


# 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					  =     get_logger()

a					  =     None







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

					def __init__(     self       :   Optional[int]							,					UpperCamelCase__       :   List[Any]=None							,					UpperCamelCase__       :   Optional[int]=None							,					**UpperCamelCase__       :   Any       ):






										'''simple docstring'''






										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`."""       )
										lowercase_  							=			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:
															lowercase_  							=			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]       )}.'''       )
															lowercase_  							=			str(jax.devices()[0]       )
										lowercase_  							=			jnp_array_kwargs

					@staticmethod
					def   UpperCAmelCase__       (     ):






										'''simple docstring'''






										import jax

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

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   List[Any]       ):






										'''simple docstring'''






										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   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






										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()

										lowercase_  							=			{}

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

															if isinstance(UpperCamelCase__							,					PIL.Image.Image       ):
																				lowercase_  							=			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:
															lowercase_  							=			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   UpperCAmelCase__       (     self       :   List[str]							,					UpperCamelCase__       :   Union[str, Any]       ):






										'''simple docstring'''






										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       ):
															lowercase_  							=			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   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   dict       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   str							,					UpperCamelCase__       :   pa.Table       ):






										'''simple docstring'''






										lowercase_  							=			self.numpy_arrow_extractor().extract_row(UpperCamelCase__       )
										lowercase_  							=			self.python_features_decoder.decode_row(UpperCamelCase__       )
										return self.recursive_tensorize(UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   pa.Table       ):






										'''simple docstring'''






										lowercase_  							=			self.numpy_arrow_extractor().extract_column(UpperCamelCase__       )
										lowercase_  							=			self.python_features_decoder.decode_column(UpperCamelCase__							,					pa_table.column_names[0]       )
										lowercase_  							=			self.recursive_tensorize(UpperCamelCase__       )
										lowercase_  							=			self._consolidate(UpperCamelCase__       )
										return column







					def   UpperCAmelCase__       (     self       :   Union[str, Any]							,					UpperCamelCase__       :   pa.Table       ):






										'''simple docstring'''






										lowercase_  							=			self.numpy_arrow_extractor().extract_batch(UpperCamelCase__       )
										lowercase_  							=			self.python_features_decoder.decode_batch(UpperCamelCase__       )
										lowercase_  							=			self.recursive_tensorize(UpperCamelCase__       )
										for column_name in batch:
															lowercase_  							=			self._consolidate(batch[column_name]       )
										return batch






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   (UnCLIPScheduler,)

					def   UpperCAmelCase__       (     self       :   int							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**UpperCamelCase__       )
										return config

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																				self.check_over_forward(time_step=UpperCamelCase__							,					prev_timestep=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000e-10       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1e-5

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""learned_range"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			0.5

										assert scheduler._get_variance(1							,					predicted_variance=UpperCamelCase__       ) - -10.1_712_790 < 1e-5
										assert scheduler._get_variance(487							,					predicted_variance=UpperCamelCase__       ) - -5.7_998_052 < 1e-5
										assert scheduler._get_variance(999							,					predicted_variance=UpperCamelCase__       ) - -0.0_010_011 < 1e-5

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 252.2_682_495       ) < 1e-2
										assert abs(result_mean.item() - 0.3_284_743       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										scheduler.set_timesteps(25       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															if i + 1 == timesteps.shape[0]:
																				lowercase_  							=			None
															else:
																				lowercase_  							=			timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(
															    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					prev_timestep=UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 258.2_044_983       ) < 1e-2
										assert abs(result_mean.item() - 0.3_362_038       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										pass







					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										pass


