



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


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							Any:
           super().__init__()

           if hasattr(scheduler.config     ,"steps_offset"      ) and scheduler.config.steps_offset != 1:
               lowercase__		       =						(
                   F'''The configuration file of this scheduler: {scheduler} is outdated.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      )
               lowercase__		       =						dict(scheduler.config      )
               lowercase__		       =						1
               lowercase__		       =						FrozenDict(__a      )

           if hasattr(scheduler.config     ,"skip_prk_steps"      ) and scheduler.config.skip_prk_steps is False:
               lowercase__		       =						(
                   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      )
               lowercase__		       =						dict(scheduler.config      )
               lowercase__		       =						True
               lowercase__		       =						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 							_a      (				self     ,UpperCAmelCase_ = "auto"      )  ->							Union[str, Any]:
           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               lowercase__		       =						self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(__a      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           self.enable_attention_slicing(__a      )



       def 							_a      (				self      )  ->							Dict:
           if is_accelerate_available():
               from accelerate import cpu_offload
           else:
               raise ImportError("Please install accelerate via `pip install accelerate`"      )

           lowercase__		       =						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 							_a      (				self      )  ->							Tuple:
           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     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = 512     ,UpperCAmelCase_ = 512     ,UpperCAmelCase_ = 50     ,UpperCAmelCase_ = 7.5     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = 1     ,UpperCAmelCase_ = 0.0     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = "pil"     ,UpperCAmelCase_ = True     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = 1     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           lowercase__		       =						self.segmentation_processor(
               text=[text]     ,images=[image]     ,padding="max_length"     ,return_tensors="pt"      ).to(self.device      )
           lowercase__		       =						self.segmentation_model(**__a      )
           lowercase__		       =						torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
           lowercase__		       =						self.numpy_to_pil(__a      )[0].resize(image.size      )

           # Run inpainting pipeline with the generated mask
           lowercase__		       =						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     ,)









'''simple docstring'''





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 				snake_case (tf.keras.layers.Layer   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None      )  ->							List[Any]:
           super().__init__()
           lowercase__		       =						pad_token_id
           lowercase__		       =						max_length
           lowercase__		       =						vocab
           lowercase__		       =						merges
           lowercase__		       =						BytePairTokenizer(UpperCAmelCase_     ,UpperCAmelCase_     ,sequence_length=UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[" ".join(UpperCAmelCase_      ) for m in tokenizer.bpe_ranks.keys()]
           lowercase__		       =						tokenizer.get_vocab()
           return cls(UpperCAmelCase_     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						GPTaTokenizer.from_pretrained(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           return cls.from_tokenizer(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_      )  ->							Optional[Any]:
           return cls(**UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple:
           lowercase__		       =						self.tf_tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tf.ones_like(UpperCAmelCase_      )

           if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__		       =						max_length if max_length is not None else self.max_length

               if max_length is not None:
                   lowercase__       ,       lowercase__		       =						pad_model_inputs(
                       UpperCAmelCase_     ,max_seq_length=UpperCAmelCase_     ,pad_value=self.pad_token_id      )

           return {"attention_mask": attention_mask, "input_ids": input_ids}




