'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __A      ( unittest.TestCase	):



    @property
    def 					_lowercase						(self    :    Tuple ):
         torch.manual_seed(0 )
         UpperCAmelCase_           =   UNetaDModel(
             block_out_channels=(32, 64)   ,			layers_per_block=2   ,			sample_size=32   ,			in_channels=3   ,			out_channels=3   ,			down_block_types=("DownBlock2D", "AttnDownBlock2D")   ,			up_block_types=("AttnUpBlock2D", "UpBlock2D")   ,			)
         return model






    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.dummy_uncond_unet
         UpperCAmelCase_           =   ScoreSdeVeScheduler()

         UpperCAmelCase_           =   ScoreSdeVePipeline(unet=__a   ,			scheduler=__a )
         sde_ve.to(__a )
         sde_ve.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   sde_ve(num_inference_steps=2   ,			output_type="numpy"   ,			generator=__a ).images

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   sde_ve(num_inference_steps=2   ,			output_type="numpy"   ,			generator=__a   ,			return_dict=__a )[
             0
         ]

         UpperCAmelCase_           =   image[0, -3:, -3:, -1]
         UpperCAmelCase_           =   image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase_           =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2




@slow
@require_torch
class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   "google/ncsnpp-church-256"
         UpperCAmelCase_           =   UNetaDModel.from_pretrained(__a )

         UpperCAmelCase_           =   ScoreSdeVeScheduler.from_pretrained(__a )

         UpperCAmelCase_           =   ScoreSdeVePipeline(unet=__a   ,			scheduler=__a )
         sde_ve.to(__a )
         sde_ve.set_progress_bar_config(disable=__a )

         UpperCAmelCase_           =   torch.manual_seed(0 )
         UpperCAmelCase_           =   sde_ve(num_inference_steps=10   ,			output_type="numpy"   ,			generator=__a ).images

         UpperCAmelCase_           =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)

         UpperCAmelCase_           =   np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2












"""simple docstring"""



# 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 torch





class 					_UpperCAmelCase       ( TensorFormatter[Mapping, "torch.Tensor", Mapping]							):




					def __init__(				self     :						Any    ,						A     :						int=None    ,						**A     :						str   )  ->  Union[str, Any]:
							super().__init__(features=A   )
							lowercase_ : Union[str, Any]							       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization




					def   A  (				self     :						Dict    ,						A     :						int   )  ->  List[Any]:
							import torch

							if isinstance(A    ,						A   ) and column:
									if all(
									    isinstance(A    ,						torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column   ):
											return torch.stack(A   )
							return column




					def   A  (				self     :						int    ,						A     :						Any   )  ->  Optional[Any]:
							import torch

							if isinstance(A    ,						(str, bytes, type(A   ))   ):
									return value
							elif isinstance(A    ,						(np.character, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.character   ):
									return value.tolist()

							lowercase_ : Any							       =   {}

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

									if isinstance(A    ,						PIL.Image.Image   ):
											lowercase_ : Dict							       =   np.asarray(A   )
							return torch.tensor(A    ,						**{**default_dtype, **self.torch_tensor_kwargs}   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]   )  ->  str:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(A    ,						'''__array__'''   ) and not isinstance(A    ,						torch.Tensor   ):
									lowercase_ : Optional[int]							       =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(A    ,						np.ndarray   ):
									if data_struct.dtype == object:  # torch 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   A  (				self     :						Dict    ,						A     :						dict   )  ->  Tuple:
							return map_nested(self._recursive_tensorize    ,						A    ,						map_list=A   )




					def   A  (				self     :						str    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[Any]							       =   self.numpy_arrow_extractor().extract_row(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_row(A   )
							return self.recursive_tensorize(A   )




					def   A  (				self     :						List[Any]    ,						A     :						pa.Table   )  ->  "torch.Tensor":
							lowercase_ : List[str]							       =   self.numpy_arrow_extractor().extract_column(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_column(A    ,						pa_table.column_names[0]   )
							lowercase_ : Optional[int]							       =   self.recursive_tensorize(A   )
							lowercase_ : Any							       =   self._consolidate(A   )
							return column






					def   A  (				self     :						List[str]    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[int]							       =   self.numpy_arrow_extractor().extract_batch(A   )
							lowercase_ : int							       =   self.python_features_decoder.decode_batch(A   )
							lowercase_ : Dict							       =   self.recursive_tensorize(A   )
							for column_name in batch:
									lowercase_ : Optional[Any]							       =   self._consolidate(batch[column_name]   )
							return batch



