
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
					import tensorflow as tf







class 	A__				(	TensorFormatter[Mapping, 'tf.Tensor', Mapping]      ):






				def __init__(		self   : int       ,	a   : Dict=None       ,	**a   : Tuple  ):






							'''simple docstring'''







							super().__init__(features=a  )
							lowerCAmelCase__ : str   =						tf_tensor_kwargs
							import tensorflow as tf  # noqa: import tf at initialization






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : str  ):






							'''simple docstring'''







							import tensorflow as tf

							if isinstance(a       ,	a  ) and column:
										if all(
										    isinstance(a       ,	tf.Tensor  ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column  ):
													return tf.stack(a  )
										elif all(
										    isinstance(a       ,	(tf.Tensor, tf.RaggedTensor)  ) and x.ndim == 1 and x.dtype == column[0].dtype
										    for x in column  ):
													# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
													return tf.ragged.stack(a  )

							return column






				def 				_lowerCamelCase						(		self   : Tuple       ,	a   : List[str]  ):






							'''simple docstring'''







							import tensorflow as tf

							if value is None:
										return value

							lowerCAmelCase__ : Dict   =						{}

							if isinstance(a       ,	(np.number, np.ndarray)  ) and np.issubdtype(value.dtype       ,	np.integer  ):
										lowerCAmelCase__ : Dict   =						{'dtype': tf.intaa}
							elif isinstance(a       ,	(np.number, np.ndarray)  ) and np.issubdtype(value.dtype       ,	np.floating  ):
										lowerCAmelCase__ : Tuple   =						{'dtype': tf.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(a       ,	PIL.Image.Image  ):
													lowerCAmelCase__ : int   =						np.asarray(a  )

							return tf.convert_to_tensor(a       ,	**{**default_dtype, **self.tf_tensor_kwargs}  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : List[Any]  ):






							'''simple docstring'''







							import tensorflow as tf

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

										if isinstance(a       ,	torch.Tensor  ):
													return self._tensorize(data_struct.detach().cpu().numpy()[()]  )
							if hasattr(a       ,	'__array__'  ) and not isinstance(a       ,	tf.Tensor  ):
										lowerCAmelCase__ : Union[str, Any]   =						data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(a       ,	np.ndarray  ):
										if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(a  ) for substruct in data_struct]  )
							elif isinstance(a       ,	(list, tuple)  ):
										return self._consolidate([self.recursive_tensorize(a  ) for substruct in data_struct]  )
							return self._tensorize(a  )






				def 				_lowerCamelCase						(		self   : int       ,	a   : dict  ):






							'''simple docstring'''







							return map_nested(self._recursive_tensorize       ,	a       ,	map_list=a  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : pa.Table  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						self.numpy_arrow_extractor().extract_row(a  )
							lowerCAmelCase__ : str   =						self.python_features_decoder.decode_row(a  )
							return self.recursive_tensorize(a  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : pa.Table  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						self.numpy_arrow_extractor().extract_column(a  )
							lowerCAmelCase__ : Any   =						self.python_features_decoder.decode_column(a       ,	pa_table.column_names[0]  )
							lowerCAmelCase__ : str   =						self.recursive_tensorize(a  )
							lowerCAmelCase__ : Union[str, Any]   =						self._consolidate(a  )
							return column






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : pa.Table  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						self.numpy_arrow_extractor().extract_batch(a  )
							lowerCAmelCase__ : List[str]   =						self.python_features_decoder.decode_batch(a  )
							lowerCAmelCase__ : List[Any]   =						self.recursive_tensorize(a  )
							for column_name in batch:
										lowerCAmelCase__ : int   =						self._consolidate(batch[column_name]  )
							return batch

from typing import List, Optional, Tuple, Union

import torch

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







class 	A__				(	__magic_name__      ):






				def __init__(		self   : Any       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a       ,	scheduler=a  )






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

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

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

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )