



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class        lowerCAmelCase__					(  __A       ):
				A_   :     List[str]								=     (DDPMScheduler,)


				def __UpperCamelCase						(							self      :  Tuple						, **__UpperCamelCase      :  str       )		->     Optional[int]:
											A      						=    {
											    '''num_train_timesteps''': 1_000,
											    '''beta_start''': 0.0_0_0_1,
											    '''beta_end''': 0.0_2,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__UpperCamelCase       )
											return config


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[Any]:

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:

											for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]						, [0.0_0_2, 0.0_2, 0.2, 2]       ):
																		self.check_over_configs(beta_start=__UpperCamelCase						, beta_end=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:

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


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

											for variance in ["fixed_small", "fixed_large", "other"]:
																		self.check_over_configs(variance_type=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     Dict:

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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											self.check_over_configs(thresholding=__UpperCamelCase       )
											for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=__UpperCamelCase						, prediction_type=__UpperCamelCase						, sample_max_value=__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  int       )		->     Dict:

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


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[int]:

											for t in [0, 500, 999]:
																		self.check_over_forward(time_step=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any       )		->     Any:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_0_9_7_9       )       ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.0_2       )       ) < 1e-5


				def __UpperCamelCase						(							self      :  Dict       )		->     str:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    len(__UpperCamelCase       )

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

											for t in reversed(range(__UpperCamelCase       )       ):
																		# 1. predict noise residual
																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )

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

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		A      						=    pred_prev_sample

											A      						=    torch.sum(torch.abs(__UpperCamelCase       )       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_sum.item() - 258.9_606       ) < 1e-2
											assert abs(result_mean.item() - 0.3_3_7_2       ) < 1e-3


				def __UpperCamelCase						(							self      :  List[str]       )		->     Dict:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config(prediction_type='v_prediction'       )
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    len(__UpperCamelCase       )

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

											for t in reversed(range(__UpperCamelCase       )       ):
																		# 1. predict noise residual
																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )

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

																		# if t > 0:
																		#     noise = self.dummy_sample_deter
																		#     variance = scheduler.get_variance(t) ** (0.5) * noise
																		#
																		# sample = pred_prev_sample + variance
																		A      						=    pred_prev_sample

											A      						=    torch.sum(torch.abs(__UpperCamelCase       )       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_sum.item() - 202.0_296       ) < 1e-2
											assert abs(result_mean.item() - 0.2_6_3_1       ) < 1e-3


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[int]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    [100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__UpperCamelCase       )

											A      						=    scheduler.timesteps

											for i, timestep in enumerate(__UpperCamelCase       ):
																		if i == len(__UpperCamelCase       ) - 1:
																									A      						=    -1
																		else:
																									A      						=    timesteps[i + 1]

																		A      						=    scheduler.previous_timestep(__UpperCamelCase       )
																		A      						=    prev_t.item()

																		self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Any:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    [100, 87, 50, 51, 0]

											with self.assertRaises(__UpperCamelCase						, msg='`custom_timesteps` must be in descending order.'       ):
																		scheduler.set_timesteps(timesteps=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    [100, 87, 50, 1, 0]
											A      						=    len(__UpperCamelCase       )

											with self.assertRaises(__UpperCamelCase						, msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
																		scheduler.set_timesteps(num_inference_steps=__UpperCamelCase						, timesteps=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[int]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config()
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    [scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __UpperCamelCase						, msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'						, ):
																		scheduler.set_timesteps(timesteps=__UpperCamelCase       )







'''simple docstring'''



# 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 torch
class UpperCAmelCase_       (     TensorFormatter[Mapping, '''torch.Tensor''', Mapping]					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		str=None  ,   **UpperCAmelCase      :		List[Any]		)	->	int:


       '''simple docstring'''

       super().__init__(features=UpperCAmelCase		)
       lowercase       : List[Any]						=torch_tensor_kwargs
       import torch  # noqa import torch at initialization






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       import torch

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






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Optional[Any]:


       '''simple docstring'''

       import torch

       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       : Any						={}

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

        if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
         lowercase       : List[Any]						=np.asarray(UpperCAmelCase		)
       return torch.tensor(UpperCAmelCase  ,   **{**default_dtype, **self.torch_tensor_kwargs}		)






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       import torch

       # support for torch, tf, jax etc.
       if hasattr(UpperCAmelCase  ,   '''__array__'''		) and not isinstance(UpperCAmelCase  ,   torch.Tensor		):
        lowercase       : List[Any]						=data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(UpperCAmelCase  ,   np.ndarray		):
        if data_struct.dtype == object:  # torch tensors 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 					A__   (			self      :		str  ,   UpperCAmelCase      :		dict		)	->	Any:


       '''simple docstring'''

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






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : List[str]						=self.numpy_arrow_extractor().extract_row(UpperCAmelCase		)
       lowercase       : Any						=self.python_features_decoder.decode_row(UpperCAmelCase		)
       return self.recursive_tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		pa.Table		)	->	"torch.Tensor":


       '''simple docstring'''

       lowercase       : int						=self.numpy_arrow_extractor().extract_column(UpperCAmelCase		)
       lowercase       : Tuple						=self.python_features_decoder.decode_column(UpperCAmelCase  ,   pa_table.column_names[0]		)
       lowercase       : Optional[Any]						=self.recursive_tensorize(UpperCAmelCase		)
       lowercase       : Any						=self._consolidate(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : Tuple						=self.numpy_arrow_extractor().extract_batch(UpperCAmelCase		)
       lowercase       : List[str]						=self.python_features_decoder.decode_batch(UpperCAmelCase		)
       lowercase       : Dict						=self.recursive_tensorize(UpperCAmelCase		)
       for column_name in batch:
        lowercase       : str						=self._consolidate(batch[column_name]		)
       return batch



