



"""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


_lowercase			:			Optional[Any]										=						logging.get_logger(__name__)

_lowercase			:			int										=						{"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

_lowercase			:			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",
    },
}

_lowercase			:			List[str]										=						{
    "abeja/gpt-neox-japanese-2.7b": 2_0_4_8,
}






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   List[str]     ):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''r'''							,		encoding='''utf-8'''     ) as f:
						lowerCamelCase__		:		Dict		     =json.loads(f.read()     )

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

			return vocab, raw_vocab, ids_to_tokens, emoji

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							PRETRAINED_VOCAB_FILES_MAP
					_a             =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							['input_ids', 'attention_mask']



					def __init__(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  str="<|endoftext|>",  lowerCamelCase							:  Union[str, Any]="<|endoftext|>",  lowerCamelCase							:  Dict="<|startoftext|>",  lowerCamelCase							:  Dict="<|endoftext|>",  lowerCamelCase							:  Any=False,  **lowerCamelCase							:  Tuple,  )-> Optional[Any]:
								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__		:		Tuple		     =do_clean_text
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =load_vocab_and_emoji(lowerCamelCase,  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =SubWordJapaneseTokenizer(
								    vocab=self.vocab,  ids_to_tokens=self.ids_to_tokens,  emoji=self.emoji					)



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



					def 		snake_case	(	self							:  Optional[int]					)-> List[str]:
								return dict(self.raw_vocab,  **self.added_tokens_encoder					)



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Dict					)-> Optional[Any]:
								return self.subword_tokenizer.tokenize(lowerCamelCase,  clean=self.do_clean_text					)



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]					)-> Tuple:
								return self.vocab.get(lowerCamelCase,  self.vocab.get(self.unk_token					)					)



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



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Any					)-> Optional[Any]:
								lowerCamelCase__		:		Tuple		     =''''''.join(lowerCamelCase					).strip()
								return out_string



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  "Conversation"					)-> List[int]:
								lowerCamelCase__		:		int		     =[]
								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__		:		List[Any]		     =input_ids[-self.model_max_length :]
								return input_ids




					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None					)-> Tuple[str]:
								lowerCamelCase__		:		str		     =0
								if os.path.isdir(lowerCamelCase					):
											lowerCamelCase__		:		Optional[int]		     =os.path.join(
											    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
											lowerCamelCase__		:		Dict		     =os.path.join(
											    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']					)
								else:
											lowerCamelCase__		:		List[str]		     =(
											    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
											)
											lowerCamelCase__		:		Dict		     =(
											    (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__		:		Any		     =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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





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



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



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



					def 		snake_case	(	self							:  str,  lowerCamelCase							:  Any,  lowerCamelCase							:  List[Any]=False					)-> List[str]:
								lowerCamelCase__		:		List[str]		     =text.replace(''' ''',  '''<SP>'''					)
								lowerCamelCase__		:		Union[str, Any]		     =text.replace('''　''',  '''<SP>'''					)
								lowerCamelCase__		:		Optional[Any]		     =text.replace('''\r\n''',  '''<BR>'''					)
								lowerCamelCase__		:		Optional[Any]		     =text.replace('''\n''',  '''<BR>'''					)
								lowerCamelCase__		:		Optional[int]		     =text.replace('''\r''',  '''<BR>'''					)
								lowerCamelCase__		:		str		     =text.replace('''\t''',  '''<TAB>'''					)
								lowerCamelCase__		:		List[str]		     =text.replace('''—''',  '''ー'''					)
								lowerCamelCase__		:		Optional[int]		     =text.replace('''−''',  '''ー'''					)
								for k, v in self.emoji["emoji"].items():
											if k in text:
														lowerCamelCase__		:		int		     =text.replace(lowerCamelCase,  lowerCamelCase					)
								if clean:
											lowerCamelCase__		:		Optional[Any]		     =self.clean_text(lowerCamelCase					)

								def check_simbol(lowerCamelCase							:  str					):
											lowerCamelCase__		:		Union[str, Any]		     =x.encode()
											if len(lowerCamelCase					) == 1 and len(lowerCamelCase					) == 2:
														lowerCamelCase__		:		List[Any]		     =(int(e[0]					) << 8) + int(e[1]					)
														if (
														    (c >= 0xc2_a1 and c <= 0xc2_bf)
														    or (c >= 0xc7_80 and c <= 0xc7_83)
														    or (c >= 0xca_b9 and c <= 0xcb_bf)
														    or (c >= 0xcc_80 and c <= 0xcd_a2)
														):
																	return True
											return False

								def checkuae(lowerCamelCase							:  List[Any]					):
											lowerCamelCase__		:		int		     =x.encode()
											if len(lowerCamelCase					) == 1 and len(lowerCamelCase					) == 3:
														lowerCamelCase__		:		Tuple		     =(int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
														if c >= 0xe2_80_80 and c <= 0xe2_b0_7f:
																	return True
											return False

