



'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class    lowerCamelCase_       :
  """simple docstring"""




  def __init__(					self :      Optional[int]     ,      _a :      str=2     ,      _a :      Optional[Any]=3     ,      _a :      Optional[int]=64     ,      _a :      Tuple=None  )   ->   Optional[int]:
    __lowerCamelCase			:			Any       			=					np.random.default_rng(_a  )
    __lowerCamelCase			:			Tuple       			=					length
    __lowerCamelCase			:			int       			=					rng.normal(size=(length,)  ).astype(np.floataa  )
    __lowerCamelCase			:			List[Any]       			=					a * self.x + b + rng.normal(scale=0.1     ,      size=(length,)  ).astype(np.floataa  )




  def __len__(					self :      Union[str, Any]  )   ->   Optional[int]:
    return self.length




  def __getitem__(					self :      int     ,      _a :      Optional[int]  )   ->   List[str]:
    return {"x": self.x[i], "y": self.y[i]}






class    lowerCamelCase_       (							torch.nn.Module	):
  """simple docstring"""




  def __init__(					self :      Dict     ,      _a :      int=0     ,      _a :      Optional[Any]=0     ,      _a :      Union[str, Any]=False  )   ->   Tuple:
    super().__init__()
    __lowerCamelCase			:			Optional[Any]       			=					torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
    __lowerCamelCase			:			List[Any]       			=					torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
    __lowerCamelCase			:			Union[str, Any]       			=					True




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Any]=None  )   ->   Tuple:






    if self.first_batch:
      print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
      __lowerCamelCase			:			int       			=					False
    return x * self.a[0] + self.b[0]






class    lowerCamelCase_       (							torch.nn.Module	):
  """simple docstring"""




  def __init__(					self :      List[Any]     ,      _a :      Dict=0     ,      _a :      Tuple=0     ,      _a :      Tuple=False  )   ->   List[Any]:
    super().__init__()
    __lowerCamelCase			:			Optional[int]       			=					torch.nn.Parameter(torch.tensor(_a  ).float()  )
    __lowerCamelCase			:			Optional[int]       			=					torch.nn.Parameter(torch.tensor(_a  ).float()  )
    __lowerCamelCase			:			Optional[int]       			=					True




  def 		_lowercase	(					self :      int     ,      _a :      int=None  )   ->   Optional[int]:






    if self.first_batch:
      print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
      __lowerCamelCase			:			Tuple       			=					False
    return x * self.a + self.b




def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase = 16			) -> str:
  from datasets import load_dataset
  from transformers import AutoTokenizer

  __lowerCamelCase			:			str       			=					AutoTokenizer.from_pretrained('bert-base-cased'			)
  __lowerCamelCase			:			Optional[Any]       			=					{'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
  __lowerCamelCase			:			Tuple       			=					load_dataset('csv'			,data_files=_lowerCAmelCase			)
  __lowerCamelCase			:			int       			=					datasets['train'].unique('label'			)

  __lowerCamelCase			:			Optional[Any]       			=					{v: i for i, v in enumerate(_lowerCAmelCase			)}

  def tokenize_function(_lowerCAmelCase			):
    # max_length=None => use the model max length (it's actually the default)
    __lowerCamelCase			:			Optional[Any]       			=					tokenizer(
        examples['sentence1']			,examples['sentence2']			,truncation=_lowerCAmelCase			,max_length=_lowerCAmelCase			,padding='max_length'			)
    if "label" in examples:
      __lowerCamelCase			:			Tuple       			=					[label_to_id[l] for l in examples['label']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  __lowerCamelCase			:			Any       			=					datasets.map(
      _lowerCAmelCase			,batched=_lowerCAmelCase			,remove_columns=['sentence1', 'sentence2', 'label']			,)

  def collate_fn(_lowerCAmelCase			):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(_lowerCAmelCase			,padding='max_length'			,max_length=128			,return_tensors='pt'			)
    return tokenizer.pad(_lowerCAmelCase			,padding='longest'			,return_tensors='pt'			)

  # Instantiate dataloaders.
  __lowerCamelCase			:			Union[str, Any]       			=					DataLoader(tokenized_datasets['train']			,shuffle=_lowerCAmelCase			,collate_fn=_lowerCAmelCase			,batch_size=2			)
  __lowerCamelCase			:			Optional[Any]       			=					DataLoader(tokenized_datasets['validation']			,shuffle=_lowerCAmelCase			,collate_fn=_lowerCAmelCase			,batch_size=1			)

  return train_dataloader, eval_dataloader








import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







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







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






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







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






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







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







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter + 0.1
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter - 0.1

				SCREAMING_SNAKE_CASE     						= samplea.shape[0]
				SCREAMING_SNAKE_CASE     						= torch.stack([samplea, samplea, samplea]   ,     dim=0			)
				SCREAMING_SNAKE_CASE     						= torch.arange(lowercase			)[0:3, None].repeat(1   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

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

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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

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

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

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

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

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

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







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

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







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

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