





'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    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:
										snake_case_  :							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:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							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))










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).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(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





