'''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


SCREAMING_SNAKE_CASE_:	Optional[int]    =logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			__A (					UpperCamelCase__      ):



    def __init__(self       :       Any  ,     __a       :       CLIPSegForImageSegmentation  ,     __a       :       CLIPSegProcessor  ,     __a       :       AutoencoderKL  ,     __a       :       CLIPTextModel  ,     __a       :       CLIPTokenizer  ,     __a       :       UNetaDConditionModel  ,     __a       :       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,     __a       :       StableDiffusionSafetyChecker  ,     __a       :       CLIPImageProcessor  ,     ):
         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                  f"""The configuration file of this scheduler: {scheduler} is outdated.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"  ,     __a  ,     standard_warn=__a					)
              UpperCAmelCase_			       =  dict(scheduler.config					)
              UpperCAmelCase_			       =  1
              UpperCAmelCase_			       =  FrozenDict(__a					)

         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"  ,     __a  ,     standard_warn=__a					)
              UpperCAmelCase_			       =  dict(scheduler.config					)
              UpperCAmelCase_			       =  True
              UpperCAmelCase_			       =  FrozenDict(__a					)

         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=__a  ,     segmentation_processor=__a  ,     vae=__a  ,     text_encoder=__a  ,     tokenizer=__a  ,     unet=__a  ,     scheduler=__a  ,     safety_checker=__a  ,     feature_extractor=__a  ,     )



    def    _lowercase		(self       :       str  ,     __a       :       Optional[Union[str, int]] = "auto"					):
         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(__a					)



    def    _lowercase		(self       :       int					):
         self.enable_attention_slicing(__a					)



    def    _lowercase		(self       :       Optional[Any]					):
         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(__a  ,     __a					)



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    _lowercase		(self       :       Optional[int]					):
         if self.device != torch.device("meta"					) or not hasattr(self.unet  ,     "_hf_hook"					):
              return self.device
         for module in self.unet.modules():
              if (
                  hasattr(__a  ,     "_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       :       Dict  ,     __a       :       Union[str, List[str]]  ,     __a       :       Union[torch.FloatTensor, PIL.Image.Image]  ,     __a       :       str  ,     __a       :       int = 512  ,     __a       :       int = 512  ,     __a       :       int = 50  ,     __a       :       float = 7.5  ,     __a       :       Optional[Union[str, List[str]]] = None  ,     __a       :       Optional[int] = 1  ,     __a       :       float = 0.0  ,     __a       :       Optional[torch.Generator] = None  ,     __a       :       Optional[torch.FloatTensor] = None  ,     __a       :       Optional[str] = "pil"  ,     __a       :       bool = True  ,     __a       :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,     __a       :       int = 1  ,     **__a       :       int  ,     ):
         UpperCAmelCase_			       =  self.segmentation_processor(
             text=[text]  ,     images=[image]  ,     padding="max_length"  ,     return_tensors="pt"					).to(self.device					)
         UpperCAmelCase_			       =  self.segmentation_model(**__a					)
         UpperCAmelCase_			       =  torch.sigmoid(outputs.logits					).cpu().detach().unsqueeze(-1					).numpy()
         UpperCAmelCase_			       =  self.numpy_to_pil(__a					)[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=__a  ,     image=__a  ,     mask_image=__a  ,     height=__a  ,     width=__a  ,     num_inference_steps=__a  ,     guidance_scale=__a  ,     negative_prompt=__a  ,     num_images_per_prompt=__a  ,     eta=__a  ,     generator=__a  ,     latents=__a  ,     output_type=__a  ,     return_dict=__a  ,     callback=__a  ,     callback_steps=__a  ,     )







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     '''▁'''

UpperCamelCase__		 =     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

UpperCamelCase__		 =     {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

UpperCamelCase__		 =     {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

UpperCamelCase__		 =     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

UpperCamelCase__		 =     {'''mustc''': MUSTC_LANGS}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							MAX_MODEL_INPUT_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]

       __SCREAMING_SNAKE_CASE				    =							[]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=None      , _UpperCamelCase=None      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              __snake_case       	=							{} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=_UpperCamelCase      , eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , do_upper_case=_UpperCamelCase      , do_lower_case=_UpperCamelCase      , tgt_lang=_UpperCamelCase      , lang_codes=_UpperCamelCase      , sp_model_kwargs=self.sp_model_kwargs      , **_UpperCamelCase      , )
              __snake_case       	=							do_upper_case
              __snake_case       	=							do_lower_case

              __snake_case       	=							load_json(_UpperCamelCase   )
              __snake_case       	=							{v: k for k, v in self.encoder.items()}
              __snake_case       	=							spm_file
              __snake_case       	=							load_spm(_UpperCamelCase      , self.sp_model_kwargs   )

              if lang_codes is not None:
                     __snake_case       	=							lang_codes
                     __snake_case       	=							LANGUAGES[lang_codes]
                     __snake_case       	=							[F'<lang:{lang}>' for lang in self.langs]
                     __snake_case       	=							{lang: self.sp_model.PieceToId(F'<lang:{lang}>'   ) for lang in self.langs}

                     __snake_case       	=							self.lang_tokens
                     __snake_case       	=							tgt_lang if tgt_lang is not None else self.langs[0]

                     self.set_tgt_lang_special_tokens(self._tgt_lang   )
              else:
                     __snake_case       	=							{}


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return len(self.encoder   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self._tgt_lang


       @tgt_lang.setter
       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							new_tgt_lang
              self.set_tgt_lang_special_tokens(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							self.lang_code_to_id[tgt_lang]
              __snake_case       	=							[lang_code_id]


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              return self.sp_model.encode(_UpperCamelCase      , out_type=_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              return self.encoder.get(_UpperCamelCase      , self.encoder[self.unk_token]   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase      , self.unk_token   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							""""""
              for token in tokens:
                     # make sure that special tokens are not decoded using sentencepiece model
                     if token in self.all_special_tokens:
                            __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
                            out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                            __snake_case       	=							[]
                     else:
                            current_sub_tokens.append(_UpperCamelCase   )
              __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
              out_string += decoded.upper() if self.do_upper_case else decoded
              return out_string.strip()


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=None   )      ->							List[int]:






              """simple docstring"""


              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + [self.eos_token_id]
              # 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.eos_token_id]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_UpperCamelCase      , token_ids_a=_UpperCamelCase      , already_has_special_tokens=_UpperCamelCase   )

              __snake_case       	=							[1] * len(self.prefix_tokens   )
              __snake_case       	=							[1]
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(_UpperCamelCase   )) + suffix_ones
              return prefix_ones + ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.encoder.copy()
              vocab.update(self.added_tokens_encoder   )
              return vocab


       def __getstate__(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.__dict__.copy()
              __snake_case       	=							None
              return state


       def __setstate__(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							d

              # for backward compatibility
              if not hasattr(self      , """sp_model_kwargs"""   ):
                     __snake_case       	=							{}

              __snake_case       	=							load_spm(self.spm_file      , self.sp_model_kwargs   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              __snake_case       	=							Path(_UpperCamelCase   )
              assert save_dir.is_dir(), F'{save_directory} should be a directory'
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
              )
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
              )

              save_json(self.encoder      , _UpperCamelCase   )

              if os.path.abspath(self.spm_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.spm_file   ):
                     copyfile(self.spm_file      , _UpperCamelCase   )
              elif not os.path.isfile(self.spm_file   ):
                     with open(_UpperCamelCase      , """wb"""   ) as fi:
                            __snake_case       	=							self.sp_model.serialized_model_proto()
                            fi.write(_UpperCamelCase   )

              return (str(_UpperCamelCase   ), str(_UpperCamelCase   ))







def 						lowerCamelCase__      ( __A    :str	,__A    :Dict[str, Any]		):


       """simple docstring"""




       __snake_case       	=							sentencepiece.SentencePieceProcessor(**__A		)
       spm.Load(str(__A		)		)
       return spm







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       with open(__A	,"""r"""		) as f:
              return json.load(__A		)







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str		):


       """simple docstring"""




       with open(__A	,"""w"""		) as f:
              json.dump(__A	,__A	,indent=2		)

