        # Create output image
        __A       =						Image.new("""RGB"""      ,					(len(cells[0]     ), len(__lowercase     ))     )
        __A       =						img.load()

        # Save cells to image
        for x in range(len(__lowercase     )     ):
            for y in range(len(cells[0]     )     ):
                __A       =						2_5_5 - cells[y][x] * 2_5_5
                __A       =						(colour, colour, colour)

        # Save image
        images.append(__lowercase     )
        __A       =						new_generation(__lowercase     )
    return images


if __name__ == "__main__":
  __a   :							Optional[int]						= generate_images(GLIDER, 16)
  images[0].save("out.gif", save_all=True, append_images=images[1:])




