
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__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

lowerCamelCase__          =		{
    """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__          =		{
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Dict:
			with open(SCREAMING_SNAKE_CASE_       ,							'r'       ,							encoding='utf-8'     ) as f:
						lowerCAmelCase__      :      int         =		json.loads(f.read()     )

			lowerCAmelCase__      :      Optional[Any]         =		collections.OrderedDict()
			lowerCAmelCase__      :      Union[str, Any]         =		collections.OrderedDict()
			lowerCAmelCase__      :      List[Any]         =		collections.OrderedDict()
			with open(SCREAMING_SNAKE_CASE_       ,							'r'       ,							encoding='utf-8'     ) as f:
						lowerCAmelCase__      :      Optional[int]         =		f.readlines()
			lowerCAmelCase__      :      Optional[Any]         =		[[t.rstrip('\n'     )] if (t == ',' or ',' not in t) else t.rstrip('\n'     ).split(','     ) for t in token]
			for idx, b in enumerate(SCREAMING_SNAKE_CASE_     ):
						lowerCAmelCase__      :      str         =		b
						lowerCAmelCase__      :      Dict         =		idx
						for wd in b:
									lowerCAmelCase__      :      str         =		idx

			return vocab, raw_vocab, ids_to_tokens, emoji







class      A__							(     __magic_name__	):
				lowercase			    =  VOCAB_FILES_NAMES
				lowercase			    =  PRETRAINED_VOCAB_FILES_MAP
				lowercase			    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase			    =  ['input_ids', 'attention_mask']






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






							'''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__      :      Optional[Any]         =		do_clean_text
							lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		load_vocab_and_emoji(a    ,     a   )
							lowerCAmelCase__      :      Any         =		SubWordJapaneseTokenizer(
							    vocab=self.vocab    ,     ids_to_tokens=self.ids_to_tokens    ,     emoji=self.emoji   )






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return len(self.raw_vocab   )






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      str   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Tuple   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict   ):






							'''simple docstring'''







							return self.subword_tokenizer.convert_id_to_token(a   )






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		''.join(a   ).strip()
							return out_string






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      "Conversation"   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		[]
							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__      :      Tuple         =		input_ids[-self.model_max_length :]
							return input_ids






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      str    ,     a					:      Optional[str] = None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		0
							if os.path.isdir(a   ):
										lowerCAmelCase__      :      Union[str, Any]         =		os.path.join(
										    a    ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
										lowerCAmelCase__      :      List[str]         =		os.path.join(
										    a    ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']   )
							else:
										lowerCAmelCase__      :      Optional[int]         =		(
										    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
										)
										lowerCAmelCase__      :      Any         =		(
										    (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__      :      Any         =		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__							(     __magic_name__	):






				def __init__(						self					:      str    ,     a					:      Union[str, Any]    ,     a					:      Dict    ,     a					:      Optional[int]   ):






							'''simple docstring'''







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






				def __len__(						self					:      Optional[int]   ):






							'''simple docstring'''







							return len(self.ids_to_tokens   )






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.content_repattera.sub('<URL>'    ,     a   )
							lowerCAmelCase__      :      Any         =		self.content_repattera.sub('<EMAIL>'    ,     a   )
							lowerCAmelCase__      :      Tuple         =		self.content_repattera.sub('<TEL>'    ,     a   )
							lowerCAmelCase__      :      Optional[Any]         =		self.content_repattera.sub('<DATE>'    ,     a   )
							lowerCAmelCase__      :      int         =		self.content_repattera.sub('<DATE>'    ,     a   )
							lowerCAmelCase__      :      int         =		self.content_repattera.sub('<PRICE>'    ,     a   )
							lowerCAmelCase__      :      List[str]         =		content.translate(self.content_transa   )
							while "<BLOCK><BLOCK>" in content:
										lowerCAmelCase__      :      Any         =		content.replace('<BLOCK><BLOCK>'    ,     '<BLOCK>'   )
							return content






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Tuple    ,     a					:      Optional[int]=False   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		text.replace(' '    ,     '<SP>'   )
							lowerCAmelCase__      :      List[str]         =		text.replace('　'    ,     '<SP>'   )
							lowerCAmelCase__      :      Union[str, Any]         =		text.replace('\r\n'    ,     '<BR>'   )
							lowerCAmelCase__      :      Dict         =		text.replace('\n'    ,     '<BR>'   )
							lowerCAmelCase__      :      Optional[Any]         =		text.replace('\r'    ,     '<BR>'   )
							lowerCAmelCase__      :      Optional[int]         =		text.replace('\t'    ,     '<TAB>'   )
							lowerCAmelCase__      :      Union[str, Any]         =		text.replace('—'    ,     'ー'   )
							lowerCAmelCase__      :      str         =		text.replace('−'    ,     'ー'   )
							for k, v in self.emoji["emoji"].items():
										if k in text:
													lowerCAmelCase__      :      Any         =		text.replace(a    ,     a   )
							if clean:
										lowerCAmelCase__      :      Union[str, Any]         =		self.clean_text(a   )

							def check_simbol(a					:      Tuple   ):
										lowerCAmelCase__      :      Tuple         =		x.encode()
										if len(a   ) == 1 and len(a   ) == 2:
													lowerCAmelCase__      :      Union[str, Any]         =		(int(e[0]   ) << 8) + int(e[1]   )
													if (
													    (c >= 0Xc_2a1 and c <= 0Xc_2bf)
													    or (c >= 0Xc_780 and c <= 0Xc_783)
													    or (c >= 0Xc_ab9 and c <= 0Xc_bbf)
													    or (c >= 0Xc_c80 and c <= 0Xc_da2)
													):
																return True
										return False

							def checkuae(a					:      Optional[Any]   ):
										lowerCAmelCase__      :      Tuple         =		x.encode()
										if len(a   ) == 1 and len(a   ) == 3:
													lowerCAmelCase__      :      str         =		(int(e[0]   ) << 16) + (int(e[1]   ) << 8) + int(e[2]   )
													if c >= 0Xe28_080 and c <= 0Xe2b_07f:
																return True
										return False

							lowerCAmelCase__      :      List[Any]         =		0
							lowerCAmelCase__      :      Any         =		[]
							while pos < len(a   ):
										lowerCAmelCase__      :      Union[str, Any]         =		min(len(a   )    ,     pos + self.maxlen + 1   ) if text[pos] == '<' else pos + 3
										lowerCAmelCase__      :      List[str]         =		[]  # (token_id, token, pos)
										for e in range(a    ,     a    ,     -1   ):
													lowerCAmelCase__      :      int         =		text[pos:e]
													if wd in self.vocab:
																if wd[0] == "<" and len(a   ) > 2:
																			lowerCAmelCase__      :      Union[str, Any]         =		[(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__      :      Dict         =		sorted(a    ,     key=lambda a   : x[0]   )[0]
													result.append(a   )
													lowerCAmelCase__      :      str         =		e
										else:
													lowerCAmelCase__      :      Any         =		pos + 1
													lowerCAmelCase__      :      Union[str, Any]         =		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__      :      Tuple         =		end
							return result






				def 							_lowerCamelCase		(						self					:      List[str]    ,     a					:      Dict    ,     a					:      Optional[Any]="\n"   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		[]
							lowerCAmelCase__      :      Optional[Any]         =		[]
							lowerCAmelCase__      :      Optional[int]         =		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__      :      Dict         =		[]
										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__      :      List[Any]         =		''.join(a   )
							return text

import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      A__							:






				@property
				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							return self.get_dummy_input()






				@property
				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							if self.block_type == "down":
										return (4, 32, 16, 16)
							elif self.block_type == "mid":
										return (4, 32, 32, 32)
							elif self.block_type == "up":
										return (4, 32, 64, 64)

							raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''   )






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[Any]=True    ,     a					:      Any=False    ,     a					:      Dict=False    ,     a					:      Union[str, Any]=False    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		4
							lowerCAmelCase__      :      int         =		32
							lowerCAmelCase__      :      Tuple         =		(32, 32)

							lowerCAmelCase__      :      List[Any]         =		torch.manual_seed(0   )
							lowerCAmelCase__      :      List[Any]         =		torch.device(a   )
							lowerCAmelCase__      :      str         =		(batch_size, num_channels) + sizes
							lowerCAmelCase__      :      Tuple         =		randn_tensor(a    ,     generator=a    ,     device=a   )
							lowerCAmelCase__      :      Optional[Any]         =		{'hidden_states': hidden_states}

							if include_temb:
										lowerCAmelCase__      :      int         =		128
										lowerCAmelCase__      :      List[str]         =		randn_tensor((batch_size, temb_channels)    ,     generator=a    ,     device=a   )

							if include_res_hidden_states_tuple:
										lowerCAmelCase__      :      int         =		torch.manual_seed(1   )
										lowerCAmelCase__      :      str         =		(randn_tensor(a    ,     generator=a    ,     device=a   ),)

							if include_encoder_hidden_states:
										lowerCAmelCase__      :      Any         =		floats_tensor((batch_size, 32, 32)   ).to(a   )

							if include_skip_sample:
										lowerCAmelCase__      :      Union[str, Any]         =		randn_tensor(((batch_size, 3) + sizes)    ,     generator=a    ,     device=a   )

							return dummy_input






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		{
							    'in_channels': 32,
							    'out_channels': 32,
							    'temb_channels': 128,
							}
							if self.block_type == "up":
										lowerCAmelCase__      :      Union[str, Any]         =		32

							if self.block_type == "mid":
										init_dict.pop('out_channels'   )

							lowerCAmelCase__      :      Optional[int]         =		self.dummy_input
							return init_dict, inputs_dict






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      int         =		self.block_class(**a   )
							unet_block.to(a   )
							unet_block.eval()

							with torch.no_grad():
										lowerCAmelCase__      :      int         =		unet_block(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      List[str]         =		output[0]

							self.assertEqual(output.shape    ,     self.output_shape   )

							lowerCAmelCase__      :      List[str]         =		output[0, -1, -3:, -3:]
							lowerCAmelCase__      :      Any         =		torch.tensor(a   ).to(a   )
							assert torch_all_close(output_slice.flatten()    ,     a    ,     atol=5E-3   )






				@unittest.skipIf(torch_device == 'mps'    ,     'Training is not supported in mps'   )
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		self.prepare_init_args_and_inputs_for_common()
							lowerCAmelCase__      :      Any         =		self.block_class(**a   )
							model.to(a   )
							model.train()
							lowerCAmelCase__      :      int         =		model(**a   )

							if isinstance(a    ,     a   ):
										lowerCAmelCase__      :      Dict         =		output[0]

							lowerCAmelCase__      :      Optional[int]         =		torch.device(a   )
							lowerCAmelCase__      :      List[Any]         =		randn_tensor(output.shape    ,     device=a   )
							lowerCAmelCase__      :      List[Any]         =		torch.nn.functional.mse_loss(a    ,     a   )
							loss.backward()