



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class  lowercase__  (       SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	StableUnCLIPImgaImgPipeline
       UpperCamelCase		=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS
       UpperCamelCase		=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCamelCase		=	frozenset(
           [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCamelCase		=	frozenset([] )



       def 	lowercase__							(				self     :    int       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				32
             UpperCAmelCase_     					=				embedder_hidden_size

             # image encoding components

             UpperCAmelCase_     					=				CLIPImageProcessor(crop_size=32						,							size=32       )

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				CLIPVisionModelWithProjection(
                 CLIPVisionConfig(
                     hidden_size=_UpperCAmelCase						,							projection_dim=_UpperCAmelCase						,							num_hidden_layers=5						,							num_attention_heads=4						,							image_size=32						,							intermediate_size=37						,							patch_size=1						,							)       )

             # regular denoising components

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase       )
             UpperCAmelCase_     					=				DDPMScheduler(beta_schedule="squaredcos_cap_v2"       )

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				CLIPTextModel(
                 CLIPTextConfig(
                     bos_token_id=0						,							eos_token_id=2						,							hidden_size=_UpperCAmelCase						,							projection_dim=32						,							intermediate_size=37						,							layer_norm_eps=1e-05						,							num_attention_heads=4						,							num_hidden_layers=5						,							pad_token_id=1						,							vocab_size=1000						,							)       )

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				UNetaDConditionModel(
                 sample_size=32						,							in_channels=4						,							out_channels=4						,							down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")						,							up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")						,							block_out_channels=(32, 64)						,							attention_head_dim=(2, 4)						,							class_embed_type="projection"						,							projection_class_embeddings_input_dim=embedder_projection_dim * 2						,							cross_attention_dim=_UpperCAmelCase						,							layers_per_block=1						,							upcast_attention=_UpperCAmelCase						,							use_linear_projection=_UpperCAmelCase						,							)

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				DDIMScheduler(
                 beta_schedule="scaled_linear"						,							beta_start=0.0_0085						,							beta_end=0.012						,							prediction_type="v_prediction"						,							set_alpha_to_one=_UpperCAmelCase						,							steps_offset=1						,							)

             torch.manual_seed(0       )
             UpperCAmelCase_     					=				AutoencoderKL()

             UpperCAmelCase_     					=				{
                 # image encoding components
                 "feature_extractor": feature_extractor,
                 "image_encoder": image_encoder.eval(),
                 # image noising components
                 "image_normalizer": image_normalizer.eval(),
                 "image_noising_scheduler": image_noising_scheduler,
                 # regular denoising components
                 "tokenizer": tokenizer,
                 "text_encoder": text_encoder.eval(),
                 "unet": unet.eval(),
                 "scheduler": scheduler,
                 "vae": vae.eval(),
             }

             return components



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    str						,							_UpperCAmelCase     :    List[Any]=0						,							_UpperCAmelCase     :    int=True       )					->		str:


             '''simple docstring'''




             if str(_UpperCAmelCase       ).startswith("mps"       ):
                   UpperCAmelCase_     					=				torch.manual_seed(_UpperCAmelCase       )
             else:
                   UpperCAmelCase_     					=				torch.Generator(device=_UpperCAmelCase       ).manual_seed(_UpperCAmelCase       )

             UpperCAmelCase_     					=				floats_tensor((1, 3, 32, 32)						,							rng=random.Random(_UpperCAmelCase       )       ).to(_UpperCAmelCase       )

             if pil_image:
                   UpperCAmelCase_     					=				input_image * 0.5 + 0.5
                   UpperCAmelCase_     					=				input_image.clamp(0						,							1       )
                   UpperCAmelCase_     					=				input_image.cpu().permute(0						,							2						,							3						,							1       ).float().numpy()
                   UpperCAmelCase_     					=				DiffusionPipeline.numpy_to_pil(_UpperCAmelCase       )[0]

             return {
                 "prompt": "An anime racoon running a marathon",
                 "image": input_image,
                 "generator": generator,
                 "num_inference_steps": 2,
                 "output_type": "np",
             }



