




"""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


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": 2_0_4_8,
}







def 			__UpperCAmelCase			(      __UpperCamelCase     ,							__UpperCamelCase     ):
			with open(__lowerCamelCase     ,							'''r'''     ,							encoding='''utf-8'''     ) as f:
						__lowercase       :   Dict        =    json.loads(f.read()     )

			__lowercase       :   Optional[int]        =    collections.OrderedDict()
			__lowercase       :   str        =    collections.OrderedDict()
			__lowercase       :   Dict        =    collections.OrderedDict()
			with open(__lowerCamelCase     ,							'''r'''     ,							encoding='''utf-8'''     ) as f:
						__lowercase       :   Optional[int]        =    f.readlines()
			__lowercase       :   Any        =    [[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[Any]        =    idx

			return vocab, raw_vocab, ids_to_tokens, emoji

class    UpperCAmelCase_  (       lowerCAmelCase_						):
		UpperCamelCase						 =VOCAB_FILES_NAMES
		UpperCamelCase						 =PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase						 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase						 =["input_ids", "attention_mask"]
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_="<|endoftext|>"							,		UpperCamelCase_="<|endoftext|>"							,		UpperCamelCase_="<|startoftext|>"							,		UpperCamelCase_="<|endoftext|>"							,		UpperCamelCase_=False							,		**UpperCamelCase_							,		)		->    Any:
					super().__init__(
					    unk_token=UpperCamelCase_							,		pad_token=UpperCamelCase_							,		bos_token=UpperCamelCase_							,		eos_token=UpperCamelCase_							,		do_clean_text=UpperCamelCase_							,		**UpperCamelCase_							,		)
					if not os.path.isfile(UpperCamelCase_    ):
								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(UpperCamelCase_    ):
								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       :   Any        =    load_vocab_and_emoji(UpperCamelCase_							,		UpperCamelCase_    )
					__lowercase       :   Any        =    SubWordJapaneseTokenizer(
					    vocab=self.vocab							,		ids_to_tokens=self.ids_to_tokens							,		emoji=self.emoji    )
		@property
		def      _lowerCamelCase				(		self    )		->    str:
					# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
					return len(self.raw_vocab    )
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					return dict(self.raw_vocab							,		**self.added_tokens_encoder    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Union[str, Any]:
					return self.subword_tokenizer.tokenize(UpperCamelCase_							,		clean=self.do_clean_text    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Tuple:
					return self.vocab.get(UpperCamelCase_							,		self.vocab.get(self.unk_token    )    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    str:
					return self.subword_tokenizer.convert_id_to_token(UpperCamelCase_    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Optional[int]:
					__lowercase       :   Optional[Any]        =    ''''''.join(UpperCamelCase_    ).strip()
					return out_string
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    List[int]:
					__lowercase       :   Tuple        =    []
					for is_user, text in conversation.iter_texts():
								input_ids.extend(self.encode(UpperCamelCase_							,		add_special_tokens=UpperCamelCase_    ) + [self.eos_token_id]    )

					if len(UpperCamelCase_    ) > self.model_max_length:
								__lowercase       :   Any        =    input_ids[-self.model_max_length :]
					return input_ids
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_ = None    )		->    Tuple[str]:
					__lowercase       :   List[Any]        =    0
					if os.path.isdir(UpperCamelCase_    ):
								__lowercase       :   List[str]        =    os.path.join(
								    UpperCamelCase_							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
								__lowercase       :   List[str]        =    os.path.join(
								    UpperCamelCase_							,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''emoji_file''']    )
					else:
								__lowercase       :   List[Any]        =    (
								    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
								)
								__lowercase       :   Optional[Any]        =    (
								    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
								)
					with open(UpperCamelCase_							,		'''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       :   str        =    token_index
											writer.write(''','''.join(UpperCamelCase_    ) + '''\n'''    )
											index += 1
					with open(UpperCamelCase_							,		'''w'''							,		encoding='''utf-8'''    ) as writer:
								json.dump(self.emoji							,		UpperCamelCase_    )
					return vocab_file, emoji_file


