





"""simple docstring"""




import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


a						: List[str]     	=			logging.get_logger(__name__)

a						: List[str]     	=			{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

a						: List[str]     	=			{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

a						: Optional[int]     	=			{
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict    ,       _lowercase						:    Tuple					) ->List[Any]:






	'''simple docstring'''






	with open(_lowercase    ,       "r"    ,       encoding="utf-8"					) as f:
		a      :		Optional[Any]	=				json.loads(f.read()					)

	a      :		str	=				collections.OrderedDict()
	a      :		Any	=				collections.OrderedDict()
	a      :		str	=				collections.OrderedDict()
	with open(_lowercase    ,       "r"    ,       encoding="utf-8"					) as f:
		a      :		Optional[int]	=				f.readlines()
	a      :		Any	=				[[t.rstrip("\n"					)] if (t == "," or "," not in t) else t.rstrip("\n"					).split(","					) for t in token]
	for idx, b in enumerate(_lowercase					):
		a      :		Optional[int]	=				b
		a      :		Dict	=				idx
		for wd in b:
			a      :		List[Any]	=				idx

	return vocab, raw_vocab, ids_to_tokens, emoji


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Optional[int]      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					Union[str, Any]      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					Dict      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					int      					=["""input_ids""", """attention_mask"""]






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__="<|endoftext|>"      ,					lowerCAmelCase__="<|endoftext|>"      ,					lowerCAmelCase__="<|startoftext|>"      ,					lowerCAmelCase__="<|endoftext|>"      ,					lowerCAmelCase__=False      ,					**lowerCAmelCase__      ,					)	-> int:
								super().__init__(
								    unk_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					bos_token=lowerCAmelCase__      ,					eos_token=lowerCAmelCase__      ,					do_clean_text=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)
								if not os.path.isfile(lowerCAmelCase__      ):
									raise ValueError(
									    f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
									    " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"      )
								if not os.path.isfile(lowerCAmelCase__      ):
									raise ValueError(
									    f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
									    " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"      )
								a      :		Tuple	=				do_clean_text
								a,       a,       a,       a      :		Any	=				load_vocab_and_emoji(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		int	=				SubWordJapaneseTokenizer(
								    vocab=self.vocab      ,					ids_to_tokens=self.ids_to_tokens      ,					emoji=self.emoji      )






							@property
							def     __a   (   self      )	-> Optional[int]:
								# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
								return len(self.raw_vocab      )






							def     __a   (   self      )	-> Union[str, Any]:
								return dict(self.raw_vocab      ,					**self.added_tokens_encoder      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								return self.subword_tokenizer.tokenize(lowerCAmelCase__      ,					clean=self.do_clean_text      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								return self.vocab.get(lowerCAmelCase__      ,					self.vocab.get(self.unk_token      )      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								return self.subword_tokenizer.convert_id_to_token(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								a      :		Union[str, Any]	=				"".join(lowerCAmelCase__      ).strip()
								return out_string






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[int]:
								a      :		Union[str, Any]	=				[]
								for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(lowerCAmelCase__      ,					add_special_tokens=lowerCAmelCase__      ) + [self.eos_token_id]      )

								if len(lowerCAmelCase__      ) > self.model_max_length:
									a      :		Optional[Any]	=				input_ids[-self.model_max_length :]
								return input_ids






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								a      :		Optional[int]	=				0
								if os.path.isdir(lowerCAmelCase__      ):
									a      :		List[str]	=				os.path.join(
									    lowerCAmelCase__      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )
									a      :		List[str]	=				os.path.join(
									    lowerCAmelCase__      ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]      )
								else:
									a      :		Any	=				(
									    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
									)
									a      :		List[Any]	=				(
									    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
									)
								with open(lowerCAmelCase__      ,					"w"      ,					encoding="utf-8"      ) as writer:
									for token_index, token in self.ids_to_tokens.items():
										if index != token_index:
											logger.warning(
											    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
											    " Please check that the vocabulary is not corrupted!"      )
											a      :		str	=				token_index
										writer.write(",".join(lowerCAmelCase__      ) + "\n"      )
										index += 1
								with open(lowerCAmelCase__      ,					"w"      ,					encoding="utf-8"      ) as writer:
									json.dump(self.emoji      ,					lowerCAmelCase__      )
								return vocab_file, emoji_file


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Any	=				vocab  # same as swe
								a      :		List[str]	=				ids_to_tokens  # same as bpe
								a      :		Optional[Any]	=				emoji
								a      :		Any	=				np.max([len(lowerCAmelCase__      ) for w in self.vocab.keys()]      )
								a      :		Dict	=				re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"      )
								a      :		Tuple	=				re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"      )
								a      :		Union[str, Any]	=				re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"      )
								a      :		Optional[Any]	=				re.compile(
								    R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"      )
								a      :		Dict	=				re.compile(
								    R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"      )
								a      :		List[str]	=				re.compile(
								    R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"      )
								a      :		Optional[int]	=				"─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
								a      :		Optional[Any]	=				"▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
								a      :		List[str]	=				str.maketrans({k: "<BLOCK>" for k in keisen + blocks}      )






							def __len__(   self      )	-> Union[str, Any]:
								return len(self.ids_to_tokens      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Any:
								a      :		List[str]	=				self.content_repattera.sub("<URL>"      ,					lowerCAmelCase__      )
								a      :		Optional[int]	=				self.content_repattera.sub("<EMAIL>"      ,					lowerCAmelCase__      )
								a      :		Optional[Any]	=				self.content_repattera.sub("<TEL>"      ,					lowerCAmelCase__      )
								a      :		Dict	=				self.content_repattera.sub("<DATE>"      ,					lowerCAmelCase__      )
								a      :		Any	=				self.content_repattera.sub("<DATE>"      ,					lowerCAmelCase__      )
								a      :		int	=				self.content_repattera.sub("<PRICE>"      ,					lowerCAmelCase__      )
								a      :		int	=				content.translate(self.content_transa      )
								while "<BLOCK><BLOCK>" in content:
									a      :		int	=				content.replace("<BLOCK><BLOCK>"      ,					"<BLOCK>"      )
								return content






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__=False      )	-> Any:
								a      :		List[str]	=				text.replace(" "      ,					"<SP>"      )
								a      :		Optional[Any]	=				text.replace("　"      ,					"<SP>"      )
								a      :		Union[str, Any]	=				text.replace("\r\n"      ,					"<BR>"      )
								a      :		List[Any]	=				text.replace("\n"      ,					"<BR>"      )
								a      :		Optional[Any]	=				text.replace("\r"      ,					"<BR>"      )
								a      :		List[Any]	=				text.replace("\t"      ,					"<TAB>"      )
								a      :		Dict	=				text.replace("—"      ,					"ー"      )
								a      :		Optional[Any]	=				text.replace("−"      ,					"ー"      )
								for k, v in self.emoji["emoji"].items():
									if k in text:
										a      :		List[str]	=				text.replace(lowerCAmelCase__      ,					lowerCAmelCase__      )
								if clean:
									a      :		Any	=				self.clean_text(lowerCAmelCase__      )

								def check_simbol(lowerCAmelCase__      ):
									a      :		int	=				x.encode()
									if len(lowerCAmelCase__      ) == 1 and len(lowerCAmelCase__      ) == 2:
										a      :		Any	=				(int(e[0]      ) << 8) + int(e[1]      )
										if (
										    (c >= 0xc_2a1 and c <= 0xc_2bf)
										    or (c >= 0xc_780 and c <= 0xc_783)
										    or (c >= 0xc_ab9 and c <= 0xc_bbf)
										    or (c >= 0xc_c80 and c <= 0xc_da2)
										):
											return True
									return False

								def checkuae(lowerCAmelCase__      ):
									a      :		Union[str, Any]	=				x.encode()
									if len(lowerCAmelCase__      ) == 1 and len(lowerCAmelCase__      ) == 3:
										a      :		str	=				(int(e[0]      ) << 16) + (int(e[1]      ) << 8) + int(e[2]      )
										if c >= 0xe28_080 and c <= 0xe2b_07f:
											return True
									return False

