


'''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 		UpperCamelCase__    (       SCREAMING_SNAKE_CASE__		):



     """simple docstring"""






     __magic_name__				=			42
     __magic_name__				=			42







     def __init__(       self					,		snake_case__					,		snake_case__      ):

            '''simple docstring'''

            super().__init__()

            _lowerCAmelCase			:							Dict      	=				PNDMScheduler.from_config(scheduler.config      )

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







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

            '''simple docstring'''

            _lowerCAmelCase			:							Any      	=				randn_tensor(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,		generator=_SCREAMING_SNAKE_CASE					,		device=self.device					,		)

            self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE      )
            for t in self.progress_bar(self.scheduler.timesteps      ):
                   _lowerCAmelCase			:							List[str]      	=				self.unet(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ).sample

                   _lowerCAmelCase			:							Tuple      	=				self.scheduler.step(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ).prev_sample

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

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE      )









import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
							lowercase     :							List[Any]	     =			[]

							lowercase     :							Optional[int]	     =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
							lowercase     :							Optional[Any]	     =			glob.glob(pattern)
							# TODO: deal with TF/Flax too
							lowercase     :							Tuple	     =			[
							    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
							]

							for test_file in test_files:
														lowercase     :							Union[str, Any]	     =			get_tester_classes(test_file)
														for tester_class in tester_classes:
																					# A few tester classes don't have `parent` parameter in `__init__`.
																					# TODO: deal this better
																					try:
																												lowercase     :							Union[str, Any]	     =			tester_class(parent=None)
																					except Exception:
																												continue
																					if hasattr(tester, '''get_config'''):
																												lowercase     :							Tuple	     =			tester.get_config()
																												for k, v in config.to_dict().items():
																																			if isinstance(v, int):
																																										lowercase     :							Tuple	     =			None
																																										if k in ["vocab_size"]:
																																																	lowercase     :							str	     =			1_00
																																										elif k in ["max_position_embeddings"]:
																																																	lowercase     :							List[str]	     =			1_28
																																										elif k in ["hidden_size", "d_model"]:
																																																	lowercase     :							Union[str, Any]	     =			40
																																										elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																																	lowercase     :							List[str]	     =			5
																																										if target is not None and v > target:
																																																	failures.append(
																																																	    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																																																	    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																																																	    F""" than {target}."""
																																																	)

							if len(failures) > 0:
														raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))