class    UpperCAmelCase_  (       lowerCAmelCase_						):
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Tuple:
					__lowercase       :   Union[str, Any]        =    vocab  # same as swe
					__lowercase       :   List[str]        =    ids_to_tokens  # same as bpe
					__lowercase       :   List[str]        =    emoji
					__lowercase       :   Optional[Any]        =    np.max([len(UpperCamelCase_    ) for w in self.vocab.keys()]    )
					__lowercase       :   List[Any]        =    re.compile(R'''(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'''    )
					__lowercase       :   int        =    re.compile(R'''[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'''    )
					__lowercase       :   Union[str, Any]        =    re.compile(R'''[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'''    )
					__lowercase       :   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|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'''    )
					__lowercase       :   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       :   List[str]        =    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       :   Any        =    '''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
					__lowercase       :   Optional[Any]        =    '''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
					__lowercase       :   Dict        =    str.maketrans({k: '''<BLOCK>''' for k in keisen + blocks}    )
		def __len__(		self    )		->    List[str]:
					return len(self.ids_to_tokens    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Optional[Any]:
					__lowercase       :   Any        =    self.content_repattera.sub('''<URL>'''							,		UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    self.content_repattera.sub('''<EMAIL>'''							,		UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    self.content_repattera.sub('''<TEL>'''							,		UpperCamelCase_    )
					__lowercase       :   Optional[int]        =    self.content_repattera.sub('''<DATE>'''							,		UpperCamelCase_    )
					__lowercase       :   str        =    self.content_repattera.sub('''<DATE>'''							,		UpperCamelCase_    )
					__lowercase       :   List[str]        =    self.content_repattera.sub('''<PRICE>'''							,		UpperCamelCase_    )
					__lowercase       :   List[str]        =    content.translate(self.content_transa    )
					while "<BLOCK><BLOCK>" in content:
								__lowercase       :   List[Any]        =    content.replace('''<BLOCK><BLOCK>'''							,		'''<BLOCK>'''    )
					return content
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_=False    )		->    Any:
					__lowercase       :   Any        =    text.replace(''' '''							,		'''<SP>'''    )
					__lowercase       :   Any        =    text.replace('''　'''							,		'''<SP>'''    )
					__lowercase       :   List[str]        =    text.replace('''\r\n'''							,		'''<BR>'''    )
					__lowercase       :   int        =    text.replace('''\n'''							,		'''<BR>'''    )
					__lowercase       :   Tuple        =    text.replace('''\r'''							,		'''<BR>'''    )
					__lowercase       :   List[Any]        =    text.replace('''\t'''							,		'''<TAB>'''    )
					__lowercase       :   int        =    text.replace('''—'''							,		'''ー'''    )
					__lowercase       :   Optional[Any]        =    text.replace('''−'''							,		'''ー'''    )
					for k, v in self.emoji["emoji"].items():
								if k in text:
											__lowercase       :   Any        =    text.replace(UpperCamelCase_							,		UpperCamelCase_    )
					if clean:
								__lowercase       :   Optional[int]        =    self.clean_text(UpperCamelCase_    )

					def check_simbol(UpperCamelCase_    ):
								__lowercase       :   Any        =    x.encode()
								if len(UpperCamelCase_    ) == 1 and len(UpperCamelCase_    ) == 2:
											__lowercase       :   Optional[int]        =    (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_    ):
								__lowercase       :   Optional[Any]        =    x.encode()
								if len(UpperCamelCase_    ) == 1 and len(UpperCamelCase_    ) == 3:
											__lowercase       :   Any        =    (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

					__lowercase       :   Optional[Any]        =    0
					__lowercase       :   Optional[int]        =    []
					while pos < len(UpperCamelCase_    ):
								__lowercase       :   Optional[Any]        =    min(len(UpperCamelCase_    )							,		pos + self.maxlen + 1    ) if text[pos] == '''<''' else pos + 3
								__lowercase       :   Optional[int]        =    []  # (token_id, token, pos)
								for e in range(UpperCamelCase_							,		UpperCamelCase_							,		-1    ):
											__lowercase       :   Dict        =    text[pos:e]
											if wd in self.vocab:
														if wd[0] == "<" and len(UpperCamelCase_    ) > 2:
																	__lowercase       :   str        =    [(self.vocab[wd], wd, e)]
																	break
														else:
																	candidates.append((self.vocab[wd], wd, e)    )
								if len(UpperCamelCase_    ) > 0:
											# the smallest token_id is adopted
											__lowercase       :   Optional[Any]        =    sorted(UpperCamelCase_							,		key=lambda UpperCamelCase_    : x[0]    )[0]
											result.append(UpperCamelCase_    )
											__lowercase       :   int        =    e
								else:
											__lowercase       :   str        =    pos + 1
											__lowercase       :   List[str]        =    text[pos:end]
											if check_simbol(UpperCamelCase_    ):
														result.append('''<KIGOU>'''    )
											elif checkuae(UpperCamelCase_    ):
														result.append('''<U2000U2BFF>'''    )
											else:
														for i in wd.encode('''utf-8'''    ):
																	result.append('''<|byte%d|>''' % i    )
											__lowercase       :   str        =    end
					return result
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_="\n"    )		->    int:
					__lowercase       :   List[str]        =    []
					__lowercase       :   List[Any]        =    []
					__lowercase       :   Dict        =    self.ids_to_tokens[index][0]
					if word[:6] == "<|byte" and word[-2:] == "|>":
								byte_tokens.append(int(word[6:-2]    )    )
					else:
								if len(UpperCamelCase_    ) > 0:
											words.append(bytearray(UpperCamelCase_    ).decode('''utf-8'''							,		errors='''replace'''    )    )
											__lowercase       :   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(UpperCamelCase_    )
								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(UpperCamelCase_    )
					if len(UpperCamelCase_    ) > 0:
								words.append(bytearray(UpperCamelCase_    ).decode('''utf-8'''							,		errors='''replace'''    )    )
					__lowercase       :   Optional[Any]        =    ''''''.join(UpperCamelCase_    )
					return text








"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					BatchEncoding:
								if images is None and text is None:
											raise ValueError('''You have to specify either images or text.'''     )

								# Get only text
								if images is None:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
											return text_encoding

								# add pixel_values
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, Any]			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
											lowerCamelCase__		:		Optional[Any]			=None

								if text_encoding is not None:
											encoding_image_processor.update(lowerCamelCase     )

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




