"""simple docstring"""



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__:List[Any]			=	"""▁"""

SCREAMING_SNAKE_CASE__:Dict			=	{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__:Any			=	{
    """google/reformer-crime-and-punishment""": 524288,
}






class 				snake_case__     (   _lowercase   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			Dict           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Tuple           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Tuple           =		["""input_ids""", """attention_mask"""]


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase=[]  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a     				=  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=__UpperCamelCase  ,						unk_token=__UpperCamelCase  ,						additional_special_tokens=__UpperCamelCase  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**__UpperCamelCase  ,						)

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


 @property
 def 		a__ (				self		):
        return self.sp_model.get_piece_size()


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


 def __getstate__(				self		):
        __a     				=  self.__dict__.copy()
        __a     				=  None
        return state


 def __setstate__(				self  ,						lowerCamelCase		):
        __a     				=  d

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

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


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.sp_model.encode(__UpperCamelCase  ,						out_type=__UpperCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.sp_model.piece_to_id(__UpperCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        if index < self.sp_model.get_piece_size():
               __a     				=  self.sp_model.IdToPiece(__UpperCamelCase		)
        return token


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        __a     				=  ""
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      out_string += self.sp_model.decode(__UpperCamelCase		) + token
                      __a     				=  []
               else:
                      current_sub_tokens.append(__UpperCamelCase		)
        out_string += self.sp_model.decode(__UpperCamelCase		)
        return out_string.strip()






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(__UpperCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            __UpperCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

        return (out_vocab_file,)

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