								lowerCamelCase__		:		Union[str, Any]		     =0
								lowerCamelCase__		:		Dict		     =[]
								while pos < len(lowerCamelCase					):
											lowerCamelCase__		:		List[str]		     =min(len(lowerCamelCase					),  pos + self.maxlen + 1					) if text[pos] == '''<''' else pos + 3
											lowerCamelCase__		:		List[Any]		     =[]  # (token_id, token, pos)
											for e in range(lowerCamelCase,  lowerCamelCase,  -1					):
														lowerCamelCase__		:		Any		     =text[pos:e]
														if wd in self.vocab:
																	if wd[0] == "<" and len(lowerCamelCase					) > 2:
																				lowerCamelCase__		:		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
														lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		Union[str, Any]		     =sorted(lowerCamelCase,  key=lambda lowerCamelCase					: x[0]					)[0]
														result.append(lowerCamelCase					)
														lowerCamelCase__		:		Optional[int]		     =e
											else:
														lowerCamelCase__		:		List[Any]		     =pos + 1
														lowerCamelCase__		:		Tuple		     =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__		:		List[Any]		     =end
								return result




					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Optional[int]="\n"					)-> Optional[int]:
								lowerCamelCase__		:		List[str]		     =[]
								lowerCamelCase__		:		List[str]		     =[]
								lowerCamelCase__		:		Optional[Any]		     =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__		:		List[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'''					)					)
								lowerCamelCase__		:		Tuple		     =''''''.join(lowerCamelCase					)
								return text









"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			if "model" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''model.'''							,		''''''     )
			if "norm1" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''norm1'''							,		'''attention.output.LayerNorm'''     )
			if "norm2" in orig_key:
						lowerCamelCase__		:		List[Any]		     =orig_key.replace('''norm2'''							,		'''output.LayerNorm'''     )
			if "norm" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''norm'''							,		'''LayerNorm'''     )
			if "transformer" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.split('''.'''     )[0].split('''_'''     )[-1]
						lowerCamelCase__		:		Dict		     =orig_key.replace(f'''transformer_{layer_num}'''							,		f'''encoder.layer.{layer_num}'''     )
			if "mha.attn" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''mha.attn'''							,		'''attention.self'''     )
			if "mha" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''mha'''							,		'''attention'''     )
			if "W_q" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''W_q'''							,		'''self.query'''     )
			if "W_k" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''W_k'''							,		'''self.key'''     )
			if "W_v" in orig_key:
						lowerCamelCase__		:		List[str]		     =orig_key.replace('''W_v'''							,		'''self.value'''     )
			if "ff1" in orig_key:
						lowerCamelCase__		:		Dict		     =orig_key.replace('''ff1'''							,		'''intermediate.dense'''     )
			if "ff2" in orig_key:
						lowerCamelCase__		:		Union[str, Any]		     =orig_key.replace('''ff2'''							,		'''output.dense'''     )
			if "ff" in orig_key:
						lowerCamelCase__		:		str		     =orig_key.replace('''ff'''							,		'''output.dense'''     )
			if "mlm_class" in orig_key:
						lowerCamelCase__		:		Tuple		     =orig_key.replace('''mlm.mlm_class'''							,		'''cls.predictions.decoder'''     )
			if "mlm" in orig_key:
						lowerCamelCase__		:		Optional[int]		     =orig_key.replace('''mlm'''							,		'''cls.predictions.transform'''     )
			if "cls" not in orig_key:
						lowerCamelCase__		:		Optional[int]		     ='''yoso.''' + orig_key

			return orig_key






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Any     ):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__		:		Optional[Any]		     =orig_state_dict.pop(__lowerCamelCase     )

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									lowerCamelCase__		:		List[str]		     =val

			lowerCamelCase__		:		Optional[int]		     =orig_state_dict['''cls.predictions.decoder.bias''']
			lowerCamelCase__		:		str		     =torch.arange(__lowerCamelCase     ).expand((1, -1)     ) + 2

			return orig_state_dict






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Tuple     ):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =torch.load(__lowerCamelCase							,		map_location='''cpu'''     )['''model_state_dict''']
			lowerCamelCase__		:		List[Any]		     =YosoConfig.from_json_file(__lowerCamelCase     )
			lowerCamelCase__		:		List[str]		     =YosoForMaskedLM(__lowerCamelCase     )

			lowerCamelCase__		:		Tuple		     =convert_checkpoint_helper(config.max_position_embeddings							,		__lowerCamelCase     )

			print(model.load_state_dict(__lowerCamelCase     )     )
			model.eval()
			model.save_pretrained(__lowerCamelCase     )

			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''     )


if __name__ == "__main__":
		_lowercase			:			int										=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		_lowercase			:			Optional[Any]										=						parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




