



'''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    ):
	lowercase    =				DDIMPipeline
	lowercase    =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	lowercase    =				PipelineTesterMixin.required_optional_params - {
	    "num_images_per_prompt",
	    "latents",
	    "callback",
	    "callback_steps",
	}
	lowercase    =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	lowercase    =				False





	def 				_lowercase(						self					)    ->    List[str]:
								torch.manual_seed(0					)
								UpperCAmelCase  :	Union[str, Any]							  =	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""")	,	)
								UpperCAmelCase  :	int							  =	DDIMScheduler()
								UpperCAmelCase  :	int							  =	{"""unet""": unet, """scheduler""": scheduler}
								return components





	def 				_lowercase(						self	,	A	,	A=0					)    ->    Union[str, Any]:







								if str(lowerCAmelCase_					).startswith("""mps"""					):
															UpperCAmelCase  :	Any							  =	torch.manual_seed(lowerCAmelCase_					)
								else:
															UpperCAmelCase  :	int							  =	torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
								UpperCAmelCase  :	Dict							  =	{
								    """batch_size""": 1,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}
								return inputs





	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	List[Any]							  =	"""cpu"""

								UpperCAmelCase  :	int							  =	self.get_dummy_components()
								UpperCAmelCase  :	List[Any]							  =	self.pipeline_class(**lowerCAmelCase_					)
								pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

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





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





	def 				_lowercase(						self					)    ->    Any:
								super().test_save_load_local(expected_max_difference=3e-3					)





	def 				_lowercase(						self					)    ->    Optional[int]:
								super().test_save_load_optional_components(expected_max_difference=3e-3					)






	def 				_lowercase(						self					)    ->    Dict:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3					)




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





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	List[str]							  =	"""google/ddpm-cifar10-32"""

								UpperCAmelCase  :	List[Any]							  =	UNetaDModel.from_pretrained(lowerCAmelCase_					)
								UpperCAmelCase  :	Union[str, Any]							  =	DDIMScheduler()

								UpperCAmelCase  :	List[str]							  =	DDIMPipeline(unet=lowerCAmelCase_	,	scheduler=lowerCAmelCase_					)
								ddim.to(lowerCAmelCase_					)
								ddim.set_progress_bar_config(disable=lowerCAmelCase_					)

								UpperCAmelCase  :	Any							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	ddim(generator=lowerCAmelCase_	,	eta=0.0	,	output_type="""numpy"""					).images

								UpperCAmelCase  :	Any							  =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase  :	Optional[int]							  =	np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]					)

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






	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	str							  =	"""google/ddpm-ema-bedroom-256"""

								UpperCAmelCase  :	List[Any]							  =	UNetaDModel.from_pretrained(lowerCAmelCase_					)
								UpperCAmelCase  :	Dict							  =	DDIMScheduler.from_pretrained(lowerCAmelCase_					)

								UpperCAmelCase  :	Dict							  =	DDIMPipeline(unet=lowerCAmelCase_	,	scheduler=lowerCAmelCase_					)
								ddpm.to(lowerCAmelCase_					)
								ddpm.set_progress_bar_config(disable=lowerCAmelCase_					)

								UpperCAmelCase  :	Tuple							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	Optional[int]							  =	ddpm(generator=lowerCAmelCase_	,	output_type="""numpy"""					).images

								UpperCAmelCase  :	Any							  =	image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								UpperCAmelCase  :	Tuple							  =	np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]					)

								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, Dict, Optional

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
							import jax
							import jaxlib

a     :    Any      						=					get_logger()

a     :    Optional[dict]      						=					None

class     UpperCamelCase_				(							TensorFormatter[Mapping, 'jax.Array', Mapping]    ):





	def __init__(						self	,	A=None	,	A=None	,	**A					)    ->    str:
								super().__init__(features=A					)
								import jax
								from jaxlib.xla_client import Device

								if isinstance(A	,	A					):
															raise ValueError(
															    f'''Expected {device} to be a `str` not {type(A					)}, as `jaxlib.xla_extension.Device` '''
															    """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
															    """the device with `str()` to get its string identifier that will be internally mapped """
															    """to the actual `jaxlib.xla_extension.Device`."""					)
								UpperCAmelCase  :	Optional[int]							  =	device if isinstance(A	,	A					) else str(jax.devices()[0]					)
								# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
								# with `pickle` nor with `dill`, so we need to use a global variable instead
								global DEVICE_MAPPING
								if DEVICE_MAPPING is None:
															UpperCAmelCase  :	Any							  =	self._map_devices_to_str()
								if self.device not in list(DEVICE_MAPPING.keys()					):
															logger.warning(
															    f'''Device with string identifier {self.device} not listed among the available '''
															    f'''devices: {list(DEVICE_MAPPING.keys()					)}, so falling back to the default '''
															    f'''device: {str(jax.devices()[0]					)}.'''					)
															UpperCAmelCase  :	List[Any]							  =	str(jax.devices()[0]					)
								UpperCAmelCase  :	Union[str, Any]							  =	jnp_array_kwargs





	@staticmethod
	def 				_lowercase(						)    ->    Dict[str, "jaxlib.xla_extension.Device"]:
								import jax

								return {str(A					): device for device in jax.devices()}





	def 				_lowercase(						self	,	A					)    ->    str:
								import jax
								import jax.numpy as jnp

								if isinstance(A	,	A					) and column:
															if all(
															    isinstance(A	,	jax.Array					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
																						return jnp.stack(A	,	axis=0					)
								return column





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax
								import jax.numpy as jnp

								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()

								UpperCAmelCase  :	List[str]							  =	{}

								if isinstance(A	,	(np.number, np.ndarray)					) and np.issubdtype(value.dtype	,	np.integer					):
															# the default int precision depends on the jax config
															# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
															if jax.config.jax_enable_xaa:
																						UpperCAmelCase  :	str							  =	{"""dtype""": jnp.intaa}
															else:
																						UpperCAmelCase  :	int							  =	{"""dtype""": jnp.intaa}
								elif isinstance(A	,	(np.number, np.ndarray)					) and np.issubdtype(value.dtype	,	np.floating					):
															UpperCAmelCase  :	Any							  =	{"""dtype""": jnp.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

															if isinstance(A	,	PIL.Image.Image					):
																						UpperCAmelCase  :	List[str]							  =	np.asarray(A					)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
								global DEVICE_MAPPING
								if DEVICE_MAPPING is None:
															UpperCAmelCase  :	Dict							  =	self._map_devices_to_str()

								with jax.default_device(DEVICE_MAPPING[self.device]					):
															# calling jnp.array on a np.ndarray does copy the data
															# see https://github.com/google/jax/issues/4486
															return jnp.array(A	,	**{**default_dtype, **self.jnp_array_kwargs}					)





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax

								# 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	,	jax.Array					):
															UpperCAmelCase  :	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:  # jax arrays 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 				_lowercase(						self	,	A					)    ->    Dict:
								return map_nested(self._recursive_tensorize	,	A	,	map_list=A					)





	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Union[str, Any]							  =	self.numpy_arrow_extractor().extract_row(A					)
								UpperCAmelCase  :	Dict							  =	self.python_features_decoder.decode_row(A					)
								return self.recursive_tensorize(A					)





	def 				_lowercase(						self	,	A					)    ->    "jax.Array":
								UpperCAmelCase  :	int							  =	self.numpy_arrow_extractor().extract_column(A					)
								UpperCAmelCase  :	Optional[Any]							  =	self.python_features_decoder.decode_column(A	,	pa_table.column_names[0]					)
								UpperCAmelCase  :	Optional[int]							  =	self.recursive_tensorize(A					)
								UpperCAmelCase  :	Any							  =	self._consolidate(A					)
								return column






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

