



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_						:     List[Any]	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			__a				,			):






           '''simple docstring'''



           super().__init__()

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                     F"""The configuration file of this scheduler: {scheduler} is outdated.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

           if hasattr(scheduler.config				,			"skip_prk_steps"    ) and scheduler.config.skip_prk_steps is False:
                 lowerCamelCase								=		(
                     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    )
                 lowerCamelCase								=		dict(scheduler.config    )
                 lowerCamelCase								=		True
                 lowerCamelCase								=		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				,			__a = "auto"    ):






           '''simple docstring'''



           if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 lowerCamelCase								=		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           self.enable_attention_slicing(__a    )







     def 						_a      (self    ):






           '''simple docstring'''



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

           lowerCamelCase								=		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    ):






           '''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(__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				,			__a				,			__a				,			__a				,			__a = 5_12				,			__a = 5_12				,			__a = 50				,			__a = 7.5				,			__a = None				,			__a = 1				,			__a = 0.0				,			__a = None				,			__a = None				,			__a = "pil"				,			__a = True				,			__a = None				,			__a = 1				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		self.segmentation_processor(
               text=[text]				,			images=[image]				,			padding="max_length"				,			return_tensors="pt"    ).to(self.device    )
           lowerCamelCase								=		self.segmentation_model(**__a    )
           lowerCamelCase								=		torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
           lowerCamelCase								=		self.numpy_to_pil(__a    )[0].resize(image.size    )

           # Run inpainting pipeline with the generated mask
           lowerCamelCase								=		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 os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


a_						:     int	     =		logging.get_logger(__name__)

a_						:     List[str]	     =		{'vocab_file': 'spiece.model'}

a_						:     Optional[Any]	     =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=False				,			__a=True				,			__a=False				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<sep>"				,			__a="<pad>"				,			__a="<cls>"				,			__a="<mask>"				,			__a=["<eop>", "<eod>"]				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else mask_token

           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__a				,			remove_space=__a				,			keep_accents=__a				,			bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			sep_token=__a				,			pad_token=__a				,			cls_token=__a				,			mask_token=__a				,			additional_special_tokens=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		3

           lowerCamelCase								=		do_lower_case
           lowerCamelCase								=		remove_space
           lowerCamelCase								=		keep_accents
           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )

           try:
                 import jieba
           except ModuleNotFoundError as error:
                 raise error.__class__(
                     "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                     "See https://pypi.org/project/jieba/ for installation."    )
           lowerCamelCase								=		jieba
           lowerCamelCase								=		str.maketrans(" \n"				,			"\u2582\u2583"    )







     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def 						_a      (self    ):






           '''simple docstring'''



           return len(self.sp_model    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



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







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

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

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           if self.remove_space:
                 lowerCamelCase								=		" ".join(inputs.strip().split()    )
           else:
                 lowerCamelCase								=		inputs
           lowerCamelCase								=		outputs.replace("``"				,			"\""    ).replace("''"				,			"\""    )

           if not self.keep_accents:
                 lowerCamelCase								=		unicodedata.normalize("NFKD"				,			__a    )
                 lowerCamelCase								=		"".join([c for c in outputs if not unicodedata.combining(__a    )]    )
           if self.do_lower_case:
                 lowerCamelCase								=		outputs.lower()

           return outputs







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.preprocess_text(__a    )
           lowerCamelCase								=		self.sp_model.encode(__a				,			out_type=__a    )
           lowerCamelCase								=		[]
           for piece in pieces:
                 if len(__a    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
                       lowerCamelCase								=		self.sp_model.EncodeAsPieces(piece[:-1].replace(__a				,			""    )    )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]    ) == 1:
                                   lowerCamelCase								=		cur_pieces[1:]
                             else:
                                   lowerCamelCase								=		cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]    )
                       new_pieces.extend(__a    )
                 else:
                       new_pieces.append(__a    )

           return new_pieces







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.PieceToId(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.IdToPiece(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		"".join(__a    ).replace(__a				,			" "    ).strip()
           return out_string







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






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[self.cls_token_id]
           if token_ids_a is None:
                 return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







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






           '''simple docstring'''



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

           if token_ids_a is not None:
                 return ([0] * len(__a    )) + [1] + ([0] * len(__a    )) + [1, 1]
           return ([0] * len(__a    )) + [1, 1]







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






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[2]

           if token_ids_a is None:
                 return len(token_ids_a + sep    ) * [0] + cls_segment_id
           return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id







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






           '''simple docstring'''



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

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

           return (out_vocab_file,)







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		super()._decode(*__a				,			**__a    )
           lowerCamelCase								=		text.replace(" "				,			""    ).replace("\u2582"				,			" "    ).replace("\u2583"				,			"\n"    )
           return text