
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   =     logging.get_logger(__name__)

_A   =     {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

_A   =     {
    '''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   =     {
    '''abeja/gpt-neox-japanese-2.7b''': 20_48,
}






def    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			with open(_lowercase    ,							"r"    ,							encoding="utf-8"				) as f:
						lowerCAmelCase_						=	json.loads(f.read()				)

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

			return vocab, raw_vocab, ids_to_tokens, emoji





class 	_lowerCAmelCase					(	a__       ):
				_lowercase					=VOCAB_FILES_NAMES
				_lowercase					=PRETRAINED_VOCAB_FILES_MAP
				_lowercase					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase					=["""input_ids""", """attention_mask"""]




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase="<|endoftext|>"				,				_UpperCamelCase="<|endoftext|>"				,				_UpperCamelCase="<|startoftext|>"				,				_UpperCamelCase="<|endoftext|>"				,				_UpperCamelCase=False				,				**_UpperCamelCase				,				)				->      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)`"     )
							lowerCAmelCase_						=	do_clean_text
							lowerCAmelCase_						=	load_vocab_and_emoji(lowerCAmelCase__				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	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				,				_UpperCamelCase     )				->      Dict:
							return self.subword_tokenizer.tokenize(lowerCAmelCase__				,				clean=self.do_clean_text     )




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




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




				def      __a						(					self				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	"".join(lowerCAmelCase__     ).strip()
							return out_string




				def      __a						(					self				,				_UpperCamelCase     )				->      List[int]:
							lowerCAmelCase_						=	[]
							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:
										lowerCAmelCase_						=	input_ids[-self.model_max_length :]
							return input_ids




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None     )				->      Tuple[str]:
							lowerCAmelCase_						=	0
							if os.path.isdir(lowerCAmelCase__     ):
										lowerCAmelCase_						=	os.path.join(
										    lowerCAmelCase__				,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
										lowerCAmelCase_						=	os.path.join(
										    lowerCAmelCase__				,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]     )
							else:
										lowerCAmelCase_						=	(
										    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
										)
										lowerCAmelCase_						=	(
										    (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!"     )
																lowerCAmelCase_						=	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 	_lowerCAmelCase					(	a__       ):




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




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




				def      __a						(					self				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	self.content_repattera.sub("<URL>"				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	self.content_repattera.sub("<EMAIL>"				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	self.content_repattera.sub("<TEL>"				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	self.content_repattera.sub("<DATE>"				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	self.content_repattera.sub("<DATE>"				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	self.content_repattera.sub("<PRICE>"				,				lowerCAmelCase__     )
							lowerCAmelCase_						=	content.translate(self.content_transa     )
							while "<BLOCK><BLOCK>" in content:
										lowerCAmelCase_						=	content.replace("<BLOCK><BLOCK>"				,				"<BLOCK>"     )
							return content




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

							def check_simbol(_UpperCamelCase     ):
										lowerCAmelCase_						=	x.encode()
										if len(lowerCAmelCase__     ) == 1 and len(lowerCAmelCase__     ) == 2:
													lowerCAmelCase_						=	(int(e[0]     ) << 8) + int(e[1]     )
													if (
													    (c >= 0xC_2_A_1 and c <= 0xC_2_B_F)
													    or (c >= 0xC_7_8_0 and c <= 0xC_7_8_3)
													    or (c >= 0xC_A_B_9 and c <= 0xC_B_B_F)
													    or (c >= 0xC_C_8_0 and c <= 0xC_D_A_2)
													):
																return True
										return False

							def checkuae(_UpperCamelCase     ):
										lowerCAmelCase_						=	x.encode()
										if len(lowerCAmelCase__     ) == 1 and len(lowerCAmelCase__     ) == 3:
													lowerCAmelCase_						=	(int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
													if c >= 0xE_2_8_0_8_0 and c <= 0xE_2_B_0_7_F:
																return True
										return False

							lowerCAmelCase_						=	0
							lowerCAmelCase_						=	[]
							while pos < len(lowerCAmelCase__     ):
										lowerCAmelCase_						=	min(len(lowerCAmelCase__     )				,				pos + self.maxlen + 1     ) if text[pos] == "<" else pos + 3
										lowerCAmelCase_						=	[]  # (token_id, token, pos)
										for e in range(lowerCAmelCase__				,				lowerCAmelCase__				,				-1     ):
													lowerCAmelCase_						=	text[pos:e]
													if wd in self.vocab:
																if wd[0] == "<" and len(lowerCAmelCase__     ) > 2:
																			lowerCAmelCase_						=	[(self.vocab[wd], wd, e)]
																			break
																else:
																			candidates.append((self.vocab[wd], wd, e)     )
										if len(lowerCAmelCase__     ) > 0:
													# the smallest token_id is adopted
													lowerCAmelCase_						=	sorted(lowerCAmelCase__				,				key=lambda _UpperCamelCase     : x[0]     )[0]
													result.append(lowerCAmelCase__     )
													lowerCAmelCase_						=	e
										else:
													lowerCAmelCase_						=	pos + 1
													lowerCAmelCase_						=	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     )
													lowerCAmelCase_						=	end
							return result




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase="\n"     )				->      str:
							lowerCAmelCase_						=	[]
							lowerCAmelCase_						=	[]
							lowerCAmelCase_						=	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"     )     )
													lowerCAmelCase_						=	[]
										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"     )     )
							lowerCAmelCase_						=	"".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()


