


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


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

SCREAMING_SNAKE_CASE			: List[Any]				   =					{"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

SCREAMING_SNAKE_CASE			: 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",
    },
}

SCREAMING_SNAKE_CASE			: Any				   =					{
    "abeja/gpt-neox-japanese-2.7b": 2048,
}



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Dict:
    with open(lowerCamelCase_				,					'r'				,					encoding='utf-8'					) as f:
        _lowercase			:				int 						=						json.loads(f.read()					)

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

    return vocab, raw_vocab, ids_to_tokens, emoji






class _lowerCamelCase(		_a  ):
       lowercase_					:      str       					=     VOCAB_FILES_NAMES
       lowercase_					:      List[str]       					=     PRETRAINED_VOCAB_FILES_MAP
       lowercase_					:      Optional[Any]       					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_					:      Union[str, Any]       					=     ["""input_ids""", """attention_mask"""]
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase="<|endoftext|>", lowerCamelCase="<|endoftext|>", lowerCamelCase="<|startoftext|>", lowerCamelCase="<|endoftext|>", lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           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)`')
           _lowercase			:				Tuple 						=						do_clean_text
           _lowercase						,							_lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						load_vocab_and_emoji(lowerCamelCase, lowerCamelCase)
           _lowercase			:				str 						=						SubWordJapaneseTokenizer(
               vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji)
       @property
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           return len(self.raw_vocab)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           return dict(self.raw_vocab, **self.added_tokens_encoder)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           return self.subword_tokenizer.tokenize(lowerCamelCase, clean=self.do_clean_text)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

           return self.vocab.get(lowerCamelCase, self.vocab.get(self.unk_token))
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				str 						=						''.join(lowerCamelCase).strip()
           return out_string
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[int]:




           """simple docstring"""

           _lowercase			:				str 						=						[]
           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:
               _lowercase			:				List[str] 						=						input_ids[-self.model_max_length :]
           return input_ids




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple[str]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						0
           if os.path.isdir(lowerCamelCase):
               _lowercase			:				int 						=						os.path.join(
                   lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
               _lowercase			:				Union[str, Any] 						=						os.path.join(
                   lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file'])
           else:
               _lowercase			:				Dict 						=						(
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
               )
               _lowercase			:				str 						=						(
                   (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!')
                       _lowercase			:				Union[str, 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 _lowerCamelCase(		_a  ):
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

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




           """simple docstring"""

           return len(self.ids_to_tokens)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Tuple:




           """simple docstring"""

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




           """simple docstring"""

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

           def check_simbol(lowerCamelCase):
               _lowercase			:				Optional[int] 						=						x.encode()
               if len(lowerCamelCase) == 1 and len(lowerCamelCase) == 2:
                   _lowercase			:				List[Any] 						=						(int(e[0]) << 8) + int(e[1])
                   if (
                       (c >= 0xC2A1 and c <= 0xC2BF)
                       or (c >= 0xC780 and c <= 0xC783)
                       or (c >= 0xCAB9 and c <= 0xCBBF)
                       or (c >= 0xCC80 and c <= 0xCDA2)
                   ):
                       return True
               return False

           def checkuae(lowerCamelCase):
               _lowercase			:				int 						=						x.encode()
               if len(lowerCamelCase) == 1 and len(lowerCamelCase) == 3:
                   _lowercase			:				Optional[Any] 						=						(int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
                   if c >= 0xE28080 and c <= 0xE2B07F:
                       return True
               return False

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase="\n")			->  List[str]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						[]
           _lowercase			:				str 						=						[]
           _lowercase			:				str 						=						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'))
                   _lowercase			:				Optional[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'))
           _lowercase			:				Union[str, Any] 						=						''.join(lowerCamelCase)
           return text









"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


