



'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
      from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     str				=	c_float


   @property
   def 	_UpperCamelCase       (  self ):
       return sizeof(self.value )





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_ = 0	):
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        UpperCamelCase__      =			fixed_dimension

    dimension -= num_token_to_add
    return dimension





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_	):
    return num_parameters * dtype.size





def 						snake_case__ (lowerCAmelCase_	):
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(lowerCAmelCase_	)
    except (ValueError, OSError, KeyError):
        UpperCamelCase__    ,  UpperCamelCase__      =			None, None
        try:
            UpperCamelCase__      =			AutoTokenizer.from_pretrained(lowerCAmelCase_	)
        except (OSError, KeyError):
            pass
        try:
            UpperCamelCase__      =			AutoFeatureExtractor.from_pretrained(lowerCAmelCase_	)
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                F'Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'	)
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor









'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

       self.register_modules(unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




