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

SCREAMING_SNAKE_CASE							:Dict							 =				{
    '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							:Dict							 =				{
    'abeja/gpt-neox-japanese-2.7b': 2048,
}


def 						UpperCAmelCase			(	a_		,   a_						)		->	Any:







				"""simple docstring"""



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

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

				return vocab, raw_vocab, ids_to_tokens, emoji





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: str							,A					: int							,A					: Union[str, Any]							,A					: List[str]="<|endoftext|>"							,A					: int="<|endoftext|>"							,A					: Any="<|startoftext|>"							,A					: Dict="<|endoftext|>"							,A					: Optional[Any]=False							,**A					: str							,):
					super().__init__(
					    unk_token=A							,pad_token=A							,bos_token=A							,eos_token=A							,do_clean_text=A							,**A							,)
					if not os.path.isfile(A ):
									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(A ):
									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   			= do_clean_text
					__A			,							__A			,							__A			,							__A   			= load_vocab_and_emoji(A							,A )
					__A   			= SubWordJapaneseTokenizer(
					    vocab=self.vocab							,ids_to_tokens=self.ids_to_tokens							,emoji=self.emoji )






	@property
	def 				UpperCamelCase_  (       self					: Any ):
					# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
					return len(self.raw_vocab )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					return dict(self.raw_vocab							,**self.added_tokens_encoder )






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Optional[int] ):
					return self.subword_tokenizer.tokenize(A							,clean=self.do_clean_text )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Optional[Any] ):
					return self.vocab.get(A							,self.vocab.get(self.unk_token ) )






	def 				UpperCamelCase_  (       self					: str							,A					: List[str] ):
					return self.subword_tokenizer.convert_id_to_token(A )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: str ):
					__A   			= "".join(A ).strip()
					return out_string






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: "Conversation" ):
					__A   			= []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(A							,add_special_tokens=A ) + [self.eos_token_id] )

					if len(A ) > self.model_max_length:
									__A   			= input_ids[-self.model_max_length :]
					return input_ids







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					__A   			= 0
					if os.path.isdir(A ):
									__A   			= os.path.join(
									    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
									__A   			= os.path.join(
									    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
					else:
									__A   			= (
									    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
									)
									__A   			= (
									    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
									)
					with open(A							,"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   			= token_index
													writer.write(",".join(A ) + "\n" )
													index += 1
					with open(A							,"w"							,encoding="utf-8" ) as writer:
									json.dump(self.emoji							,A )
					return vocab_file, emoji_file





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






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






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Any ):
					__A   			= self.content_repattera.sub("<URL>"							,A )
					__A   			= self.content_repattera.sub("<EMAIL>"							,A )
					__A   			= self.content_repattera.sub("<TEL>"							,A )
					__A   			= self.content_repattera.sub("<DATE>"							,A )
					__A   			= self.content_repattera.sub("<DATE>"							,A )
					__A   			= self.content_repattera.sub("<PRICE>"							,A )
					__A   			= content.translate(self.content_transa )
					while "<BLOCK><BLOCK>" in content:
									__A   			= content.replace("<BLOCK><BLOCK>"							,"<BLOCK>" )
					return content






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

					def check_simbol(A					: str ):
									__A   			= x.encode()
									if len(A ) == 1 and len(A ) == 2:
													__A   			= (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(A					: str ):
									__A   			= x.encode()
									if len(A ) == 1 and len(A ) == 3:
													__A   			= (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
													if c >= 0xe28080 and c <= 0xe2b07f:
																	return True
									return False

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







	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[str]							,A					: Any="\n" ):
					__A   			= []
					__A   			= []
					__A   			= self.ids_to_tokens[index][0]
					if word[:6] == "<|byte" and word[-2:] == "|>":
									byte_tokens.append(int(word[6:-2] ) )
					else:
									if len(A ) > 0:
													words.append(bytearray(A ).decode("utf-8"							,errors="replace" ) )
													__A   			= []
									if word[:7] == "<|emoji" and word[-2:] == "|>":
													words.append(self.emoji["emoji_inv"][word] )
									elif word == "<SP>":
													words.append(" " )
									elif word == "<BR>":
													words.append(A )
									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(A )
					if len(A ) > 0:
									words.append(bytearray(A ).decode("utf-8"							,errors="replace" ) )
					__A   			= "".join(A )
					return text







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
				from ...onnx.config import PatchingSpec
				from ...tokenization_utils_base import PreTrainedTokenizerBase


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    str       =   {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''longformer'''
	def __init__(    self							,					_A = 5_1_2							,					_A = 2							,					_A = 1							,					_A = 0							,					_A = 2							,					_A = 3_0_5_2_2							,					_A = 7_6_8							,					_A = 1_2							,					_A = 1_2							,					_A = 3_0_7_2							,					_A = "gelu"							,					_A = 0.1							,					_A = 0.1							,					_A = 5_1_2							,					_A = 2							,					_A = 0.02							,					_A = 1E-12							,					_A = False							,					**_A							,					):






				'''simple docstring'''







				super().__init__(pad_token_id=_A							,					**_A		)

				UpperCAmelCase   		=				attention_window
				UpperCAmelCase   		=				sep_token_id
				UpperCAmelCase   		=				bos_token_id
				UpperCAmelCase   		=				eos_token_id
				UpperCAmelCase   		=				vocab_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   		=				onnx_export

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A = "default"							,					_A = None		):






				'''simple docstring'''







				super().__init__(_A							,					_A							,					_A		)
				UpperCAmelCase   		=				True
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				        ('''global_attention_mask''', dynamic_axis),
				    ]		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				super().outputs
				if self.task == "default":
							UpperCAmelCase   		=				{0: '''batch'''}
				return outputs
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return 1E-4
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return max(super().default_onnx_opset							,					1_4		)







	def      _lowercase				(    self							,					_A							,					_A = -1							,					_A = -1							,					_A = False							,					_A = None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				super().generate_dummy_inputs(
				    preprocessor=_A							,					batch_size=_A							,					seq_length=_A							,					is_pair=_A							,					framework=_A		)
				import torch

				# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
				# makes the export fail randomly
				UpperCAmelCase   		=				torch.zeros_like(inputs['''input_ids''']		)
				# make every second token global
				UpperCAmelCase   		=				1

				return inputs



