






"""simple docstring"""

import os
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


lowercase__          = logging.get_logger(__name__)

lowercase__          = """▁"""

lowercase__          = {"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase__          = {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase__          = {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}


class 		__lowerCamelCase				( snake_case_			):





		'''simple docstring'''






		a_	:				str            =							VOCAB_FILES_NAMES
		a_	:				List[str]            =							PRETRAINED_VOCAB_FILES_MAP
		a_	:				str            =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_	:				Optional[Any]            =							["""input_ids""", """attention_mask"""]





		def __init__(   self							:	Dict						,			a_							:	Optional[Any]						,			a_							:	Optional[int]="<s>"						,			a_							:	Dict="</s>"						,			a_							:	Optional[Any]="</s>"						,			a_							:	Union[str, Any]="<s>"						,			a_							:	Tuple="<unk>"						,			a_							:	List[str]="<pad>"						,			a_							:	List[str]="<mask>"						,			a_							:	Tuple = None						,			**a_							:	str						,			):
				# Mask token behave like a normal word, i.e. include the space before it
				lowerCAmelCase_   :							Tuple									=			AddedToken(a_						,			lstrip=a_						,			rstrip=a_	) if isinstance(a_						,			a_	) else mask_token

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

				super().__init__(
				    bos_token=a_						,			eos_token=a_						,			unk_token=a_						,			sep_token=a_						,			cls_token=a_						,			pad_token=a_						,			mask_token=a_						,			sp_model_kwargs=self.sp_model_kwargs						,			**a_						,			)

				lowerCAmelCase_   :							Dict									=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.Load(str(a_	)	)
				lowerCAmelCase_   :							str									=			vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				lowerCAmelCase_   :							List[str]									=			{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				lowerCAmelCase_   :							int									=			1

				lowerCAmelCase_   :							Any									=			len(self.sp_model	) + self.fairseq_offset
				lowerCAmelCase_   :							int									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}





		def __getstate__(   self							:	Tuple	):
				lowerCAmelCase_   :							Dict									=			self.__dict__.copy()
				lowerCAmelCase_   :							int									=			None
				lowerCAmelCase_   :							str									=			self.sp_model.serialized_model_proto()
				return state





		def __setstate__(   self							:	int						,			a_							:	Dict	):
				lowerCAmelCase_   :							str									=			d

				# for backward compatibility
				if not hasattr(self						,			"sp_model_kwargs"	):
						lowerCAmelCase_   :							Optional[int]									=			{}

				lowerCAmelCase_   :							List[str]									=			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	List[Any]						,			a_							:	Optional[Any] = None	):

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





		def    lowerCamelCase       (   self							:	List[Any]						,			a_							:	Union[str, Any]						,			a_							:	List[Any] = None						,			a_							:	Optional[Any] = False	):

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

				if token_ids_a is None:
						return [1] + ([0] * len(a_	)) + [1]
				return [1] + ([0] * len(a_	)) + [1, 1] + ([0] * len(a_	)) + [1]





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	Union[str, Any]						,			a_							:	Union[str, Any] = None	):
				lowerCAmelCase_   :							Union[str, Any]									=			[self.sep_token_id]
				lowerCAmelCase_   :							List[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 + sep + token_ids_a + sep	) * [0]





		@property
		def    lowerCamelCase       (   self							:	str	):
				return len(self.sp_model	) + self.fairseq_offset + 1  # Add the <mask> token





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Optional[Any]									=			{self.convert_ids_to_tokens(a_	): i for i in range(self.vocab_size	)}
				vocab.update(self.added_tokens_encoder	)
				return vocab





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	str	):
				return self.sp_model.encode(a_						,			out_type=a_	)





		def    lowerCamelCase       (   self							:	Optional[int]						,			a_							:	int	):
				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				lowerCAmelCase_   :							Tuple									=			self.sp_model.PieceToId(a_	)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





		def    lowerCamelCase       (   self							:	List[str]						,			a_							:	Optional[int]	):
				if index in self.fairseq_ids_to_tokens:
						return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset	)





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	List[Any]	):
				lowerCAmelCase_   :							Dict									=			"".join(a_	).replace(a_						,			" "	).strip()
				return out_string





		def    lowerCamelCase       (   self							:	Union[str, Any]						,			a_							:	Optional[Any]						,			a_							:	int = None	):
				if not os.path.isdir(a_	):
						logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
						return
				lowerCAmelCase_   :							List[str]									=			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_	) and os.path.isfile(self.vocab_file	):
						copyfile(self.vocab_file						,			a_	)
				elif not os.path.isfile(self.vocab_file	):
						with open(a_						,			"wb"	) as fi:
								lowerCAmelCase_   :							Union[str, Any]									=			self.sp_model.serialized_model_proto()
								fi.write(a_	)

				return (out_vocab_file,)







'''simple docstring'''

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

lowercase_					= """Usage of script: script_name <size_of_canvas:int>"""

lowercase_					= [0] * 100 + [1] * 10
random.shuffle(choice)





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			[[False for i in range(__lowerCamelCase    )] for j in range(__lowerCamelCase    )]
    return canvas





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->None:
    for i, row in enumerate(__lowerCamelCase    ):
        for j, _ in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			bool(random.getrandbits(1    )    )





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			np.array(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowerCamelCase    ):
        for c, pt in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			__judge_point(
                __lowerCamelCase					,       current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

    _SCREAMING_SNAKE_CASE									=			next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    _SCREAMING_SNAKE_CASE									=			current_canvas.tolist()
    return return_canvas





def lowerCamelCase						(     __lowerCamelCase :    bool					,       __lowerCamelCase :    list[list[bool]]    )						->bool:
    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    _SCREAMING_SNAKE_CASE									=			pt
    if pt:
        if alive < 2:
            _SCREAMING_SNAKE_CASE									=			False
        elif alive == 2 or alive == 3:
            _SCREAMING_SNAKE_CASE									=			True
        elif alive > 3:
            _SCREAMING_SNAKE_CASE									=			False
    else:
        if alive == 3:
            _SCREAMING_SNAKE_CASE									=			True

    return state


if __name__ == "__main__":
      if len(sys.argv) != 2:
            raise Exception(usage_doc)

      lowercase_					= int(sys.argv[1])
      # main working structure of this module.
      lowercase_					= create_canvas(canvas_size)
      seed(c)
      lowercase_				,			lowercase_					= plt.subplots()
      fig.show()
      lowercase_					= ListedColormap(["""w""", """k"""])
      try:
            while True:
                  lowercase_					= run(c)
                  ax.matshow(c, cmap=cmap)
                  fig.canvas.draw()
                  ax.cla()
      except KeyboardInterrupt:
            # do nothing.
            pass



