





"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






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




       @torch.no_grad()
       def __call__(		self	,SCREAMING_SNAKE_CASE__ = 1	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = 10_00	,SCREAMING_SNAKE_CASE__ = "pil"	,SCREAMING_SNAKE_CASE__ = True	,)    ->   Union[ImagePipelineOutput, Tuple]:





             """simple docstring"""






             if isinstance(self.unet.config.sample_size	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
             else:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

             if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						randn_tensor(SCREAMING_SNAKE_CASE__	,generator=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						image.to(self.device							)
             else:
                   __SCREAMING_SNAKE_CASE     :Any	       =						randn_tensor(SCREAMING_SNAKE_CASE__	,generator=SCREAMING_SNAKE_CASE__	,device=self.device							)

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

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

                   # 2. compute previous image: x_t -> x_t-1
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.scheduler.step(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,generator=SCREAMING_SNAKE_CASE__							).prev_sample

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

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__							)






"""simple docstring"""
# Lint as: python3

import hashlib
from typing import Union



def   __lowerCamelCase     (							a_  :	Union[str, int, bytes]							)    ->   bytes:
      if isinstance(a_				,     a_							):
            # Data already in bytes, returns as it as
            return hash_data
      elif isinstance(a_				,     a_							):
            # We keep the data as it as for it ot be later encoded to UTF-8
            # However replace `\\` with `/` for Windows compatibility
            __SCREAMING_SNAKE_CASE     :str	       =						hash_data.replace('''\\'''				,     '''/'''							)
      elif isinstance(a_				,     a_							):
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						str(a_							)
      else:
            # If data is not of the required type, raise error
            raise InvalidKeyError(a_							)

      return hash_data.encode('''utf-8'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						'''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
             __SCREAMING_SNAKE_CASE     :int	       =						f'''\nFound Key {hash_data} of type {type(SCREAMING_SNAKE_CASE__							)}'''
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''\nKeys should be either str, int or bytes type'''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=""							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						key
             __SCREAMING_SNAKE_CASE     :str	       =						duplicate_key_indices
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						fix_msg
             __SCREAMING_SNAKE_CASE     :int	       =						'''Found multiple examples generated with the same key'''
             if len(SCREAMING_SNAKE_CASE__							) <= 20:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						f'''\nThe examples at index {", ".join(SCREAMING_SNAKE_CASE__							)} have the key {key}'''
             else:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						f'''\nThe examples at index {", ".join(duplicate_key_indices[:20]							)}... ({len(SCREAMING_SNAKE_CASE__							) - 20} more) have the key {key}'''
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						'''\n''' + fix_msg if fix_msg else ''''''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						hashlib.mda(_as_bytes(SCREAMING_SNAKE_CASE__							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self._split_mda.copy()
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						_as_bytes(SCREAMING_SNAKE_CASE__							)
             mda.update(SCREAMING_SNAKE_CASE__							)
             # Convert to integer with hexadecimal conversion
             return int(mda.hexdigest()	,16							)