




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		str       =(HeunDiscreteScheduler,)
     _snake_case:		int       =10




     def 	__snake_case				(      self					:				List[str]		,					**A__					:				Optional[int]     )			->    Tuple:
           lowerCamelCase__ 					=			{
               'num_train_timesteps': 1_1_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**A__     )
           return config




     def 	__snake_case				(      self					:				str     )			->    List[str]:
           for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    Optional[Any]:
           for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,					[0.0002, 0.002, 0.02]     ):
                 self.check_over_configs(beta_start=A__		,					beta_end=A__     )




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[int]:
           for schedule in ["linear", "scaled_linear", "exp"]:
                 self.check_over_configs(beta_schedule=A__     )




     def 	__snake_case				(      self					:				List[str]     )			->    List[Any]:
           for clip_sample_range in [1.0, 2.0, 3.0]:
                 self.check_over_configs(clip_sample_range=A__		,					clip_sample=A__     )




     def 	__snake_case				(      self					:				Dict     )			->    Tuple:
           for prediction_type in ["epsilon", "v_prediction", "sample"]:
                 self.check_over_configs(prediction_type=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config()
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase__ 					=			sample.to(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			scheduler.scale_model_input(A__		,					A__     )

                 lowerCamelCase__ 					=			model(A__		,					A__     )

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     )
                 lowerCamelCase__ 					=			output.prev_sample

           lowerCamelCase__ 					=			torch.sum(torch.abs(A__     )     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           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 	__snake_case				(      self					:				str     )			->    List[Any]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config(prediction_type='v_prediction'     )
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           scheduler.set_timesteps(self.num_inference_steps     )

           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase__ 					=			sample.to(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			scheduler.scale_model_input(A__		,					A__     )

                 lowerCamelCase__ 					=			model(A__		,					A__     )

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     )
                 lowerCamelCase__ 					=			output.prev_sample

           lowerCamelCase__ 					=			torch.sum(torch.abs(A__     )     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6934E-07     ) < 1E-2
                 assert abs(result_mean.item() - 6.1112E-10     ) < 1E-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.693428650170972E-07     ) < 1E-2
                 assert abs(result_mean.item() - 0.0002     ) < 1E-3




     def 	__snake_case				(      self					:				List[Any]     )			->    List[Any]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config()
           lowerCamelCase__ 					=			scheduler_class(**A__     )

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

           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.to(A__     ) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 lowerCamelCase__ 					=			scheduler.scale_model_input(A__		,					A__     )

                 lowerCamelCase__ 					=			model(A__		,					A__     )

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     )
                 lowerCamelCase__ 					=			output.prev_sample

           lowerCamelCase__ 					=			torch.sum(torch.abs(A__     )     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           if str(A__     ).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(A__     ).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 	__snake_case				(      self					:				Union[str, Any]     )			->    str:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config()
           lowerCamelCase__ 					=			scheduler_class(**A__		,					use_karras_sigmas=A__     )

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

           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.to(A__     ) * scheduler.init_noise_sigma
           lowerCamelCase__ 					=			sample.to(A__     )

           for t in scheduler.timesteps:
                 lowerCamelCase__ 					=			scheduler.scale_model_input(A__		,					A__     )

                 lowerCamelCase__ 					=			model(A__		,					A__     )

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     )
                 lowerCamelCase__ 					=			output.prev_sample

           lowerCamelCase__ 					=			torch.sum(torch.abs(A__     )     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_sum.item() - 0.00015     ) < 1E-2
           assert abs(result_mean.item() - 1.9869554535034695E-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]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(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(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

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

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

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




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # 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__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors 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 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch