"""simple docstring"""







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_a					:					str=			logging.get_logger(__name__)  # pylint: disable=invalid-name
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[List, PIL.Image.Image, torch.Tensor]  )							->      List[Any]:

	'''simple docstring'''





	warnings.warn(
	    'The preprocess method is deprecated and will be removed in a future version. Please'
	    ' use VaeImageProcessor.preprocess instead'      ,							UpperCAmelCase_      ,							)
	if isinstance(UpperCAmelCase_      ,							torch.Tensor  ):
		return image
	elif isinstance(UpperCAmelCase_      ,							PIL.Image.Image  ):
		__snake_case      :							Dict						=			[image]

	if isinstance(image[0]      ,							PIL.Image.Image  ):
		__snake_case		,    __snake_case      :							Optional[Any]						=			image[0].size
		__snake_case		,    __snake_case      :							Dict						=			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

		__snake_case      :							Any						=			[np.array(i.resize((w, h)      ,							resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
		__snake_case      :							Optional[int]						=			np.concatenate(UpperCAmelCase_      ,							axis=0  )
		__snake_case      :							str						=			np.array(UpperCAmelCase_  ).astype(np.floataa  ) / 255.0
		__snake_case      :							Tuple						=			image.transpose(0      ,							3      ,							1      ,							2  )
		__snake_case      :							Union[str, Any]						=			2.0 * image - 1.0
		__snake_case      :							Dict						=			torch.from_numpy(UpperCAmelCase_  )
	elif isinstance(image[0]      ,							torch.Tensor  ):
		__snake_case      :							Any						=			torch.cat(UpperCAmelCase_      ,							dim=0  )
	return image
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[List, PIL.Image.Image, torch.Tensor]  )							->      Any:

	'''simple docstring'''





	if isinstance(UpperCAmelCase_      ,							torch.Tensor  ):
		return mask
	elif isinstance(UpperCAmelCase_      ,							PIL.Image.Image  ):
		__snake_case      :							List[str]						=			[mask]

	if isinstance(mask[0]      ,							PIL.Image.Image  ):
		__snake_case		,    __snake_case      :							Any						=			mask[0].size
		__snake_case		,    __snake_case      :							Optional[int]						=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		__snake_case      :							Union[str, Any]						=			[np.array(m.convert('L'  ).resize((w, h)      ,							resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
		__snake_case      :							List[str]						=			np.concatenate(UpperCAmelCase_      ,							axis=0  )
		__snake_case      :							int						=			mask.astype(np.floataa  ) / 255.0
		__snake_case      :							Tuple						=			0
		__snake_case      :							int						=			1
		__snake_case      :							Tuple						=			torch.from_numpy(UpperCAmelCase_  )
	elif isinstance(mask[0]      ,							torch.Tensor  ):
		__snake_case      :							Tuple						=			torch.cat(UpperCAmelCase_      ,							dim=0  )
	return mask

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				UNetaDModel
				UpperCAmelCase    :				RePaintScheduler



				def __init__(self    :				Dict     ,  _A    :				str     ,  _A    :				Union[str, Any])   ->     Union[str, Any]:
					super().__init__()
					self.register_modules(unet=_A     ,  scheduler=_A)






				@torch.no_grad()
				def __call__(self    :				List[str]     ,  _A    :				Union[torch.Tensor, PIL.Image.Image]     ,  _A    :				Union[torch.Tensor, PIL.Image.Image]     ,  _A    :				int = 2_50     ,  _A    :				float = 0.0     ,  _A    :				int = 10     ,  _A    :				int = 10     ,  _A    :				Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  _A    :				Optional[str] = "pil"     ,  _A    :				bool = True     ,  )   ->     Union[ImagePipelineOutput, Tuple]:
					__snake_case      :							Optional[int]						=			image

					__snake_case      :							Union[str, Any]						=			_preprocess_image(_A)
					__snake_case      :							Any						=			original_image.to(device=self.device     ,  dtype=self.unet.dtype)
					__snake_case      :							Optional[int]						=			_preprocess_mask(_A)
					__snake_case      :							Union[str, Any]						=			mask_image.to(device=self.device     ,  dtype=self.unet.dtype)

					__snake_case      :							Optional[Any]						=			original_image.shape[0]

					# sample gaussian noise to begin the loop
					if isinstance(_A     ,  _A) and len(_A) != batch_size:
						raise ValueError(
						    f"You have passed a list of generators of length {len(_A)}, but requested an effective batch"
						    f" size of {batch_size}. Make sure the batch size matches the length of the generators.")

					__snake_case      :							int						=			original_image.shape
					__snake_case      :							int						=			randn_tensor(_A     ,  generator=_A     ,  device=self.device     ,  dtype=self.unet.dtype)

					# set step values
					self.scheduler.set_timesteps(_A     ,  _A     ,  _A     ,  self.device)
					__snake_case      :							Dict						=			eta

					__snake_case      :							List[Any]						=			self.scheduler.timesteps[0] + 1
					__snake_case      :							Any						=			generator[0] if isinstance(_A     ,  _A) else generator
					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
						if t < t_last:
							# predict the noise residual
							__snake_case      :							List[str]						=			self.unet(_A     ,  _A).sample
							# compute previous image: x_t -> x_t-1
							__snake_case      :							Any						=			self.scheduler.step(_A     ,  _A     ,  _A     ,  _A     ,  _A     ,  _A).prev_sample

						else:
							# compute the reverse: x_t-1 -> x_t
							__snake_case      :							Dict						=			self.scheduler.undo_step(_A     ,  _A     ,  _A)
						__snake_case      :							List[Any]						=			t

					__snake_case      :							List[Any]						=			(image / 2 + 0.5).clamp(0     ,  1)
					__snake_case      :							Tuple						=			image.cpu().permute(0     ,  2     ,  3     ,  1).numpy()
					if output_type == "pil":
						__snake_case      :							str						=			self.numpy_to_pil(_A)

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=_A)






"""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


_a					:					List[Any]=			logging.get_logger(__name__)

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

_a					:					int=			{
    "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"
        ),
    }
}

_a					:					Optional[Any]=			{
    "junnyu/roformer_chinese_small": 1_536,
    "junnyu/roformer_chinese_base": 1_536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


_a					:					str=			{
    "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 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Union[str, Any]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				Optional[int]   =		PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				List[str]   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				int   =		PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Dict   =		RoFormerTokenizer



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

					__snake_case      :							List[Any]						=			json.loads(self.backend_tokenizer.normalizer.__getstate__())
					if (
					    pre_tok_state.get('lowercase'     ,  _A) != do_lower_case
					    or pre_tok_state.get('strip_accents'     ,  _A) != strip_accents
					):
						__snake_case      :							Union[str, Any]						=			getattr(_A     ,  pre_tok_state.pop('type'))
						__snake_case      :							Union[str, Any]						=			do_lower_case
						__snake_case      :							str						=			strip_accents
						__snake_case      :							Optional[int]						=			pre_tok_class(**_A)

					__snake_case      :							int						=			do_lower_case



				def __getstate__(self    :				Optional[Any])   ->     Dict:
					__snake_case      :							Optional[int]						=			self.__dict__.copy()
					__snake_case      :							int						=			BertPreTokenizer()
					return state



				def __setstate__(self    :				Optional[Any]     ,  _A    :				Optional[Any])   ->     Dict:
					__snake_case      :							List[str]						=			d
					__snake_case      :							str						=			self.__dict__['_tokenizer'].get_vocab()
					__snake_case      :							int						=			PreTokenizer.custom(JiebaPreTokenizer(_A))



				def  _lowercase					(self    :				int     ,  _A    :				Tuple     ,  _A    :				Any=None)   ->     str:
					__snake_case      :							Dict						=			[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  _lowercase					(self    :				List[str]     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					__snake_case      :							Tuple						=			[self.sep_token_id]
					__snake_case      :							Optional[Any]						=			[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  _lowercase					(self    :				List[Any]     ,  _A    :				str     ,  _A    :				Optional[str] = None)   ->     Tuple[str]:
					__snake_case      :							List[Any]						=			self._tokenizer.model.save(_A     ,  name=_A)
					return tuple(_A)






				def  _lowercase					(self    :				int     ,  _A    :				Optional[int]     ,  _A    :				Tuple=None     ,  _A    :				Tuple=None     ,  _A    :				Dict=False     ,  **_A    :				Optional[int]     ,  )   ->     Optional[Any]:
					__snake_case      :							Optional[Any]						=			BertPreTokenizer()
					return super().save_pretrained(_A     ,  _A     ,  _A     ,  _A     ,  **_A)





