




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 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= 42




					def __init__(   self:		Union[str, Any],     _lowercase:		UNetaDModel,     _lowercase:		PNDMScheduler):


									'''simple docstring'''


									super().__init__()

									__lowerCAmelCase					=  PNDMScheduler.from_config(scheduler.config)

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







					@torch.no_grad()
					def __call__(   self:		Optional[Any],     _lowercase:		int = 1,     _lowercase:		int = 50,     _lowercase:		Optional[Union[torch.Generator, List[torch.Generator]]] = None,     _lowercase:		Optional[str] = "pil",     _lowercase:		bool = True,     **_lowercase:		str,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     device=self.device,     )

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

													__lowerCAmelCase					=  self.scheduler.step(_lowercase,     _lowercase,     _lowercase).prev_sample

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

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_lowercase)











'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




