


"""simple docstring"""



import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter







class       __a	(   TensorFormatter[Mapping, np.ndarray, Mapping]       ):







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

					'''simple docstring'''






					super().__init__(features=UpperCAmelCase      )
					lowerCAmelCase_			       = np_array_kwargs







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






					if isinstance(UpperCAmelCase			,  UpperCAmelCase      ):
						if column and all(
						    isinstance(UpperCAmelCase			,  np.ndarray      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
							return np.stack(UpperCAmelCase      )
						else:
							# don't use np.array(column, dtype=object)
							# since it fails in certain cases
							# see https://stackoverflow.com/q/51005699
							lowerCAmelCase_			       = np.empty(len(UpperCAmelCase      )			,  dtype=UpperCAmelCase      )
							lowerCAmelCase_			       = column
							return out
					return column







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






					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
					elif isinstance(UpperCAmelCase			,  np.number      ):
						return value

					lowerCAmelCase_			       = {}

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

						if isinstance(UpperCAmelCase			,  PIL.Image.Image      ):
							return np.asarray(UpperCAmelCase			,  **self.np_array_kwargs      )

					return np.asarray(UpperCAmelCase			,  **{**default_dtype, **self.np_array_kwargs}      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






					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			,  (np.ndarray, np.character, np.number)      ):
						lowerCAmelCase_			       = data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(UpperCAmelCase			,  np.ndarray      ):
						if data_struct.dtype == object:
							return self._consolidate([self.recursive_tensorize(UpperCAmelCase      ) for substruct in data_struct]      )
					if 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'''






					lowerCAmelCase_			       = self.numpy_arrow_extractor().extract_row(UpperCAmelCase      )
					lowerCAmelCase_			       = self.python_features_decoder.decode_row(UpperCAmelCase      )
					return self.recursive_tensorize(UpperCAmelCase      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self			,  UpperCAmelCase      ):

					'''simple docstring'''






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



"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''num_train_timesteps''': 1100,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**UpperCAmelCase      )
					return config







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for timesteps in [10, 50, 100, 1000]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]			,  [0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
						self.check_over_configs(beta_start=UpperCAmelCase			,  beta_end=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for schedule in ["linear", "scaled_linear"]:
						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 t in [0, 500, 800]:
						self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
					assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

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

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

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

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for t in scheduler.timesteps:
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3