






'''simple docstring'''







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			:    Union[str, Any]					  =get_logger()

_A			:    Optional[dict]					  =None

class _lowercase			(       TensorFormatter[Mapping, """jax.Array""", Mapping]	):




			def __init__(							self:       List[Any]    ,							UpperCamelCase__:       List[Any]=None    ,							UpperCamelCase__:       Union[str, Any]=None    ,							**UpperCamelCase__:       Optional[int]  ):
										super().__init__(features=UpperCamelCase__  )
										import jax
										from jaxlib.xla_client import Device

										if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																	raise ValueError(
																	    F'''Expected {device} to be a `str` not {type(UpperCamelCase__  )}, 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`."""  )
										lowerCamelCase__      :			List[str] 						= device if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ) 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:
																	lowerCamelCase__      :			str 						= 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]  )}.'''  )
																	lowerCamelCase__      :			str 						= str(jax.devices()[0]  )
										lowerCamelCase__      :			List[Any] 						= jnp_array_kwargs




			@staticmethod
			def 	lowerCamelCase_       (							):
										import jax

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




			def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       List[Any]  ):
										import jax
										import jax.numpy as jnp

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




			def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Optional[Any]  ):
										import jax
										import jax.numpy as jnp

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

										lowerCamelCase__      :			int 						= {}

										if isinstance(UpperCamelCase__    ,							(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:
																								lowerCamelCase__      :			Union[str, Any] 						= {"""dtype""": jnp.intaa}
																	else:
																								lowerCamelCase__      :			Any 						= {"""dtype""": jnp.intaa}
										elif isinstance(UpperCamelCase__    ,							(np.number, np.ndarray)  ) and np.issubdtype(value.dtype    ,							np.floating  ):
																	lowerCamelCase__      :			Any 						= {"""dtype""": jnp.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

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

        # 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:
																	lowerCamelCase__      :			Optional[int] 						= 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(UpperCamelCase__    ,							**{**default_dtype, **self.jnp_array_kwargs}  )




			def 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       Optional[Any]  ):
										import jax

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

																	if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																								return self._tensorize(data_struct.detach().cpu().numpy()[()]  )
										if hasattr(UpperCamelCase__    ,							"""__array__"""  ) and not isinstance(UpperCamelCase__    ,							jax.Array  ):
																	lowerCamelCase__      :			List[Any] 						= data_struct.__array__()
										# support for nested types like struct of list of struct
										if isinstance(UpperCamelCase__    ,							np.ndarray  ):
																	if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																								return self._consolidate([self.recursive_tensorize(UpperCamelCase__  ) for substruct in data_struct]  )
										elif isinstance(UpperCamelCase__    ,							(list, tuple)  ):
																	return self._consolidate([self.recursive_tensorize(UpperCamelCase__  ) for substruct in data_struct]  )
										return self._tensorize(UpperCamelCase__  )




			def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       dict  ):
										return map_nested(self._recursive_tensorize    ,							UpperCamelCase__    ,							map_list=UpperCamelCase__  )




			def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       pa.Table  ):
										lowerCamelCase__      :			str 						= self.numpy_arrow_extractor().extract_row(UpperCamelCase__  )
										lowerCamelCase__      :			List[str] 						= self.python_features_decoder.decode_row(UpperCamelCase__  )
										return self.recursive_tensorize(UpperCamelCase__  )




			def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       pa.Table  ):
										lowerCamelCase__      :			List[Any] 						= self.numpy_arrow_extractor().extract_column(UpperCamelCase__  )
										lowerCamelCase__      :			Optional[Any] 						= self.python_features_decoder.decode_column(UpperCamelCase__    ,							pa_table.column_names[0]  )
										lowerCamelCase__      :			int 						= self.recursive_tensorize(UpperCamelCase__  )
										lowerCamelCase__      :			Any 						= self._consolidate(UpperCamelCase__  )
										return column




			def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       pa.Table  ):
										lowerCamelCase__      :			Any 						= self.numpy_arrow_extractor().extract_batch(UpperCamelCase__  )
										lowerCamelCase__      :			int 						= self.python_features_decoder.decode_batch(UpperCamelCase__  )
										lowerCamelCase__      :			int 						= self.recursive_tensorize(UpperCamelCase__  )
										for column_name in batch:
																	lowerCamelCase__      :			Tuple 						= self._consolidate(batch[column_name]  )
										return batch









'''simple docstring'''







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

														assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

														# train with a DDPM scheduler
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			str 						= ddpm_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			str 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Tuple 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														# recreate the model and optimizer, and retry with DDIM
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[Any] 						= self.get_model_optimizer(resolution=32  )
														model.train().to(UpperCamelCase__  )
														for i in range(4  ):
																					optimizer.zero_grad()
																					lowerCamelCase__      :			Optional[Any] 						= ddim_scheduler.add_noise(clean_images[i]    ,							noise[i]    ,							timesteps[i]  )
																					lowerCamelCase__      :			Dict 						= model(UpperCamelCase__    ,							timesteps[i]  ).sample
																					lowerCamelCase__      :			Union[str, Any] 						= torch.nn.functional.mse_loss(UpperCamelCase__    ,							noise[i]  )
																					loss.backward()
																					optimizer.step()
														del model, optimizer

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

