






'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      _UpperCAmelCase    (  SCREAMING_SNAKE_CASE__		):




   """simple docstring"""

   def __init__(	self				:			Optional[Any]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			Optional[int]	):





         '''simple docstring'''





         super().__init__()
         self.register_modules(unet=__UpperCAmelCase   ,   scheduler=__UpperCAmelCase	)

   @torch.no_grad()
   def __call__(	self				:			Tuple   ,   __UpperCAmelCase				:			Any = 1   ,   __UpperCAmelCase				:			Union[str, Any] = None   ,   __UpperCAmelCase				:			Dict = 1000   ,   __UpperCAmelCase				:			Optional[int] = "pil"   ,   __UpperCAmelCase				:			Tuple = True   ,   ):





         '''simple docstring'''





         if isinstance(self.unet.config.sample_size   ,   __UpperCAmelCase	):
               _A      =			(
                   batch_size,
                   self.unet.config.in_channels,
                   self.unet.config.sample_size,
                   self.unet.config.sample_size,
               )
         else:
               _A      =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

         if self.device.type == "mps":
               # randn does not work reproducibly on mps
               _A      =			randn_tensor(__UpperCAmelCase   ,   generator=__UpperCAmelCase	)
               _A      =			image.to(self.device	)
         else:
               _A      =			randn_tensor(__UpperCAmelCase   ,   generator=__UpperCAmelCase   ,   device=self.device	)

         # set step values
         self.scheduler.set_timesteps(__UpperCAmelCase	)

         for t in self.progress_bar(self.scheduler.timesteps	):
               # 1. predict noise model_output
               _A      =			self.unet(__UpperCAmelCase   ,   __UpperCAmelCase	).sample

               # 2. compute previous image: x_t -> x_t-1
               _A      =			self.scheduler.step(__UpperCAmelCase   ,   __UpperCAmelCase   ,   __UpperCAmelCase   ,   generator=__UpperCAmelCase	).prev_sample

         _A      =			(image / 2 + 0.5).clamp(0   ,   1	)
         _A      =			image.cpu().permute(0   ,   2   ,   3   ,   1	).numpy()
         if output_type == "pil":
               _A      =			self.numpy_to_pil(__UpperCAmelCase	)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=__UpperCAmelCase	)













'''simple docstring'''




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_reformer import ReformerTokenizer
else:
    lowerCamelCase_							      =      None


lowerCamelCase_							      =      logging.get_logger(__name__)


lowerCamelCase_							      =      '''▁'''

lowerCamelCase_							      =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

lowerCamelCase_							      =      {
    '''google/reformer-crime-and-punishment''': 52_42_88,
}

class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   snake_case					       =		VOCAB_FILES_NAMES
   snake_case					       =		PRETRAINED_VOCAB_FILES_MAP
   snake_case					       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case					       =		['''input_ids''', '''attention_mask''']
   snake_case					       =		ReformerTokenizer

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]=None   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			List[Any]="</s>"   ,   __UpperCAmelCase				:			Optional[Any]="<unk>"   ,   __UpperCAmelCase				:			List[Any]=[]   ,   **__UpperCAmelCase				:			int   ,   ):





         '''simple docstring'''





         super().__init__(
             __UpperCAmelCase   ,   tokenizer_file=__UpperCAmelCase   ,   eos_token=__UpperCAmelCase   ,   unk_token=__UpperCAmelCase   ,   additional_special_tokens=__UpperCAmelCase   ,   **__UpperCAmelCase   ,   )

         _A      =			vocab_file
         _A      =			False if not self.vocab_file else True

   def 		lowerCAmelCase				(	self				:			Any   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Optional[str] = None	):





         '''simple docstring'''





         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."	)

         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	):
               copyfile(self.vocab_file   ,   __UpperCAmelCase	)

         return (out_vocab_file,)





