

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 						_A     (				__UpperCAmelCase				):





  '''simple docstring'''



  __lowerCamelCase			:      UNetaDModel
  __lowerCamelCase			:      KarrasVeScheduler





  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      self.register_modules(unet=lowerCamelCase__   ,scheduler=lowerCamelCase__    )





  @torch.no_grad()
  def __call__(  self   ,SCREAMING_SNAKE_CASE_ = 1   ,SCREAMING_SNAKE_CASE_ = 50   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = "pil"   ,SCREAMING_SNAKE_CASE_ = True   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		self.unet.config.sample_size
      snake_case							:		int        =		(batch_size, 3, img_size, img_size)

      snake_case							:		Optional[Any]        =		self.unet

      # sample x_0 ~ N(0, sigma_0^2 * I)
      snake_case							:		Tuple        =		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
          snake_case							:		List[Any]        =		self.scheduler.schedule[t]
          snake_case							:		Tuple        =		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
          snake_case							:		str        =		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].
          snake_case							:		List[Any]        =		(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
          snake_case							:		Any        =		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].
              snake_case							:		Optional[int]        =		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2   ,sigma_prev / 2    ).sample
              snake_case							:		Optional[int]        =		self.scheduler.step_correct(
                  lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,lowerCamelCase__   ,step_output.prev_sample   ,step_output["""derivative"""]   ,)
          snake_case							:		int        =		step_output.prev_sample

      snake_case							:		int        =		(sample / 2 + 0.5).clamp(0   ,1    )
      snake_case							:		Optional[int]        =		sample.cpu().permute(0   ,2   ,3   ,1    ).numpy()
      if output_type == "pil":
          snake_case							:		List[Any]        =		self.numpy_to_pil(lowerCamelCase__    )

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=lowerCamelCase__    )







"""simple docstring"""


import os
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 logging


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<mask>" ,     lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    # Mask token behave like a normal word, i.e. include the space before it
    lowercase__       :       Optional[int]				=						AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) else mask_token

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

    if token_ids_a is None:
     return [1] + ([0] * len(lowerCamelCase__						)) + [1]
    return [1] + ([0] * len(lowerCamelCase__						)) + [1, 1] + ([0] * len(lowerCamelCase__						)) + [1]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[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 + sep + token_ids_a + sep						) * [0]


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

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

    lowercase__       :       Optional[Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(self.vocab_file						)







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

    return (out_vocab_file,)