




'''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


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





class    __lowercase						(  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:
       __a			=					(
           f"The configuration file of this scheduler: {scheduler} is outdated.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'      ,							UpperCamelCase      ,							standard_warn=UpperCamelCase      )
       __a			=					dict(scheduler.config      )
       __a			=					1
       __a			=					FrozenDict(UpperCamelCase      )

     if hasattr(scheduler.config      ,							'skip_prk_steps'      ) and scheduler.config.skip_prk_steps is False:
       __a			=					(
           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'      ,							UpperCamelCase      ,							standard_warn=UpperCamelCase      )
       __a			=					dict(scheduler.config      )
       __a			=					True
       __a			=					FrozenDict(UpperCamelCase      )

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







   def 		UpperCamelCase__			(	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
       __a			=					self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     self.enable_attention_slicing(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:




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

     __a			=					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(UpperCamelCase      ,							UpperCamelCase      )







   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:




     if self.device != torch.device('meta'      ) or not hasattr(self.unet      ,							'_hf_hook'      ):
       return self.device
     for module in self.unet.modules():
       if (
           hasattr(UpperCamelCase      ,							'_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      ,							)				->    Optional[Any]:
     __a			=					self.segmentation_processor(
         text=[text]      ,							images=[image]      ,							padding='max_length'      ,							return_tensors='pt'      ).to(self.device      )
     __a			=					self.segmentation_model(**UpperCamelCase      )
     __a			=					torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
     __a			=					self.numpy_to_pil(UpperCamelCase      )[0].resize(image.size      )

     # Run inpainting pipeline with the generated mask
     __a			=					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=UpperCamelCase      ,							image=UpperCamelCase      ,							mask_image=UpperCamelCase      ,							height=UpperCamelCase      ,							width=UpperCamelCase      ,							num_inference_steps=UpperCamelCase      ,							guidance_scale=UpperCamelCase      ,							negative_prompt=UpperCamelCase      ,							num_images_per_prompt=UpperCamelCase      ,							eta=UpperCamelCase      ,							generator=UpperCamelCase      ,							latents=UpperCamelCase      ,							output_type=UpperCamelCase      ,							return_dict=UpperCamelCase      ,							callback=UpperCamelCase      ,							callback_steps=UpperCamelCase      ,							)






'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      RoFormerTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         pre_tok_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or pre_tok_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
     ):
       __a			=					getattr(UpperCamelCase      ,							pre_tok_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					pre_tok_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def __getstate__(	self      )				->    List[Any]:
     __a			=					self.__dict__.copy()
     __a			=					BertPreTokenizer()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					d
     __a			=					self.__dict__['_tokenizer'].get_vocab()
     __a			=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    int:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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      ) * [0] + len(token_ids_a + sep      ) * [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    List[Any]:
     __a			=					BertPreTokenizer()
     return super().save_pretrained(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )



