






'''simple docstring'''
import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


__lowerCAmelCase							=importlib.metadata.version("numpy")
__lowerCAmelCase							=".".join([str(x) for x in sys.version_info[:3]])


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[Any]:
										# lt + different version strings
										require_version_core('numpy<1000.4.5'     )
										require_version_core('numpy<1000.4'     )
										require_version_core('numpy<1000'     )

										# le
										require_version_core('numpy<=1000.4.5'     )
										require_version_core(F'''numpy<={numpy_ver}'''     )

										# eq
										require_version_core(F'''numpy=={numpy_ver}'''     )

										# ne
										require_version_core('numpy!=1000.4.5'     )

										# ge
										require_version_core('numpy>=1.0'     )
										require_version_core('numpy>=1.0.0'     )
										require_version_core(F'''numpy>={numpy_ver}'''     )

										# gt
										require_version_core('numpy>1.0.0'     )

										# mix
										require_version_core('numpy>1.0.0,<1000'     )

										# requirement w/o version
										require_version_core('numpy'     )

										# unmet requirements due to version conflict
										for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
															try:
																				require_version_core(UpperCAmelCase__     )
															except ImportError as e:
																				self.assertIn(F'''{req} is required'''   , str(UpperCAmelCase__     )     )
																				self.assertIn('but found'   , str(UpperCAmelCase__     )     )

        # unmet requirements due to missing module
										for req in ["numpipypie>1", "numpipypie2"]:
															try:
																				require_version_core(UpperCAmelCase__     )
															except importlib.metadata.PackageNotFoundError as e:
																				self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''   , str(UpperCAmelCase__     )     )
																				self.assertIn('Try: pip install transformers -U'   , str(UpperCAmelCase__     )     )

        # bogus requirements formats:
        # 1. whole thing
										for req in ["numpy??1.0.0", "numpy1.0.0"]:
															try:
																				require_version_core(UpperCAmelCase__     )
															except ValueError as e:
																				self.assertIn('requirement needs to be in the pip package format'   , str(UpperCAmelCase__     )     )
        # 2. only operators
										for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
															try:
																				require_version_core(UpperCAmelCase__     )
															except ValueError as e:
																				self.assertIn('need one of '   , str(UpperCAmelCase__     )     )




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										# matching requirement
										require_version('python>=3.6.0'     )

										# not matching requirements
										for req in ["python>9.9.9", "python<3.0.0"]:
															try:
																				require_version_core(UpperCAmelCase__     )
															except ImportError as e:
																				self.assertIn(F'''{req} is required'''   , str(UpperCAmelCase__     )     )
																				self.assertIn(F'''but found python=={python_ver}'''   , str(UpperCAmelCase__     )     )









'''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    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   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__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   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     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

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

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

