


'''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

__snake_case											=					get_logger()

__snake_case											=					None
class        lowercase     (							TensorFormatter[Mapping, 'jax.Array', Mapping] ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       **UpperCamelCase_							):

							'''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`.'''							)
							UpperCamelCase__       :Tuple							 =			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:
												UpperCamelCase__       :Optional[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__       :Optional[int]							 =			str(jax.devices()[0]							)
							UpperCamelCase__       :Tuple							 =			jnp_array_kwargs




		@staticmethod
		def    lowerCAmelCase__    (		):

							'''simple docstring'''


							import jax

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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''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    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''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()

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

												if isinstance(UpperCamelCase_   ,       PIL.Image.Image							):
																	UpperCamelCase__       :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:
												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(UpperCamelCase_   ,       **{**default_dtype, **self.jnp_array_kwargs}							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''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							):
												UpperCamelCase__       :int							 =			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   ,       UpperCamelCase_							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_row(UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			self.python_features_decoder.decode_row(UpperCamelCase_							)
							return self.recursive_tensorize(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_column(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			self.python_features_decoder.decode_column(UpperCamelCase_   ,       pa_table.column_names[0]							)
							UpperCamelCase__       :Dict							 =			self.recursive_tensorize(UpperCamelCase_							)
							UpperCamelCase__       :str							 =			self._consolidate(UpperCamelCase_							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.numpy_arrow_extractor().extract_batch(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			self.python_features_decoder.decode_batch(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			self.recursive_tensorize(UpperCamelCase_							)
							for column_name in batch:
												UpperCamelCase__       :Optional[int]							 =			self._consolidate(batch[column_name]							)
							return batch



'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config(steps_offset=1							)
							UpperCamelCase__       :str							 =			scheduler_class(**UpperCamelCase_							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.check_over_configs(thresholding=UpperCamelCase_							)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=UpperCamelCase_   ,       prediction_type=UpperCamelCase_   ,       sample_max_value=UpperCamelCase_   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t in [1, 10, 49]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       num_inference_steps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, eta in zip([1, 10, 49]   ,       [0.0, 0.5, 1.0]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       eta=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config()
							UpperCamelCase__       :int							 =			scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Dict							 =			self.get_scheduler_config()
							UpperCamelCase__       :Any							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

							UpperCamelCase__       :List[str]							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3