								a      :		str	=				0
								a      :		int	=				[]
								while pos < len(lowerCAmelCase__      ):
									a      :		Optional[Any]	=				min(len(lowerCAmelCase__      )      ,					pos + self.maxlen + 1      ) if text[pos] == "<" else pos + 3
									a      :		Optional[Any]	=				[]  # (token_id, token, pos)
									for e in range(lowerCAmelCase__      ,					lowerCAmelCase__      ,					-1      ):
										a      :		str	=				text[pos:e]
										if wd in self.vocab:
											if wd[0] == "<" and len(lowerCAmelCase__      ) > 2:
												a      :		Optional[int]	=				[(self.vocab[wd], wd, e)]
												break
											else:
												candidates.append((self.vocab[wd], wd, e)      )
									if len(lowerCAmelCase__      ) > 0:
										# the smallest token_id is adopted
										a,       a,       a      :		Any	=				sorted(lowerCAmelCase__      ,					key=lambda lowerCAmelCase__      : x[0]      )[0]
										result.append(lowerCAmelCase__      )
										a      :		Any	=				e
									else:
										a      :		List[Any]	=				pos + 1
										a      :		Union[str, Any]	=				text[pos:end]
										if check_simbol(lowerCAmelCase__      ):
											result.append("<KIGOU>"      )
										elif checkuae(lowerCAmelCase__      ):
											result.append("<U2000U2BFF>"      )
										else:
											for i in wd.encode("utf-8"      ):
												result.append("<|byte%d|>" % i      )
										a      :		List[Any]	=				end
								return result






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__="\n"      )	-> str:
								a      :		Union[str, Any]	=				[]
								a      :		Union[str, Any]	=				[]
								a      :		int	=				self.ids_to_tokens[index][0]
								if word[:6] == "<|byte" and word[-2:] == "|>":
									byte_tokens.append(int(word[6:-2]      )      )
								else:
									if len(lowerCAmelCase__      ) > 0:
										words.append(bytearray(lowerCAmelCase__      ).decode("utf-8"      ,					errors="replace"      )      )
										a      :		Union[str, Any]	=				[]
									if word[:7] == "<|emoji" and word[-2:] == "|>":
										words.append(self.emoji["emoji_inv"][word]      )
									elif word == "<SP>":
										words.append(" "      )
									elif word == "<BR>":
										words.append(lowerCAmelCase__      )
									elif word == "<TAB>":
										words.append("\t"      )
									elif word == "<BLOCK>":
										words.append("▀"      )
									elif word == "<KIGOU>":
										words.append("ǀ"      )
									elif word == "<U2000U2BFF>":
										words.append("‖"      )
									else:
										words.append(lowerCAmelCase__      )
								if len(lowerCAmelCase__      ) > 0:
									words.append(bytearray(lowerCAmelCase__      ).decode("utf-8"      ,					errors="replace"      )      )
								a      :		Optional[Any]	=				"".join(lowerCAmelCase__      )
								return text









"""simple docstring"""




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(_lowercase    ,       _lowercase					)					)					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->list[list[list[float] | float]]:






	'''simple docstring'''






	if dataset.ndim != value_array.ndim:
		a      :		str	=				(
		    "Wrong input data's dimensions... "
		    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
		)
		raise ValueError(_lowercase					)

	try:
		if dataset.shape[1] != value_array.shape[1]:
			a      :		int	=				(
			    "Wrong input data's shape... "
			    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
			)
			raise ValueError(_lowercase					)
	except IndexError:
		if dataset.ndim != value_array.ndim:
			raise TypeError("Wrong shape"					)

	if dataset.dtype != value_array.dtype:
		a      :		Union[str, Any]	=				(
		    "Input data have different datatype... "
		    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
		)
		raise TypeError(_lowercase					)

	a      :		str	=				[]

	for value in value_array:
		a      :		List[Any]	=				euclidean(_lowercase    ,       dataset[0]					)
		a      :		str	=				dataset[0].tolist()

		for dataset_value in dataset[1:]:
			a      :		Tuple	=				euclidean(_lowercase    ,       _lowercase					)

			if dist > temp_dist:
				a      :		Dict	=				temp_dist
				a      :		Optional[int]	=				dataset_value.tolist()

		answer.append([vector, dist]					)

	return answer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    np.ndarray    ,       _lowercase						:    np.ndarray					) ->float:






	'''simple docstring'''






	return np.dot(_lowercase    ,       _lowercase					) / (norm(_lowercase					) * norm(_lowercase					))


if __name__ == "__main__":
		import doctest

		doctest.testmod()


