






"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   DDIMPipeline
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					SCREAMING_SNAKE_CASE_		:				str							    =   PipelineTesterMixin.required_optional_params - {
					    "num_images_per_prompt",
					    "latents",
					    "callback",
					    "callback_steps",
					}
					SCREAMING_SNAKE_CASE_		:				int							    =   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False




					def   A  (				self     :						Union[str, Any]   )  ->  Union[str, Any]:
							torch.manual_seed(0   )
							lowercase_ : Tuple							       =   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''')    ,						)
							lowercase_ : Dict							       =   DDIMScheduler()
							lowercase_ : Any							       =   {'''unet''': unet, '''scheduler''': scheduler}
							return components




					def   A  (				self     :						List[Any]    ,						A     :						Optional[Any]    ,						A     :						str=0   )  ->  Optional[Any]:
							if str(A   ).startswith('''mps'''   ):
									lowercase_ : str							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : Union[str, Any]							       =   {
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs




					def   A  (				self     :						int   )  ->  Optional[int]:
							lowercase_ : List[Any]							       =   '''cpu'''

							lowercase_ : Optional[Any]							       =   self.get_dummy_components()
							lowercase_ : str							       =   self.pipeline_class(**A   )
							pipe.to(A   )
							pipe.set_progress_bar_config(disable=A   )

							lowercase_ : List[Any]							       =   self.get_dummy_inputs(A   )
							lowercase_ : List[Any]							       =   pipe(**A   ).images
							lowercase_ : Dict							       =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape    ,						(1, 32, 32, 3)   )
							lowercase_ : Dict							       =   np.array(
							    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]   )
							lowercase_ : Dict							       =   np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(A    ,						1e-3   )




					def   A  (				self     :						Optional[int]   )  ->  Optional[Any]:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )




					def   A  (				self     :						Optional[int]   )  ->  Any:
							super().test_save_load_local(expected_max_difference=3e-3   )




					def   A  (				self     :						Tuple   )  ->  str:
							super().test_save_load_optional_components(expected_max_difference=3e-3   )






					def   A  (				self     :						Dict   )  ->  List[str]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3   )





@slow
@require_torch_gpu
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						List[Any]   )  ->  Any:
							lowercase_ : Optional[Any]							       =   '''google/ddpm-cifar10-32'''

							lowercase_ : Optional[int]							       =   UNetaDModel.from_pretrained(A   )
							lowercase_ : Optional[int]							       =   DDIMScheduler()

							lowercase_ : Optional[int]							       =   DDIMPipeline(unet=A    ,						scheduler=A   )
							ddim.to(A   )
							ddim.set_progress_bar_config(disable=A   )

							lowercase_ : Optional[Any]							       =   torch.manual_seed(0   )
							lowercase_ : Tuple							       =   ddim(generator=A    ,						eta=0.0    ,						output_type='''numpy'''   ).images

							lowercase_ : List[str]							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowercase_ : Any							       =   np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]   )

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






					def   A  (				self     :						Any   )  ->  Dict:
							lowercase_ : int							       =   '''google/ddpm-ema-bedroom-256'''

							lowercase_ : Tuple							       =   UNetaDModel.from_pretrained(A   )
							lowercase_ : List[Any]							       =   DDIMScheduler.from_pretrained(A   )

							lowercase_ : str							       =   DDIMPipeline(unet=A    ,						scheduler=A   )
							ddpm.to(A   )
							ddpm.set_progress_bar_config(disable=A   )

							lowercase_ : Tuple							       =   torch.manual_seed(0   )
							lowercase_ : Optional[Any]							       =   ddpm(generator=A    ,						output_type='''numpy'''   ).images

							lowercase_ : Optional[Any]							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 2_56, 2_56, 3)
							lowercase_ : List[str]							       =   np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]   )

							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



