




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


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

lowerCamelCase							:						Union[str, Any]					       =  {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

lowerCamelCase							:						Tuple					       =  {
    "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",
    },
}

lowerCamelCase							:						Any					       =  {
    "abeja/gpt-neox-japanese-2.7b": 2_048,
}

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Tuple      ,				lowercase      :			Any	):


      '''simple docstring'''



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

      lowerCamelCase_							=       collections.OrderedDict()
      lowerCamelCase_							=       collections.OrderedDict()
      lowerCamelCase_							=       collections.OrderedDict()
      with open(lowercase      ,				'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(lowercase	):
            lowerCamelCase_							=       b
            lowerCamelCase_							=       idx
            for wd in b:
                  lowerCamelCase_							=       idx

      return vocab, raw_vocab, ids_to_tokens, emoji







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		VOCAB_FILES_NAMES
 UpperCamelCase		  =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		  =		['''input_ids''', '''attention_mask''']

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

       """simple docstring"""






       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)`'    )
       lowerCamelCase_							=       do_clean_text
       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       load_vocab_and_emoji(A_		,				A_    )
       lowerCamelCase_							=       SubWordJapaneseTokenizer(
           vocab=self.vocab		,				ids_to_tokens=self.ids_to_tokens		,				emoji=self.emoji    )

 @property
 def 							a__					(							self   :    List[str]    )  ->      Dict:

       """simple docstring"""






       return len(self.raw_vocab    )

 def 							a__					(							self   :    Tuple    )  ->      Optional[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    Optional[Any]		,				A_   :    str    )  ->      Tuple:

       """simple docstring"""






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

 def 							a__					(							self   :    Optional[int]		,				A_   :    Dict    )  ->      List[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       return self.subword_tokenizer.convert_id_to_token(A_    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    Optional[int]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       ''.join(A_    ).strip()
       return out_string

 def 							a__					(							self   :    Optional[Any]		,				A_   :    "Conversation"    )  ->      List[int]:

       """simple docstring"""






       lowerCamelCase_							=       []
       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:
             lowerCamelCase_							=       input_ids[-self.model_max_length :]
       return input_ids





 def 							a__					(							self   :    List[Any]		,				A_   :    str		,				A_   :    Optional[str] = None    )  ->      Tuple[str]:

       """simple docstring"""






       lowerCamelCase_							=       0
       if os.path.isdir(A_    ):
             lowerCamelCase_							=       os.path.join(
                 A_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
             lowerCamelCase_							=       os.path.join(
                 A_		,				(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(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!'    )
                         lowerCamelCase_							=       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     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Any		,				A_   :    Union[str, Any]		,				A_   :    int		,				A_   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       vocab  # same as swe
       lowerCamelCase_							=       ids_to_tokens  # same as bpe
       lowerCamelCase_							=       emoji
       lowerCamelCase_							=       np.max([len(A_    ) 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   :    str    )  ->      Optional[int]:

       """simple docstring"""






       return len(self.ids_to_tokens    )

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    Dict    )  ->      Optional[Any]:

       """simple docstring"""






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

 def 							a__					(							self   :    int		,				A_   :    Optional[Any]		,				A_   :    Tuple=False    )  ->      Dict:

       """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(A_		,				A_    )
       if clean:
             lowerCamelCase_							=       self.clean_text(A_    )

       def check_simbol(A_   :    Union[str, Any]    ):
             lowerCamelCase_							=       x.encode()
             if len(A_    ) == 1 and len(A_    ) == 2:
                   lowerCamelCase_							=       (int(e[0]    ) << 8) + int(e[1]    )
                   if (
                       (c >= 0XC2_A1 and c <= 0XC2_BF)
                       or (c >= 0XC7_80 and c <= 0XC7_83)
                       or (c >= 0XCA_B9 and c <= 0XCB_BF)
                       or (c >= 0XCC_80 and c <= 0XCD_A2)
                   ):
                         return True
             return False

       def checkuae(A_   :    Tuple    ):
             lowerCamelCase_							=       x.encode()
             if len(A_    ) == 1 and len(A_    ) == 3:
                   lowerCamelCase_							=       (int(e[0]    ) << 16) + (int(e[1]    ) << 8) + int(e[2]    )
                   if c >= 0XE2_80_80 and c <= 0XE2_B0_7F:
                         return True
             return False

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





 def 							a__					(							self   :    List[Any]		,				A_   :    Tuple		,				A_   :    List[str]="\n"    )  ->      List[str]:

       """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(A_    ) > 0:
                   words.append(bytearray(A_    ).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(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'    )    )
       lowerCamelCase_							=       ''.join(A_    )
       return text







import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE			: str				   =					"scheduler_config.json"






class _lowerCamelCase(		_a  ):
       lowercase_					:      Any       					=     1
       lowercase_					:      Dict       					=     2
       lowercase_					:      Union[str, Any]       					=     3
       lowercase_					:      Tuple       					=     4
       lowercase_					:      Optional[Any]       					=     5






@dataclass
class _lowerCamelCase(		_a  ):
       lowercase_					:      jnp.ndarray






class _lowerCamelCase:
       lowercase_					:      Union[str, Any]       					=     SCHEDULER_CONFIG_NAME
       lowercase_					:      str       					=     ["""dtype"""]
       lowercase_					:      Dict       					=     []
       lowercase_					:      int       					=     True
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase=False, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase, subfolder=lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase, )
           _lowercase						,							_lowercase			:				Tuple 						=						cls.from_config(lowerCamelCase, return_unused_kwargs=lowerCamelCase, **lowerCamelCase)

           if hasattr(lowerCamelCase, 'create_state') and getattr(lowerCamelCase, 'has_state', lowerCamelCase):
               _lowercase			:				List[Any] 						=						scheduler.create_state()

           if return_unused_kwargs:
               return scheduler, state, unused_kwargs

           return scheduler, state
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = False, **lowerCamelCase)			->  Any:




           """simple docstring"""

           self.save_config(save_directory=lowerCamelCase, push_to_hub=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self._get_compatibles()




       @classmethod
       def      UpperCamelCase	(   cls)			->  Any:




           """simple docstring"""

           _lowercase			:				Any 						=						list(set([cls.__name__] + cls._compatibles))
           _lowercase			:				Dict 						=						importlib.import_module(__name__.split('.')[0])
           _lowercase			:				Any 						=						[
               getattr(lowerCamelCase, lowerCamelCase) for c in compatible_classes_str if hasattr(lowerCamelCase, lowerCamelCase)
           ]
           return compatible_classes



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		jnp.ndarray:
    assert len(lowerCamelCase_					) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_					) - x.ndim)					)				,					lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_=0.9_99				,					lowerCamelCase_=jnp.floataa					)       ->		jnp.ndarray:

    def alpha_bar(lowerCamelCase_					):
        return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

    _lowercase			:				List[Any] 						=						[]
    for i in range(lowerCamelCase_					):
        _lowercase			:				Any 						=						i / num_diffusion_timesteps
        _lowercase			:				Any 						=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(lowerCamelCase_					) / alpha_bar(lowerCamelCase_					)				,					lowerCamelCase_					)					)
    return jnp.array(lowerCamelCase_				,					dtype=lowerCamelCase_					)






@flax.struct.dataclass
class _lowerCamelCase:
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       lowercase_					:      jnp.ndarray
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase)			->  str:




           """simple docstring"""

           _lowercase			:				int 						=						scheduler.config

           if config.trained_betas is not None:
               _lowercase			:				str 						=						jnp.asarray(config.trained_betas, dtype=scheduler.dtype)
           elif config.beta_schedule == "linear":
               _lowercase			:				List[Any] 						=						jnp.linspace(config.beta_start, config.beta_end, config.num_train_timesteps, dtype=scheduler.dtype)
           elif config.beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               _lowercase			:				Dict 						=						(
                   jnp.linspace(
                       config.beta_start**0.5, config.beta_end**0.5, config.num_train_timesteps, dtype=scheduler.dtype)
                   ** 2
               )
           elif config.beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               _lowercase			:				Optional[int] 						=						betas_for_alpha_bar(config.num_train_timesteps, dtype=scheduler.dtype)
           else:
               raise NotImplementedError(
                   F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}''')

           _lowercase			:				List[str] 						=						1.0 - betas

           _lowercase			:				Union[str, Any] 						=						jnp.cumprod(lowerCamelCase, axis=0)

           return cls(
               alphas=lowerCamelCase, betas=lowerCamelCase, alphas_cumprod=lowerCamelCase, )



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		int:
    _lowercase			:				str 						=						state.alphas_cumprod

    _lowercase			:				str 						=						alphas_cumprod[timesteps] ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_alpha_prod.flatten()
    _lowercase			:				Tuple 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    _lowercase			:				Union[str, Any] 						=						(1 - alphas_cumprod[timesteps]) ** 0.5
    _lowercase			:				Optional[Any] 						=						sqrt_one_minus_alpha_prod.flatten()
    _lowercase			:				int 						=						broadcast_to_shape_from_left(lowerCamelCase_				,					original_samples.shape					)

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				Optional[int] 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				str 						=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Tuple:
    _lowercase						,							_lowercase			:				Tuple 						=						get_sqrt_alpha_prod(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity


