

"""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


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

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       ):


        """simple docstring"""

        super().__init__()

        if hasattr(scheduler.config							,       """steps_offset"""	) and scheduler.config.steps_offset != 1:
              UpperCAmelCase__			:							Optional[int]							=    (
                  F"""The configuration file of this scheduler: {scheduler} is outdated.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	)
              UpperCAmelCase__			:							List[Any]							=    dict(scheduler.config	)
              UpperCAmelCase__			:							List[Any]							=    1
              UpperCAmelCase__			:							Union[str, Any]							=    FrozenDict(_lowerCamelCase	)

        if hasattr(scheduler.config							,       """skip_prk_steps"""	) and scheduler.config.skip_prk_steps is False:
              UpperCAmelCase__			:							Optional[int]							=    (
                  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"""							,       _lowerCamelCase							,       standard_warn=_lowerCamelCase	)
              UpperCAmelCase__			:							Optional[Any]							=    dict(scheduler.config	)
              UpperCAmelCase__			:							str							=    True
              UpperCAmelCase__			:							str							=    FrozenDict(_lowerCamelCase	)

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



  def  _a      (self							,       _lowerCamelCase = "auto"	):


        """simple docstring"""

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



  def  _a      (self	):


        """simple docstring"""

        self.enable_attention_slicing(_lowerCamelCase	)



  def  _a      (self	):


        """simple docstring"""

        if is_accelerate_available():
              from accelerate import cpu_offload
        else:
              raise ImportError("""Please install accelerate via `pip install accelerate`"""	)

        UpperCAmelCase__			:							Dict							=    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(_lowerCamelCase							,       _lowerCamelCase	)



  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def  _a      (self	):


        """simple docstring"""

        if self.device != torch.device("""meta"""	) or not hasattr(self.unet							,       """_hf_hook"""	):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(_lowerCamelCase							,       """_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							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase = 512							,       _lowerCamelCase = 512							,       _lowerCamelCase = 50							,       _lowerCamelCase = 7.5							,       _lowerCamelCase = None							,       _lowerCamelCase = 1							,       _lowerCamelCase = 0.0							,       _lowerCamelCase = None							,       _lowerCamelCase = None							,       _lowerCamelCase = "pil"							,       _lowerCamelCase = True							,       _lowerCamelCase = None							,       _lowerCamelCase = 1							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        UpperCAmelCase__			:							str							=    self.segmentation_processor(
            text=[text]							,       images=[image]							,       padding="""max_length"""							,       return_tensors="""pt"""	).to(self.device	)
        UpperCAmelCase__			:							int							=    self.segmentation_model(**_lowerCamelCase	)
        UpperCAmelCase__			:							List[str]							=    torch.sigmoid(outputs.logits	).cpu().detach().unsqueeze(-1	).numpy()
        UpperCAmelCase__			:							Dict							=    self.numpy_to_pil(_lowerCamelCase	)[0].resize(image.size	)

        # Run inpainting pipeline with the generated mask
        UpperCAmelCase__			:							Optional[int]							=    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=_lowerCamelCase							,       image=_lowerCamelCase							,       mask_image=_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							,       )




"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A							=  logging.get_logger(__name__)

class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  ['input_ids', 'attention_mask']



  def __init__(self							,       _lowerCamelCase="</s>"							,       _lowerCamelCase="<unk>"							,       _lowerCamelCase="<pad>"							,       _lowerCamelCase=125							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        if extra_ids > 0 and additional_special_tokens is None:
              UpperCAmelCase__			:							List[str]							=    [F"""<extra_id_{i}>""" for i in range(_lowerCamelCase	)]
        elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              UpperCAmelCase__			:							int							=    len(set(filter(lambda _lowerCamelCase	: bool("""extra_id""" in str(_lowerCamelCase	)	)							,       _lowerCamelCase	)	)	)
              if extra_tokens != extra_ids:
                    raise ValueError(
                        F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                        """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
                        """ extra_ids tokens"""	)

        UpperCAmelCase__			:							List[str]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else pad_token
        UpperCAmelCase__			:							int							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else eos_token
        UpperCAmelCase__			:							Optional[int]							=    AddedToken(_lowerCamelCase							,       lstrip=_lowerCamelCase							,       rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,       _lowerCamelCase	) else unk_token

        super().__init__(
            eos_token=_lowerCamelCase							,       unk_token=_lowerCamelCase							,       pad_token=_lowerCamelCase							,       extra_ids=_lowerCamelCase							,       additional_special_tokens=_lowerCamelCase							,       **_lowerCamelCase							,       )

        UpperCAmelCase__			:							int							=    extra_ids

        UpperCAmelCase__			:							List[str]							=    2**8  # utf is 8 bits

        # define special tokens dict
        UpperCAmelCase__			:							Dict[int, str]							=    {
            self.pad_token: 0,
            self.eos_token: 1,
            self.unk_token: 2,
        }
        UpperCAmelCase__			:							str							=    len(self.special_tokens_encoder	)
        UpperCAmelCase__			:							Union[str, Any]							=    len(_lowerCamelCase	)
        for i, token in enumerate(_lowerCamelCase	):
              UpperCAmelCase__			:							Optional[Any]							=    self.vocab_size + i - n
        UpperCAmelCase__			:							Dict[str, int]							=    {v: k for k, v in self.special_tokens_encoder.items()}



  @property
  def  _a      (self	):


        """simple docstring"""

        return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None							,       _lowerCamelCase = False	):


        """simple docstring"""

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

        # normal case: some special tokens
        if token_ids_a is None:
              return ([0] * len(_lowerCamelCase	)) + [1]
        return ([0] * len(_lowerCamelCase	)) + [1] + ([0] * len(_lowerCamelCase	)) + [1]



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if len(_lowerCamelCase	) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                  """ eos tokens being added."""	)
              return token_ids
        else:
              return token_ids + [self.eos_token_id]



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    [self.eos_token_id]

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



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        UpperCAmelCase__			:							Tuple							=    self._add_eos_if_not_present(_lowerCamelCase	)
        if token_ids_a is None:
              return token_ids_a
        else:
              UpperCAmelCase__			:							List[str]							=    self._add_eos_if_not_present(_lowerCamelCase	)
              return token_ids_a + token_ids_a



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[Any]							=    [chr(_lowerCamelCase	) for i in text.encode("""utf-8"""	)]
        return tokens



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if token in self.special_tokens_encoder:
              UpperCAmelCase__			:							List[str]							=    self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
              UpperCAmelCase__			:							Tuple							=    self.added_tokens_encoder[token]
        elif len(_lowerCamelCase	) != 1:
              UpperCAmelCase__			:							Dict							=    self.unk_token_id
        else:
              UpperCAmelCase__			:							Tuple							=    ord(_lowerCamelCase	) + self._num_special_tokens
        return token_id



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        if index in self.special_tokens_decoder:
              UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[index]
        else:
              UpperCAmelCase__			:							Dict							=    chr(index - self._num_special_tokens	)
        return token



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    B""""""
        for token in tokens:
              if token in self.special_tokens_decoder:
                    UpperCAmelCase__			:							Tuple							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.added_tokens_decoder:
                    UpperCAmelCase__			:							Optional[Any]							=    self.special_tokens_decoder[token].encode("""utf-8"""	)
              elif token in self.special_tokens_encoder:
                    UpperCAmelCase__			:							str							=    token.encode("""utf-8"""	)
              elif token in self.added_tokens_encoder:
                    UpperCAmelCase__			:							List[Any]							=    token.encode("""utf-8"""	)
              else:
                    UpperCAmelCase__			:							Optional[int]							=    bytes([ord(_lowerCamelCase	)]	)
              bstring += tok_string
        UpperCAmelCase__			:							int							=    bstring.decode("""utf-8"""							,       errors="""ignore"""	)
        return string




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase = None	):


        """simple docstring"""

        return ()

