






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

from accelerate.utils.dataclasses import DistributedType



class 				__UpperCAmelCase		:







   def __init__(     self:  Optional[Any]				,     UpperCAmelCase_:  List[str]=2				,     UpperCAmelCase_:  Dict=3				,     UpperCAmelCase_:  Dict=64				,     UpperCAmelCase_:  List[Any]=None					):







         '''simple docstring'''




         _SCREAMING_SNAKE_CASE     				=  np.random.default_rng(UpperCAmelCase_					)
         _SCREAMING_SNAKE_CASE     				=  length
         _SCREAMING_SNAKE_CASE     				=  rng.normal(size=(length,)					).astype(np.floataa					)
         _SCREAMING_SNAKE_CASE     				=  a * self.x + b + rng.normal(scale=0.1				,     size=(length,)					).astype(np.floataa					)







   def __len__(     self:  Dict					):







         '''simple docstring'''




         return self.length







   def __getitem__(     self:  Union[str, Any]				,     UpperCAmelCase_:  int					):







         '''simple docstring'''




         return {"x": self.x[i], "y": self.y[i]}



class 				__UpperCAmelCase		(torch.nn.Module   ):







   def __init__(     self:  Any				,     UpperCAmelCase_:  int=0				,     UpperCAmelCase_:  Optional[Any]=0				,     UpperCAmelCase_:  Any=False					):







         '''simple docstring'''




         super().__init__()
         _SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
         _SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
         _SCREAMING_SNAKE_CASE     				=  True







   def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Optional[Any]=None					):







         '''simple docstring'''




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



class 				__UpperCAmelCase		(torch.nn.Module   ):







   def __init__(     self:  Optional[int]				,     UpperCAmelCase_:  List[Any]=0				,     UpperCAmelCase_:  Union[str, Any]=0				,     UpperCAmelCase_:  int=False					):







         '''simple docstring'''




         super().__init__()
         _SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor(UpperCAmelCase_					).float()					)
         _SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(torch.tensor(UpperCAmelCase_					).float()					)
         _SCREAMING_SNAKE_CASE     				=  True







   def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  Optional[Any]=None					):







         '''simple docstring'''




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

def 	__lowerCamelCase      (		snake_case__	,snake_case__ = 16		)							->  List[str]:






      """simple docstring"""





      from datasets import load_dataset
      from transformers import AutoTokenizer

      _SCREAMING_SNAKE_CASE     				=  AutoTokenizer.from_pretrained("""bert-base-cased"""		)
      _SCREAMING_SNAKE_CASE     				=  {"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
      _SCREAMING_SNAKE_CASE     				=  load_dataset("""csv"""	,data_files=snake_case__		)
      _SCREAMING_SNAKE_CASE     				=  datasets["""train"""].unique("""label"""		)

      _SCREAMING_SNAKE_CASE     				=  {v: i for i, v in enumerate(snake_case__		)}

      def tokenize_function(snake_case__		):
            # max_length=None => use the model max length (it's actually the default)
            _SCREAMING_SNAKE_CASE     				=  tokenizer(
                examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""		)
            if "label" in examples:
                  _SCREAMING_SNAKE_CASE     				=  [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
      _SCREAMING_SNAKE_CASE     				=  datasets.map(
          snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

      def collate_fn(snake_case__		):
            # 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(snake_case__	,padding="""max_length"""	,max_length=1_28	,return_tensors="""pt"""		)
            return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""		)

      # Instantiate dataloaders.
      _SCREAMING_SNAKE_CASE     				=  DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2		)
      _SCREAMING_SNAKE_CASE     				=  DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1		)

      return train_dataloader, eval_dataloader









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Optional[Any]									=							(UnCLIPScheduler,)







			def 							UpperCamelCase				(     self:  int				,     **UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  {
									    """num_train_timesteps""": 1_000,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**UpperCAmelCase_					)
									return config







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																					if prev_timestep is not None and prev_timestep >= time_step:
																											continue

																					self.check_over_forward(time_step=UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""fixed_small_log"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.0_000E-10					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config(variance_type="""learned_range"""					)
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  0.5

									assert scheduler._get_variance(1				,     predicted_variance=UpperCAmelCase_					) - -10.1_71_27_90 < 1E-5
									assert scheduler._get_variance(487				,     predicted_variance=UpperCAmelCase_					) - -5.7_99_80_52 < 1E-5
									assert scheduler._get_variance(999				,     predicted_variance=UpperCAmelCase_					) - -0.0_01_00_11 < 1E-5







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

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

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

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

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
									assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.scheduler_classes[0]
									_SCREAMING_SNAKE_CASE     				=  self.get_scheduler_config()
									_SCREAMING_SNAKE_CASE     				=  scheduler_class(**UpperCAmelCase_					)

									scheduler.set_timesteps(25					)

									_SCREAMING_SNAKE_CASE     				=  scheduler.timesteps

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

									for i, t in enumerate(UpperCAmelCase_					):
															# 1. predict noise residual
															_SCREAMING_SNAKE_CASE     				=  model(UpperCAmelCase_				,     UpperCAmelCase_					)

															if i + 1 == timesteps.shape[0]:
																					_SCREAMING_SNAKE_CASE     				=  None
															else:
																					_SCREAMING_SNAKE_CASE     				=  timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															_SCREAMING_SNAKE_CASE     				=  scheduler.step(
															    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     prev_timestep=UpperCAmelCase_				,     generator=UpperCAmelCase_					).prev_sample

															_SCREAMING_SNAKE_CASE     				=  pred_prev_sample

									_SCREAMING_SNAKE_CASE     				=  torch.sum(torch.abs(UpperCAmelCase_					)					)
									_SCREAMING_SNAKE_CASE     				=  torch.mean(torch.abs(UpperCAmelCase_					)					)

									assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
									assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									pass

