


import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 	_lowercase				(      A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		GPTNeoXJapaneseTokenizer
				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		False
				SCREAMING_SNAKE_CASE__    :      Tuple      =		{'''do_clean_text''': False, '''add_prefix_space''': False}


				def 		__magic_name__(	self      :Optional[Any]     )		->      Tuple:
										super().setUp()

										__SCREAMING_SNAKE_CASE						:					int		      =		[
										    '''こん''',
										    '''こんに''',
										    '''にちは''',
										    '''ばんは''',
										    '''世界,㔺界''',
										    '''、''',
										    '''。''',
										    '''<BR>''',
										    '''<SP>''',
										    '''<TAB>''',
										    '''<URL>''',
										    '''<EMAIL>''',
										    '''<TEL>''',
										    '''<DATE>''',
										    '''<PRICE>''',
										    '''<BLOCK>''',
										    '''<KIGOU>''',
										    '''<U2000U2BFF>''',
										    '''<|emoji1|>''',
										    '''<unk>''',
										    '''<|startoftext|>''',
										    '''<|endoftext|>''',
										]
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		{'''unk_token''': '''<unk>'''}

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['''vocab_file''']     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['''emoji_file''']     )
										with open(self.vocab_file      ,      '''w'''      ,      encoding='''utf-8'''     ) as vocab_writer:
																vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]     )     )
										with open(self.emoji_file      ,      '''w'''     ) as emoji_writer:
																emoji_writer.write(json.dumps(lowerCAmelCase__     )     )


				def 		__magic_name__(	self      :Tuple      ,      **lowerCAmelCase__      :Tuple     )		->      Optional[Any]:
										kwargs.update(self.special_tokens_map     )
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,      **lowerCAmelCase__     )


				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :Any     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		'''こんにちは、世界。 \nこんばんは、世界。😀'''
										return input_text, output_text


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[int]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.get_input_output_texts(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					str		      =		tokenizer.encode(lowerCAmelCase__      ,      add_special_tokens=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		tokenizer.decode(lowerCAmelCase__      ,      clean_up_tokenization_spaces=lowerCAmelCase__     )
										return text, ids


				def 		__magic_name__(	self      :Dict     )		->      Dict:
										pass  # TODO add if relevant


				def 		__magic_name__(	self      :List[str]     )		->      Any:
										pass  # TODO add if relevant


				def 		__magic_name__(	self      :Dict     )		->      str:
										pass  # TODO add if relevant


				def 		__magic_name__(	self      :Optional[int]     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_tokenizer()

										# Testing tokenization
										__SCREAMING_SNAKE_CASE						:					str		      =		'''こんにちは、世界。　こんばんは、㔺界。'''
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer.tokenize(lowerCAmelCase__     )
										self.assertListEqual(lowerCAmelCase__      ,      lowerCAmelCase__     )

										# Testing conversion to ids without special tokens
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )
										self.assertListEqual(lowerCAmelCase__      ,      lowerCAmelCase__     )

										# Testing conversion to ids with special tokens
										__SCREAMING_SNAKE_CASE						:					str		      =		tokens + [tokenizer.unk_token]
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
										__SCREAMING_SNAKE_CASE						:					str		      =		tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )
										self.assertListEqual(lowerCAmelCase__      ,      lowerCAmelCase__     )


				@slow
				def 		__magic_name__(	self      :Any     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''     )

										__SCREAMING_SNAKE_CASE						:					int		      =		tokenizer.encode('''ありがとう。'''      ,      add_special_tokens=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		tokenizer.encode('''どういたしまして。'''      ,      add_special_tokens=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__      ,      lowerCAmelCase__     )

										assert encoded_sentence == ids_a
										assert encoded_pair == ids_a + ids_a


				def 		__magic_name__(	self      :str     )		->      List[Any]:
										# Intentionally convert some words to accommodate character fluctuations unique to Japanese
										pass



				def 		__magic_name__(	self      :List[str]     )		->      Union[str, Any]:
										# tokenizer has no padding token
										pass




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__lowerCAmelCase     :			Tuple      =logging.get_logger(__name__)  # pylint: disable=invalid-name
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		['''safety_checker''', '''feature_extractor''']


				def __init__(	self      :int      ,      lowerCAmelCase__      :AutoencoderKL      ,      lowerCAmelCase__      :CLIPTextModel      ,      lowerCAmelCase__      :CLIPTokenizer      ,      lowerCAmelCase__      :UNetaDConditionModel      ,      lowerCAmelCase__      :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,      lowerCAmelCase__      :StableDiffusionSafetyChecker      ,      lowerCAmelCase__      :CLIPImageProcessor      ,      lowerCAmelCase__      :bool = True      ,      )		->      List[Any]:
										super().__init__()
										if hasattr(scheduler.config      ,      '''steps_offset'''     ) and scheduler.config.steps_offset != 1:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		(
																    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
																    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
																    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																    ''' file'''
																)
																deprecate('''steps_offset!=1'''      ,      '''1.0.0'''      ,      lowerCAmelCase__      ,      standard_warn=lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		dict(scheduler.config     )
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		1
																__SCREAMING_SNAKE_CASE						:					Dict		      =		FrozenDict(lowerCAmelCase__     )

										self.register_modules(
										    vae=lowerCAmelCase__      ,      text_encoder=lowerCAmelCase__      ,      tokenizer=lowerCAmelCase__      ,      unet=lowerCAmelCase__      ,      scheduler=lowerCAmelCase__      ,      safety_checker=lowerCAmelCase__      ,      feature_extractor=lowerCAmelCase__      ,      )
										self.register_to_config(requires_safety_checker=lowerCAmelCase__     )


				@property
				def 		__magic_name__(	self      :Union[str, Any]     )		->      Dict[str, Any]:
										return {k: getattr(self      ,      lowerCAmelCase__     ) for k in self.config.keys() if not k.startswith('''_'''     )}


				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Optional[Union[str, int]] = "auto"     )		->      str:
										if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Union[str, Any]:
										self.enable_attention_slicing(lowerCAmelCase__     )


				@torch.no_grad()
				def 		__magic_name__(	self      :str      ,      lowerCAmelCase__      :Union[str, List[str]]      ,      lowerCAmelCase__      :Union[torch.FloatTensor, PIL.Image.Image]      ,      lowerCAmelCase__      :Union[torch.FloatTensor, PIL.Image.Image]      ,      lowerCAmelCase__      :float = 0.8      ,      lowerCAmelCase__      :Optional[int] = 50      ,      lowerCAmelCase__      :Optional[float] = 7.5      ,      lowerCAmelCase__      :Optional[Union[str, List[str]]] = None      ,      lowerCAmelCase__      :Optional[int] = 1      ,      lowerCAmelCase__      :Optional[float] = 0.0      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      lowerCAmelCase__      :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      lowerCAmelCase__      :int = 1      ,      )		->      Any:
										# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
										return StableDiffusionInpaintPipelineLegacy(**self.components     )(
										    prompt=lowerCAmelCase__      ,      image=lowerCAmelCase__      ,      mask_image=lowerCAmelCase__      ,      strength=lowerCAmelCase__      ,      num_inference_steps=lowerCAmelCase__      ,      guidance_scale=lowerCAmelCase__      ,      negative_prompt=lowerCAmelCase__      ,      num_images_per_prompt=lowerCAmelCase__      ,      eta=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      output_type=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__      ,      callback=lowerCAmelCase__      ,      )


				@torch.no_grad()
				def 		__magic_name__(	self      :Dict      ,      lowerCAmelCase__      :Union[str, List[str]]      ,      lowerCAmelCase__      :Union[torch.FloatTensor, PIL.Image.Image]      ,      lowerCAmelCase__      :float = 0.8      ,      lowerCAmelCase__      :Optional[int] = 50      ,      lowerCAmelCase__      :Optional[float] = 7.5      ,      lowerCAmelCase__      :Optional[Union[str, List[str]]] = None      ,      lowerCAmelCase__      :Optional[int] = 1      ,      lowerCAmelCase__      :Optional[float] = 0.0      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      lowerCAmelCase__      :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      lowerCAmelCase__      :int = 1      ,      **lowerCAmelCase__      :Optional[Any]      ,      )		->      Dict:
										# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
										return StableDiffusionImgaImgPipeline(**self.components     )(
										    prompt=lowerCAmelCase__      ,      image=lowerCAmelCase__      ,      strength=lowerCAmelCase__      ,      num_inference_steps=lowerCAmelCase__      ,      guidance_scale=lowerCAmelCase__      ,      negative_prompt=lowerCAmelCase__      ,      num_images_per_prompt=lowerCAmelCase__      ,      eta=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      output_type=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__      ,      callback=lowerCAmelCase__      ,      callback_steps=lowerCAmelCase__      ,      )



				@torch.no_grad()
				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :Union[str, List[str]]      ,      lowerCAmelCase__      :int = 512      ,      lowerCAmelCase__      :int = 512      ,      lowerCAmelCase__      :int = 50      ,      lowerCAmelCase__      :float = 7.5      ,      lowerCAmelCase__      :Optional[Union[str, List[str]]] = None      ,      lowerCAmelCase__      :Optional[int] = 1      ,      lowerCAmelCase__      :float = 0.0      ,      lowerCAmelCase__      :Optional[torch.Generator] = None      ,      lowerCAmelCase__      :Optional[torch.FloatTensor] = None      ,      lowerCAmelCase__      :Optional[str] = "pil"      ,      lowerCAmelCase__      :bool = True      ,      lowerCAmelCase__      :Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      lowerCAmelCase__      :int = 1      ,      )		->      Optional[int]:
										# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
										return StableDiffusionPipeline(**self.components     )(
										    prompt=lowerCAmelCase__      ,      height=lowerCAmelCase__      ,      width=lowerCAmelCase__      ,      num_inference_steps=lowerCAmelCase__      ,      guidance_scale=lowerCAmelCase__      ,      negative_prompt=lowerCAmelCase__      ,      num_images_per_prompt=lowerCAmelCase__      ,      eta=lowerCAmelCase__      ,      generator=lowerCAmelCase__      ,      latents=lowerCAmelCase__      ,      output_type=lowerCAmelCase__      ,      return_dict=lowerCAmelCase__      ,      callback=lowerCAmelCase__      ,      callback_steps=lowerCAmelCase__      ,      )
