


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     UNetaDModel
      snake_case__				:     ScoreSdeVeScheduler
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       UNetaDModel     ,							SCREAMING_SNAKE_CASE__				:       ScoreSdeVeScheduler     )		->			Optional[int]:
             super().__init__()
             self.register_modules(unet=SCREAMING_SNAKE_CASE__     ,							scheduler=SCREAMING_SNAKE_CASE__     )

      @torch.no_grad()
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int = 1     ,							SCREAMING_SNAKE_CASE__				:       int = 2_0_0_0     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[str] = "pil"     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							**SCREAMING_SNAKE_CASE__				:       str     ,							)		->			Union[ImagePipelineOutput, Tuple]:
             a_					:		Tuple							=  self.unet.config.sample_size
             a_					:		Optional[Any]							=  (batch_size, 3, img_size, img_size)

             a_					:		Optional[Any]							=  self.unet

             a_					:		List[str]							=  randn_tensor(SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ) * self.scheduler.init_noise_sigma
             a_					:		Tuple							=  sample.to(self.device     )

             self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__     )
             self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE__     )

             for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
                    a_					:		Dict							=  self.scheduler.sigmas[i] * torch.ones(shape[0]     ,							device=self.device     )

                    # correction step
                    for _ in range(self.scheduler.config.correct_steps     ):
                           a_					:		Tuple							=  self.unet(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ).sample
                           a_					:		Optional[Any]							=  self.scheduler.step_correct(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     ).prev_sample

                    # prediction step
                    a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ).sample
                    a_					:		Tuple							=  self.scheduler.step_pred(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							generator=SCREAMING_SNAKE_CASE__     )

                    a_							,		a_					:		Union[str, Any]							=  output.prev_sample, output.prev_sample_mean

             a_					:		Dict							=  sample_mean.clamp(0     ,							1     )
             a_					:		Optional[Any]							=  sample.cpu().permute(0     ,							2     ,							3     ,							1     ).numpy()
             if output_type == "pil":
                    a_					:		List[Any]							=  self.numpy_to_pil(SCREAMING_SNAKE_CASE__     )

             if not return_dict:
                    return (sample,)

             return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__     )






import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

       # Make a hash from all this code
       a_					:		Tuple							=  full_str.encode('utf-8'				)
       return shaaaa(__A				).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase_      : List[Any]  					=       {
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
UpperCAmelCase_      : Dict  					=       {
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCAmelCase_      : Optional[int]  					=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : Dict[str, List[str]]  					=       {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
   _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')