       @skip_mps
       def 	lowercase__							(				self     :    int       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_     					=				self.get_dummy_components()
             UpperCAmelCase_     					=				StableUnCLIPImgaImgPipeline(**_UpperCAmelCase       )
             UpperCAmelCase_     					=				sd_pipe.to(_UpperCAmelCase       )
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             UpperCAmelCase_     					=				self.get_dummy_inputs(_UpperCAmelCase       )
             inputs.update({"image_embeds": None}       )
             UpperCAmelCase_     					=				sd_pipe(**_UpperCAmelCase       ).images
             UpperCAmelCase_     					=				image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             UpperCAmelCase_     					=				np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]       )

             assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3



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


             '''simple docstring'''




             UpperCAmelCase_     					=				torch_device in ["cpu", "mps"]

             self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase       )



       def 	lowercase__							(				self     :    List[Any]       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				torch_device in ["cpu", "mps"]

             self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase       )







       @unittest.skipIf(
           torch_device != "cuda" or not is_xformers_available()						,							reason="XFormers attention is only available with CUDA and `xformers` installed"						,							)
       def 	lowercase__							(				self     :    Tuple       )					->		Union[str, Any]:


             '''simple docstring'''




             self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_UpperCAmelCase       )



@slow
@require_torch_gpu
class  lowercase__  (       unittest.TestCase ):







       '''simple docstring'''


       def 	lowercase__							(				self     :    Union[str, Any]       )					->		Dict:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       def 	lowercase__							(				self     :    Tuple       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"       )

             UpperCAmelCase_     					=				load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"       )

             UpperCAmelCase_     					=				StableUnCLIPImgaImgPipeline.from_pretrained(
                 "fusing/stable-unclip-2-1-l-img2img"						,							torch_dtype=torch.floataa       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             # stable unclip will oom when integration tests are run on a V100,
             # so turn on memory savings
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase_     					=				torch.Generator(device="cpu"       ).manual_seed(0       )
             UpperCAmelCase_     					=				pipe(_UpperCAmelCase						,							"anime turle"						,							generator=_UpperCAmelCase						,							output_type="np"       )

             UpperCAmelCase_     					=				output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(_UpperCAmelCase						,							_UpperCAmelCase       )



       def 	lowercase__							(				self     :    int       )					->		List[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"       )

             UpperCAmelCase_     					=				load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"       )

             UpperCAmelCase_     					=				StableUnCLIPImgaImgPipeline.from_pretrained(
                 "fusing/stable-unclip-2-1-h-img2img"						,							torch_dtype=torch.floataa       )
             pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             # stable unclip will oom when integration tests are run on a V100,
             # so turn on memory savings
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase_     					=				torch.Generator(device="cpu"       ).manual_seed(0       )
             UpperCAmelCase_     					=				pipe(_UpperCAmelCase						,							"anime turle"						,							generator=_UpperCAmelCase						,							output_type="np"       )

             UpperCAmelCase_     					=				output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(_UpperCAmelCase						,							_UpperCAmelCase       )







       def 	lowercase__							(				self     :    List[str]       )					->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"       )

             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase_     					=				StableUnCLIPImgaImgPipeline.from_pretrained(
                 "fusing/stable-unclip-2-1-h-img2img"						,							torch_dtype=torch.floataa       )
             UpperCAmelCase_     					=				pipe.to(_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )
             pipe.enable_attention_slicing()
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase_     					=				pipe(
                 _UpperCAmelCase						,							"anime turtle"						,							num_inference_steps=2						,							output_type="np"						,							)

             UpperCAmelCase_     					=				torch.cuda.max_memory_allocated()
             # make sure that less than 7 GB is allocated
             assert mem_bytes < 7 * 10**9








'''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


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

__SCREAMING_SNAKE_CASE					:				str									=		{
    '''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					:				List[Any]									=		{
    '''abeja/gpt-neox-japanese-2.7b''': 2048,
}







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

							A_			    =		collections.OrderedDict()
							A_			    =		collections.OrderedDict()
							A_			    =		collections.OrderedDict()
							with open(UpperCamelCase_						,  "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(UpperCamelCase_ ):
														A_			    =		b
														A_			    =		idx
														for wd in b:
																					A_			    =		idx

							return vocab, raw_vocab, ids_to_tokens, emoji






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






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






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






						@property
						def 				_UpperCAmelCase       (  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 				_UpperCAmelCase       (  self							:      Optional[int]	):
													return dict(self.raw_vocab ,  **self.added_tokens_encoder	)






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






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      int	):
													return self.vocab.get(lowerCAmelCase ,  self.vocab.get(self.unk_token	)	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      List[str]	):
													return self.subword_tokenizer.convert_id_to_token(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      Optional[Any]	):
													A_			    =		"".join(lowerCAmelCase	).strip()
													return out_string






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      "Conversation"	):
													A_			    =		[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(lowerCAmelCase ,  add_special_tokens=lowerCAmelCase	) + [self.eos_token_id]	)

													if len(lowerCAmelCase	) > self.model_max_length:
																				A_			    =		input_ids[-self.model_max_length :]
													return input_ids






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Optional[str] = None	):
													A_			    =		0
													if os.path.isdir(lowerCAmelCase	):
																				A_			    =		os.path.join(
																				    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
																				A_			    =		os.path.join(
																				    lowerCAmelCase ,  (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(lowerCAmelCase ,  "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(lowerCAmelCase	) + "\n"	)
																											index += 1
													with open(lowerCAmelCase ,  "w" ,  encoding="utf-8"	) as writer:
																				json.dump(self.emoji ,  lowerCAmelCase	)
													return vocab_file, emoji_file






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      List[Any]	):
													A_			    =		vocab  # same as swe
													A_			    =		ids_to_tokens  # same as bpe
													A_			    =		emoji
													A_			    =		np.max([len(lowerCAmelCase	) 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							:      List[str] ,  lowerCAmelCase							:      int	):
													A_			    =		self.content_repattera.sub("<URL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<EMAIL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<TEL>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<DATE>" ,  lowerCAmelCase	)
													A_			    =		self.content_repattera.sub("<PRICE>" ,  lowerCAmelCase	)
													A_			    =		content.translate(self.content_transa	)
													while "<BLOCK><BLOCK>" in content:
																				A_			    =		content.replace("<BLOCK><BLOCK>" ,  "<BLOCK>"	)
													return content






						def 				_UpperCAmelCase       (  self							:      Any ,  lowerCAmelCase							:      Optional[Any] ,  lowerCAmelCase							:      Tuple=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(lowerCAmelCase ,  lowerCAmelCase	)
													if clean:
																				A_			    =		self.clean_text(lowerCAmelCase	)

													def check_simbol(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(lowerCAmelCase	) == 1 and len(lowerCAmelCase	) == 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(lowerCAmelCase							:      Tuple	):
																				A_			    =		x.encode()
																				if len(lowerCAmelCase	) == 1 and len(lowerCAmelCase	) == 3:
																											A_			    =		(int(e[0]	) << 16) + (int(e[1]	) << 8) + int(e[2]	)
																											if c >= 0xE2_8080 and c <= 0xE2_B07F:
																																		return True
																				return False

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






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      Union[str, 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(lowerCAmelCase	) > 0:
																											words.append(bytearray(lowerCAmelCase	).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(lowerCAmelCase	)
																				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(lowerCAmelCase	)
													if len(lowerCAmelCase	) > 0:
																				words.append(bytearray(lowerCAmelCase	).decode("utf-8" ,  errors="replace"	)	)
													A_			    =		"".join(lowerCAmelCase	)
													return text





