
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class     __A(				_a    ):
				snake_case_		     =					(HeunDiscreteScheduler,)
				snake_case_		     =					1_0




				def        SCREAMING_SNAKE_CASE_	(  self	,  **_snake_case       )					->   List[str]:


								'''simple docstring'''




								__a    							=					{
								    '''num_train_timesteps''': 1_100,
								    '''beta_start''': 0.0001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**_snake_case       )
								return config




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								for timesteps in [10, 50, 100, 1_000]:
												self.check_over_configs(num_train_timesteps=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,  [0.0002, 0.002, 0.02]       ):
												self.check_over_configs(beta_start=_snake_case	,  beta_end=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								for schedule in ["linear", "scaled_linear", "exp"]:
												self.check_over_configs(beta_schedule=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								for clip_sample_range in [1.0, 2.0, 3.0]:
												self.check_over_configs(clip_sample_range=_snake_case	,  clip_sample=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					self.scheduler_classes[0]
								__a    							=					self.get_scheduler_config()
								__a    							=					scheduler_class(**_snake_case       )

								scheduler.set_timesteps(self.num_inference_steps       )

								__a    							=					self.dummy_model()
								__a    							=					self.dummy_sample_deter * scheduler.init_noise_sigma
								__a    							=					sample.to(_snake_case       )

								for i, t in enumerate(scheduler.timesteps       ):
												__a    							=					scheduler.scale_model_input(_snake_case	,  _snake_case       )

												__a    							=					model(_snake_case	,  _snake_case       )

												__a    							=					scheduler.step(_snake_case	,  _snake_case	,  _snake_case       )
												__a    							=					output.prev_sample

								__a    							=					torch.sum(torch.abs(_snake_case       )       )
								__a    							=					torch.mean(torch.abs(_snake_case       )       )

								if torch_device in ["cpu", "mps"]:
												assert abs(result_sum.item() - 0.1233       ) < 1E-2
												assert abs(result_mean.item() - 0.0002       ) < 1E-3
								else:
												# CUDA
												assert abs(result_sum.item() - 0.1233       ) < 1E-2
												assert abs(result_mean.item() - 0.0002       ) < 1E-3




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					self.scheduler_classes[0]
								__a    							=					self.get_scheduler_config(prediction_type='''v_prediction'''       )
								__a    							=					scheduler_class(**_snake_case       )

								scheduler.set_timesteps(self.num_inference_steps       )

								__a    							=					self.dummy_model()
								__a    							=					self.dummy_sample_deter * scheduler.init_noise_sigma
								__a    							=					sample.to(_snake_case       )

								for i, t in enumerate(scheduler.timesteps       ):
												__a    							=					scheduler.scale_model_input(_snake_case	,  _snake_case       )

												__a    							=					model(_snake_case	,  _snake_case       )

												__a    							=					scheduler.step(_snake_case	,  _snake_case	,  _snake_case       )
												__a    							=					output.prev_sample

								__a    							=					torch.sum(torch.abs(_snake_case       )       )
								__a    							=					torch.mean(torch.abs(_snake_case       )       )

								if torch_device in ["cpu", "mps"]:
												assert abs(result_sum.item() - 4.6_934E-07       ) < 1E-2
												assert abs(result_mean.item() - 6.1_112E-10       ) < 1E-3
								else:
												# CUDA
												assert abs(result_sum.item() - 4.693_428_650_170_972E-07       ) < 1E-2
												assert abs(result_mean.item() - 0.0002       ) < 1E-3




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								__a    							=					self.scheduler_classes[0]
								__a    							=					self.get_scheduler_config()
								__a    							=					scheduler_class(**_snake_case       )

								scheduler.set_timesteps(self.num_inference_steps	,  device=_snake_case       )

								__a    							=					self.dummy_model()
								__a    							=					self.dummy_sample_deter.to(_snake_case       ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
												__a    							=					scheduler.scale_model_input(_snake_case	,  _snake_case       )

												__a    							=					model(_snake_case	,  _snake_case       )

												__a    							=					scheduler.step(_snake_case	,  _snake_case	,  _snake_case       )
												__a    							=					output.prev_sample

								__a    							=					torch.sum(torch.abs(_snake_case       )       )
								__a    							=					torch.mean(torch.abs(_snake_case       )       )

								if str(_snake_case       ).startswith('''cpu'''       ):
												# The following sum varies between 148 and 156 on mps. Why?
												assert abs(result_sum.item() - 0.1233       ) < 1E-2
												assert abs(result_mean.item() - 0.0002       ) < 1E-3
								elif str(_snake_case       ).startswith('''mps'''       ):
												# Larger tolerance on mps
												assert abs(result_mean.item() - 0.0002       ) < 1E-2
								else:
												# CUDA
												assert abs(result_sum.item() - 0.1233       ) < 1E-2
												assert abs(result_mean.item() - 0.0002       ) < 1E-3




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					self.scheduler_classes[0]
								__a    							=					self.get_scheduler_config()
								__a    							=					scheduler_class(**_snake_case	,  use_karras_sigmas=_snake_case       )

								scheduler.set_timesteps(self.num_inference_steps	,  device=_snake_case       )

								__a    							=					self.dummy_model()
								__a    							=					self.dummy_sample_deter.to(_snake_case       ) * scheduler.init_noise_sigma
								__a    							=					sample.to(_snake_case       )

								for t in scheduler.timesteps:
												__a    							=					scheduler.scale_model_input(_snake_case	,  _snake_case       )

												__a    							=					model(_snake_case	,  _snake_case       )

												__a    							=					scheduler.step(_snake_case	,  _snake_case	,  _snake_case       )
												__a    							=					output.prev_sample

								__a    							=					torch.sum(torch.abs(_snake_case       )       )
								__a    							=					torch.mean(torch.abs(_snake_case       )       )

								assert abs(result_sum.item() - 0.0_0015       ) < 1E-2
								assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07       ) < 1E-2








# 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 tensorflow as tf







class     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




								super().__init__(features=_snake_case       )
								__a    							=					tf_tensor_kwargs
								import tensorflow as tf  # noqa: import tf at initialization




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (tf.Tensor, tf.RaggedTensor)       ) and x.ndim == 1 and x.dtype == column[0].dtype
												    for x in column       ):
																# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																return tf.ragged.stack(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.floating       ):
												__a    							=					{'''dtype''': tf.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

								return tf.convert_to_tensor(_snake_case	,  **{**default_dtype, **self.tf_tensor_kwargs}       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


								'''simple docstring'''




								import tensorflow as tf

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

												if isinstance(_snake_case	,  torch.Tensor       ):
																return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
								if hasattr(_snake_case	,  '''__array__'''       ) and not isinstance(_snake_case	,  tf.Tensor       ):
												__a    							=					data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(_snake_case	,  np.ndarray       ):
												if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								elif isinstance(_snake_case	,  (list, tuple)       ):
												return self._consolidate([self.recursive_tensorize(_snake_case       ) for substruct in data_struct]       )
								return self._tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




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