
'''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


UpperCAmelCase__ :			Dict											=		logging.get_logger(__name__)

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

UpperCAmelCase__ :			Any											=		{
    "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",
    },
}

UpperCAmelCase__ :			int											=		{
    "abeja/gpt-neox-japanese-2.7b": 20_48,
}







def 	A  (   UpperCamelCase_       :  Tuple    ,						UpperCamelCase_       :  List[Any]   )       ->							List[Any]:







			'''simple docstring'''



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

			lowerCAmelCase__										=				collections.OrderedDict()
			lowerCAmelCase__										=				collections.OrderedDict()
			lowerCAmelCase__										=				collections.OrderedDict()
			with open(UpperCamelCase_    ,						"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(UpperCamelCase_   ):
						lowerCAmelCase__										=				b
						lowerCAmelCase__										=				idx
						for wd in b:
									lowerCAmelCase__										=				idx

			return vocab, raw_vocab, ids_to_tokens, emoji






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :List[str]   =  VOCAB_FILES_NAMES
	snake_case__   :Dict   =  PRETRAINED_VOCAB_FILES_MAP
	snake_case__   :Optional[Any]   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__   :int   =  ['input_ids', 'attention_mask']





	def __init__(       self      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							int   ,				__magic_name__      :							Dict="<|endoftext|>"   ,				__magic_name__      :							List[str]="<|endoftext|>"   ,				__magic_name__      :							Tuple="<|startoftext|>"   ,				__magic_name__      :							Optional[int]="<|endoftext|>"   ,				__magic_name__      :							List[Any]=False   ,				**__magic_name__      :							Dict   ,				):


				"""simple docstring"""

				super().__init__(
				    unk_token=__magic_name__   ,				pad_token=__magic_name__   ,				bos_token=__magic_name__   ,				eos_token=__magic_name__   ,				do_clean_text=__magic_name__   ,				**__magic_name__   ,				)
				if not os.path.isfile(__magic_name__				):
							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(__magic_name__				):
							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__						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				load_vocab_and_emoji(__magic_name__   ,				__magic_name__				)
				lowerCAmelCase__										=				SubWordJapaneseTokenizer(
				    vocab=self.vocab   ,				ids_to_tokens=self.ids_to_tokens   ,				emoji=self.emoji				)





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				return len(self.raw_vocab				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return dict(self.raw_vocab   ,				**self.added_tokens_encoder				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							int				):


				"""simple docstring"""

				return self.subword_tokenizer.tokenize(__magic_name__   ,				clean=self.do_clean_text				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple   ,				__magic_name__      :							Any				):


				"""simple docstring"""

				return self.vocab.get(__magic_name__   ,				self.vocab.get(self.unk_token				)				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				return self.subword_tokenizer.convert_id_to_token(__magic_name__				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							str				):


				"""simple docstring"""

				lowerCAmelCase__										=				"".join(__magic_name__				).strip()
				return out_string





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							"Conversation"				):


				"""simple docstring"""

				lowerCAmelCase__										=				[]
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(__magic_name__   ,				add_special_tokens=__magic_name__				) + [self.eos_token_id]				)

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							str   ,				__magic_name__      :							Optional[str] = None				):


				"""simple docstring"""

				lowerCAmelCase__										=				0
				if os.path.isdir(__magic_name__				):
							lowerCAmelCase__										=				os.path.join(
							    __magic_name__   ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
							lowerCAmelCase__										=				os.path.join(
							    __magic_name__   ,				(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(__magic_name__   ,				"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(__magic_name__				) + "\n"				)
										index += 1
				with open(__magic_name__   ,				"w"   ,				encoding="utf-8"				) as writer:
							json.dump(self.emoji   ,				__magic_name__				)
				return vocab_file, emoji_file






class  A  (						SCREAMING_SNAKE_CASE__     ):





	def __init__(       self      :							List[str]   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				vocab  # same as swe
				lowerCAmelCase__										=				ids_to_tokens  # same as bpe
				lowerCAmelCase__										=				emoji
				lowerCAmelCase__										=				np.max([len(__magic_name__				) 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      :							Dict				):


				"""simple docstring"""

				return len(self.ids_to_tokens				)





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]   ,				__magic_name__      :							Union[str, Any]				):


				"""simple docstring"""

				lowerCAmelCase__										=				self.content_repattera.sub("<URL>"   ,				__magic_name__				)
				lowerCAmelCase__										=				self.content_repattera.sub("<EMAIL>"   ,				__magic_name__				)
				lowerCAmelCase__										=				self.content_repattera.sub("<TEL>"   ,				__magic_name__				)
				lowerCAmelCase__										=				self.content_repattera.sub("<DATE>"   ,				__magic_name__				)
				lowerCAmelCase__										=				self.content_repattera.sub("<DATE>"   ,				__magic_name__				)
				lowerCAmelCase__										=				self.content_repattera.sub("<PRICE>"   ,				__magic_name__				)
				lowerCAmelCase__										=				content.translate(self.content_transa				)
				while "<BLOCK><BLOCK>" in content:
							lowerCAmelCase__										=				content.replace("<BLOCK><BLOCK>"   ,				"<BLOCK>"				)
				return content





	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							Dict   ,				__magic_name__      :							Optional[int]=False				):


				"""simple docstring"""

				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(__magic_name__   ,				__magic_name__				)
				if clean:
							lowerCAmelCase__										=				self.clean_text(__magic_name__				)

				def check_simbol(__magic_name__      :							int				):
							lowerCAmelCase__										=				x.encode()
							if len(__magic_name__				) == 1 and len(__magic_name__				) == 2:
										lowerCAmelCase__										=				(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(__magic_name__      :							int				):
							lowerCAmelCase__										=				x.encode()
							if len(__magic_name__				) == 1 and len(__magic_name__				) == 3:
										lowerCAmelCase__										=				(int(e[0]				) << 16) + (int(e[1]				) << 8) + int(e[2]				)
										if c >= 0XE2_8080 and c <= 0XE2_B07F:
													return True
							return False

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







	def 				__SCREAMING_SNAKE_CASE	(       self      :							int   ,				__magic_name__      :							str   ,				__magic_name__      :							str="\n"				):


				"""simple docstring"""

				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(__magic_name__				) > 0:
										words.append(bytearray(__magic_name__				).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(__magic_name__				)
							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(__magic_name__				)
				if len(__magic_name__				) > 0:
							words.append(bytearray(__magic_name__				).decode("utf-8"   ,				errors="replace"				)				)
				lowerCAmelCase__										=				"".join(__magic_name__				)
				return text












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'roformer'

 def __init__(  self,		A_=5_0000,		A_=None,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=1536,		A_=2,		A_=0.02,		A_=1E-12,		A_=0,		A_=False,		A_=True,		**A_,		)	->       Dict:
    super().__init__(pad_token_id=A_,		**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size if embedding_size is None else embedding_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=rotary_value
    UpperCAmelCase__						=use_cache

class     snake_case_		(   a							):






 '''simple docstring'''





 @property
 def  __UpperCAmelCase						(  self		)	->       Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
       UpperCAmelCase__						={0: "batch", 1: "choice", 2: "sequence"}
    else:
       UpperCAmelCase__						={0: "batch", 1: "sequence"}
    UpperCAmelCase__						={0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("input_ids", dynamic_axis),
            ("attention_mask", dynamic_axis),
            ("token_type_ids", dynamic_axis),
        ]		)



