





'''simple docstring'''







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


class    __a (TensorFormatter[Mapping, np.ndarray, Mapping]       ):

							def __init__(							self							:       List[Any]    ,       __magic_name__							:       List[str]=None    ,       **__magic_name__							:       int				)   ->    Optional[Any]:







													"""simple docstring"""
													super().__init__(features=__magic_name__				)
													UpperCAmelCase_     : Tuple         =					np_array_kwargs

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       str				)   ->    Dict:







													"""simple docstring"""
													if isinstance(__magic_name__    ,       __magic_name__				):
																			if column and all(
																			    isinstance(__magic_name__    ,       np.ndarray				) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column				):
																									return np.stack(__magic_name__				)
																			else:
																									# don't use np.array(column, dtype=object)
																									# since it fails in certain cases
																									# see https://stackoverflow.com/q/51005699
																									UpperCAmelCase_     : Any         =					np.empty(len(__magic_name__				)    ,       dtype=__magic_name__				)
																									UpperCAmelCase_     : List[Any]         =					column
																									return out
													return column

							def       UpperCAmelCase__		(							self							:       Optional[Any]    ,       __magic_name__							:       Any				)   ->    str:







													"""simple docstring"""
													if isinstance(__magic_name__    ,       (str, bytes, type(__magic_name__				))				):
																			return value
													elif isinstance(__magic_name__    ,       (np.character, np.ndarray)				) and np.issubdtype(value.dtype    ,       np.character				):
																			return value
													elif isinstance(__magic_name__    ,       np.number				):
																			return value

													UpperCAmelCase_     : Optional[Any]         =					{}

													if isinstance(__magic_name__    ,       np.ndarray				) and np.issubdtype(value.dtype    ,       np.integer				):
																			UpperCAmelCase_     : Any         =					{'''dtype''': np.intaa}
													elif isinstance(__magic_name__    ,       np.ndarray				) and np.issubdtype(value.dtype    ,       np.floating				):
																			UpperCAmelCase_     : Union[str, Any]         =					{'''dtype''': np.floataa}
													elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																			import PIL.Image

																			if isinstance(__magic_name__    ,       PIL.Image.Image				):
																									return np.asarray(__magic_name__    ,       **self.np_array_kwargs				)

													return np.asarray(__magic_name__    ,       **{**default_dtype, **self.np_array_kwargs}				)

							def       UpperCAmelCase__		(							self							:       Dict    ,       __magic_name__							:       Any				)   ->    Tuple:







													"""simple docstring"""
													# support for torch, tf, jax etc.
													if config.TORCH_AVAILABLE and "torch" in sys.modules:
																			import torch

																			if isinstance(__magic_name__    ,       torch.Tensor				):
																									return self._tensorize(data_struct.detach().cpu().numpy()[()]				)
													if hasattr(__magic_name__    ,       '''__array__'''				) and not isinstance(__magic_name__    ,       (np.ndarray, np.character, np.number)				):
																			UpperCAmelCase_     : int         =					data_struct.__array__()
													# support for nested types like struct of list of struct
													if isinstance(__magic_name__    ,       np.ndarray				):
																			if data_struct.dtype == object:
																									return self._consolidate([self.recursive_tensorize(__magic_name__				) for substruct in data_struct]				)
													if isinstance(__magic_name__    ,       (list, tuple)				):
																			return self._consolidate([self.recursive_tensorize(__magic_name__				) for substruct in data_struct]				)
													return self._tensorize(__magic_name__				)

							def       UpperCAmelCase__		(							self							:       List[Any]    ,       __magic_name__							:       dict				)   ->    str:







													"""simple docstring"""
													return map_nested(self._recursive_tensorize    ,       __magic_name__    ,       map_list=__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Any    ,       __magic_name__							:       pa.Table				)   ->    Mapping:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					self.numpy_arrow_extractor().extract_row(__magic_name__				)
													UpperCAmelCase_     : Tuple         =					self.python_features_decoder.decode_row(__magic_name__				)
													return self.recursive_tensorize(__magic_name__				)

							def       UpperCAmelCase__		(							self							:       int    ,       __magic_name__							:       pa.Table				)   ->    np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					self.numpy_arrow_extractor().extract_column(__magic_name__				)
													UpperCAmelCase_     : Tuple         =					self.python_features_decoder.decode_column(__magic_name__    ,       pa_table.column_names[0]				)
													UpperCAmelCase_     : Any         =					self.recursive_tensorize(__magic_name__				)
													UpperCAmelCase_     : str         =					self._consolidate(__magic_name__				)
													return column



							def       UpperCAmelCase__		(							self							:       List[str]    ,       __magic_name__							:       pa.Table				)   ->    Mapping:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.numpy_arrow_extractor().extract_batch(__magic_name__				)
													UpperCAmelCase_     : Union[str, Any]         =					self.python_features_decoder.decode_batch(__magic_name__				)
													UpperCAmelCase_     : Dict         =					self.recursive_tensorize(__magic_name__				)
													for column_name in batch:
																			UpperCAmelCase_     : int         =					self._consolidate(batch[column_name]				)
													return batch












'''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    __a (lowerCamelCase       ):
							__a   :				UNetaDModel
							__a   :				PNDMScheduler

							def __init__(							self							:       Optional[Any]    ,       __magic_name__							:       UNetaDModel    ,       __magic_name__							:       PNDMScheduler				)   ->    Tuple:







													"""simple docstring"""
													super().__init__()

													UpperCAmelCase_     : Optional[int]         =					PNDMScheduler.from_config(scheduler.config				)

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



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







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					randn_tensor(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,       generator=__magic_name__    ,       device=self.device    ,       )

													self.scheduler.set_timesteps(__magic_name__				)
													for t in self.progress_bar(self.scheduler.timesteps				):
																			UpperCAmelCase_     : Optional[int]         =					self.unet(__magic_name__    ,       __magic_name__				).sample

																			UpperCAmelCase_     : str         =					self.scheduler.step(__magic_name__    ,       __magic_name__    ,       __magic_name__				).prev_sample

													UpperCAmelCase_     : int         =					(image / 2 + 0.5).clamp(0    ,       1				)
													UpperCAmelCase_     : Any         =					image.cpu().permute(0    ,       2    ,       3    ,       1				).numpy()
													if output_type == "pil":
																			UpperCAmelCase_     : Optional[int]         =					self.numpy_to_pil(__magic_name__				)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__magic_name__				)





