





'''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__							:							int =logging.get_logger(__name__)

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

a__							:							int ={
    '''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__							:							Dict ={
    '''abeja/gpt-neox-japanese-2.7b''': 2_048,
}
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






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

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

       return vocab, raw_vocab, ids_to_tokens, emoji

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					List[str]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					str					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Dict					=["input_ids", "attention_mask"]






 def __init__(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Union[str, Any]							,	__A       :							List[str]="<|endoftext|>"							,	__A       :							int="<|endoftext|>"							,	__A       :							Optional[Any]="<|startoftext|>"							,	__A       :							Dict="<|endoftext|>"							,	__A       :							Dict=False							,	**__A       :							Any							,	):
        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)`'	)
        __UpperCamelCase       =  do_clean_text
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  load_vocab_and_emoji(__A							,	__A	)
        __UpperCamelCase       =  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       :							Any	):
        return dict(self.raw_vocab							,	**self.added_tokens_encoder	)






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							str	):
        return self.subword_tokenizer.tokenize(__A							,	clean=self.do_clean_text	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int	):
        return self.vocab.get(__A							,	self.vocab.get(self.unk_token	)	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[Any]	):
        return self.subword_tokenizer.convert_id_to_token(__A	)






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[str]	):
        __UpperCamelCase       =  ''.join(__A	).strip()
        return out_string






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							"Conversation"	):
        __UpperCamelCase       =  []
        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:
               __UpperCamelCase       =  input_ids[-self.model_max_length :]
        return input_ids






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							str							,	__A       :							Optional[str] = None	):
        __UpperCamelCase       =  0
        if os.path.isdir(__A	):
               __UpperCamelCase       =  os.path.join(
                   __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
               __UpperCamelCase       =  os.path.join(
                   __A							,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']	)
        else:
               __UpperCamelCase       =  (
                   (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
               )
               __UpperCamelCase       =  (
                   (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!'	)
                             __UpperCamelCase       =  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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



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






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






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[str]	):
        __UpperCamelCase       =  self.content_repattera.sub('<URL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<EMAIL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<TEL>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<DATE>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<DATE>'							,	__A	)
        __UpperCamelCase       =  self.content_repattera.sub('<PRICE>'							,	__A	)
        __UpperCamelCase       =  content.translate(self.content_transa	)
        while "<BLOCK><BLOCK>" in content:
               __UpperCamelCase       =  content.replace('<BLOCK><BLOCK>'							,	'<BLOCK>'	)
        return content






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

        def check_simbol(__A       :							str	):
               __UpperCamelCase       =  x.encode()
               if len(__A	) == 1 and len(__A	) == 2:
                      __UpperCamelCase       =  (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       :							Tuple	):
               __UpperCamelCase       =  x.encode()
               if len(__A	) == 1 and len(__A	) == 3:
                      __UpperCamelCase       =  (int(e[0]	) << 1_6) + (int(e[1]	) << 8) + int(e[2]	)
                      if c >= 0xe2_8080 and c <= 0xe2_b07f:
                             return True
               return False

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






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Any							,	__A       :							List[Any]="\n"	):
        __UpperCamelCase       =  []
        __UpperCamelCase       =  []
        __UpperCamelCase       =  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'	)	)
                      __UpperCamelCase       =  []
               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'	)	)
        __UpperCamelCase       =  ''.join(__A	)
        return text

import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		R'''\w+[.]\d+'''
    __UpperCamelCase     :List[str]			  =		re.findall(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for pat in pats:
        __UpperCamelCase     :int			  =		key.replace(SCREAMING_SNAKE_CASE  ,			'''_'''.join(pat.split('''.'''	)	)	)
    return key



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key	)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        __UpperCamelCase     :Any			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        __UpperCamelCase     :List[Any]			  =		pt_tensor.transpose(2  ,			3  ,			1  ,			0	)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        __UpperCamelCase     :Any			  =		pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=42	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    __UpperCamelCase     :str			  =		flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE	)	)

    __UpperCamelCase     :int			  =		flatten_dict(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        __UpperCamelCase     :List[Any]			  =		rename_key(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :List[Any]			  =		tuple(renamed_pt_key.split('''.'''	)	)

        # Correctly rename weight parameters
        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
        __UpperCamelCase     :str			  =		jnp.asarray(SCREAMING_SNAKE_CASE	)

    return unflatten_dict(SCREAMING_SNAKE_CASE	)





