



"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_nllb import NllbTokenizer
else:
				snake_case_    					=	None


snake_case_    					=	logging.get_logger(__name__)


snake_case_    					=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case_    					=	{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_    					=	{
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
snake_case_    					=	["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							VOCAB_FILES_NAMES
						__UpperCamelCase		    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase		    =							PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase		    =							["""input_ids""", """attention_mask"""]
						__UpperCamelCase		    =							NllbTokenizer

						__UpperCamelCase		    =							[]
						__UpperCamelCase		    =							[]






						def __init__( self			:str       ,  lowercase_			:Dict=None       ,  lowercase_			:List[Any]=None       ,  lowercase_			:Optional[int]="<s>"       ,  lowercase_			:List[str]="</s>"       ,  lowercase_			:List[Any]="</s>"       ,  lowercase_			:Dict="<s>"       ,  lowercase_			:List[str]="<unk>"       ,  lowercase_			:Tuple="<pad>"       ,  lowercase_			:List[Any]="<mask>"       ,  lowercase_			:Optional[Any]=None       ,  lowercase_			:Any=None       ,  lowercase_			:List[str]=None       ,  lowercase_			:str=False       ,  **lowercase_			:str       ,  )    ->      Any:
												# Mask token behave like a normal word, i.e. include the space before it
												UpperCAmelCase					=			AddedToken(lowercase_       ,  lstrip=lowercase_       ,  rstrip=lowercase_ ) if isinstance(lowercase_       ,  lowercase_ ) else mask_token
												UpperCAmelCase					=			legacy_behaviour
												super().__init__(
												    vocab_file=lowercase_       ,  tokenizer_file=lowercase_       ,  bos_token=lowercase_       ,  eos_token=lowercase_       ,  sep_token=lowercase_       ,  cls_token=lowercase_       ,  unk_token=lowercase_       ,  pad_token=lowercase_       ,  mask_token=lowercase_       ,  src_lang=lowercase_       ,  tgt_lang=lowercase_       ,  additional_special_tokens=lowercase_       ,  legacy_behaviour=lowercase_       ,  **lowercase_       ,  )

												UpperCAmelCase					=			vocab_file
												UpperCAmelCase					=			False if not self.vocab_file else True

												UpperCAmelCase					=			FAIRSEQ_LANGUAGE_CODES.copy()

												if additional_special_tokens is not None:
																		# Only add those special tokens if they are not already there.
																		_additional_special_tokens.extend(
																		    [t for t in additional_special_tokens if t not in _additional_special_tokens] )

												self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
												UpperCAmelCase					=			{
												    lang_code: self.convert_tokens_to_ids(lowercase_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												UpperCAmelCase					=			src_lang if src_lang is not None else 'eng_Latn'
												UpperCAmelCase					=			self.convert_tokens_to_ids(self._src_lang )
												UpperCAmelCase					=			tgt_lang
												self.set_src_lang_special_tokens(self._src_lang )






						@property
						def      UpperCAmelCase__					( self			:int )    ->      str:
												return self._src_lang






						@src_lang.setter
						def      UpperCAmelCase__					( self			:int       ,  lowercase_			:str )    ->      None:
												UpperCAmelCase					=			new_src_lang
												self.set_src_lang_special_tokens(self._src_lang )






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:List[int]       ,  lowercase_			:Optional[List[int]] = None )    ->      List[int]:
												if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:List[int]       ,  lowercase_			:Optional[List[int]] = None )    ->      List[int]:
												UpperCAmelCase					=			[self.sep_token_id]
												UpperCAmelCase					=			[self.cls_token_id]

												if token_ids_a is None:
																		return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:List[str]       ,  lowercase_			:str       ,  lowercase_			:Optional[str]       ,  lowercase_			:Optional[str]       ,  **lowercase_			:Any )    ->      Dict:
												if src_lang is None or tgt_lang is None:
																		raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
												UpperCAmelCase					=			src_lang
												UpperCAmelCase					=			self(lowercase_       ,  add_special_tokens=lowercase_       ,  return_tensors=lowercase_       ,  **lowercase_ )
												UpperCAmelCase					=			self.convert_tokens_to_ids(lowercase_ )
												UpperCAmelCase					=			tgt_lang_id
												return inputs






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:List[str]       ,  lowercase_			:str = "eng_Latn"       ,  lowercase_			:Optional[List[str]] = None       ,  lowercase_			:str = "fra_Latn"       ,  **lowercase_			:Tuple       ,  )    ->      BatchEncoding:
												UpperCAmelCase					=			src_lang
												UpperCAmelCase					=			tgt_lang
												return super().prepare_seqaseq_batch(lowercase_       ,  lowercase_       ,  **lowercase_ )






						def      UpperCAmelCase__					( self			:List[str] )    ->      Optional[int]:
												return self.set_src_lang_special_tokens(self.src_lang )






						def      UpperCAmelCase__					( self			:List[str] )    ->      List[str]:
												return self.set_tgt_lang_special_tokens(self.tgt_lang )






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:List[str] )    ->      None:
												UpperCAmelCase					=			self.convert_tokens_to_ids(lowercase_ )

												if self.legacy_behaviour:
																		UpperCAmelCase					=			[]
																		UpperCAmelCase					=			[self.eos_token_id, self.cur_lang_code]
												else:
																		UpperCAmelCase					=			[self.cur_lang_code]
																		UpperCAmelCase					=			[self.eos_token_id]

												UpperCAmelCase					=			self.convert_ids_to_tokens(self.prefix_tokens )
												UpperCAmelCase					=			self.convert_ids_to_tokens(self.suffix_tokens )

												UpperCAmelCase					=			processors.TemplateProcessing(
												    single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,  self.prefix_tokens + self.suffix_tokens ) )       ,  )






						def      UpperCAmelCase__					( self			:Any       ,  lowercase_			:str )    ->      None:
												UpperCAmelCase					=			self.convert_tokens_to_ids(lowercase_ )
												if self.legacy_behaviour:
																		UpperCAmelCase					=			[]
																		UpperCAmelCase					=			[self.eos_token_id, self.cur_lang_code]
												else:
																		UpperCAmelCase					=			[self.cur_lang_code]
																		UpperCAmelCase					=			[self.eos_token_id]

												UpperCAmelCase					=			self.convert_ids_to_tokens(self.prefix_tokens )
												UpperCAmelCase					=			self.convert_ids_to_tokens(self.suffix_tokens )

												UpperCAmelCase					=			processors.TemplateProcessing(
												    single=prefix_tokens_str + ['$A'] + suffix_tokens_str       ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str       ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,  self.prefix_tokens + self.suffix_tokens ) )       ,  )






						def      UpperCAmelCase__					( self			:List[str]       ,  lowercase_			:str       ,  lowercase_			:Optional[str] = None )    ->      Tuple[str]:
												if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																		    'tokenizer.' )

												if not os.path.isdir(lowercase_ ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" )
																		return
												UpperCAmelCase					=			os.path.join(
												    lowercase_       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

												if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
																		copyfile(self.vocab_file       ,  lowercase_ )

												return (out_vocab_file,)










"""simple docstring"""


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


snake_case_    					=	logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:CLIPSegForImageSegmentation       ,  lowercase_			:CLIPSegProcessor       ,  lowercase_			:AutoencoderKL       ,  lowercase_			:CLIPTextModel       ,  lowercase_			:CLIPTokenizer       ,  lowercase_			:UNetaDConditionModel       ,  lowercase_			:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,  lowercase_			:StableDiffusionSafetyChecker       ,  lowercase_			:CLIPImageProcessor       ,  )    ->      List[str]:
												super().__init__()

												if hasattr(scheduler.config       ,  'steps_offset' ) and scheduler.config.steps_offset != 1:
																		UpperCAmelCase					=			(
																		    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'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			1
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if hasattr(scheduler.config       ,  'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
																		UpperCAmelCase					=			(
																		    f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
																		    ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
																		    ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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('skip_prk_steps not set'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			True
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if safety_checker is None:
																		logger.warning(
																		    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
																		    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
																		    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
																		    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
																		    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )

												self.register_modules(
												    segmentation_model=lowercase_       ,  segmentation_processor=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  unet=lowercase_       ,  scheduler=lowercase_       ,  safety_checker=lowercase_       ,  feature_extractor=lowercase_       ,  )






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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												self.enable_attention_slicing(lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      int:
												if is_accelerate_available():
																		from accelerate import cpu_offload
												else:
																		raise ImportError('Please install accelerate via `pip install accelerate`' )

												UpperCAmelCase					=			torch.device('cuda' )

												for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																		if cpu_offloaded_model is not None:
																								cpu_offload(lowercase_       ,  lowercase_ )






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      List[str]:
												if self.device != torch.device('meta' ) or not hasattr(self.unet       ,  '_hf_hook' ):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(lowercase_       ,  '_hf_hook' )
																		    and hasattr(module._hf_hook       ,  'execution_device' )
																		    and module._hf_hook.execution_device is not None
																		):
																								return torch.device(module._hf_hook.execution_device )
												return self.device






						@torch.no_grad()
						def __call__( self			:Optional[Any]       ,  lowercase_			:Union[str, List[str]]       ,  lowercase_			:Union[torch.FloatTensor, PIL.Image.Image]       ,  lowercase_			:str       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 50       ,  lowercase_			:float = 7.5       ,  lowercase_			:Optional[Union[str, List[str]]] = None       ,  lowercase_			:Optional[int] = 1       ,  lowercase_			:float = 0.0       ,  lowercase_			:Optional[torch.Generator] = None       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:Optional[str] = "pil"       ,  lowercase_			:bool = True       ,  lowercase_			:Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,  lowercase_			:int = 1       ,  **lowercase_			:int       ,  )    ->      int:
												UpperCAmelCase					=			self.segmentation_processor(
												    text=[text]       ,  images=[image]       ,  padding='max_length'       ,  return_tensors='pt' ).to(self.device )
												UpperCAmelCase					=			self.segmentation_model(**lowercase_ )
												UpperCAmelCase					=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
												UpperCAmelCase					=			self.numpy_to_pil(lowercase_ )[0].resize(image.size )

												# Run inpainting pipeline with the generated mask
												UpperCAmelCase					=			StableDiffusionInpaintPipeline(
												    vae=self.vae       ,  text_encoder=self.text_encoder       ,  tokenizer=self.tokenizer       ,  unet=self.unet       ,  scheduler=self.scheduler       ,  safety_checker=self.safety_checker       ,  feature_extractor=self.feature_extractor       ,  )
												return inpainting_pipeline(
												    prompt=lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  height=lowercase_       ,  width=lowercase_       ,  num_inference_steps=lowercase_       ,  guidance_scale=lowercase_       ,  negative_prompt=lowercase_       ,  num_images_per_prompt=lowercase_       ,  eta=lowercase_       ,  generator=lowercase_       ,  latents=lowercase_       ,  output_type=lowercase_       ,  return_dict=lowercase_       ,  callback=lowercase_       ,  callback_steps=lowercase_       ,  )





