


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset



class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     str):
				return [
				    {"col_1": 3, "col_2": "a"},
				    {"col_1": 2, "col_2": "b"},
				    {"col_1": 1, "col_2": "c"},
				    {"col_1": 0, "col_2": "d"},
				]






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
				return Dataset.from_dict(UpperCAmelCase)






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Any				     =						self._create_example_records()
				SCREAMING_SNAKE_CASE_				:Any				     =						Dataset.from_list(UpperCAmelCase)
				self.assertListEqual(dset.column_names   ,  ["col_1", "col_2"])
				for i, r in enumerate(UpperCAmelCase):
						self.assertDictEqual(UpperCAmelCase   ,  example_records[i])






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:int				     =						self._create_example_records()
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						Dataset.from_list(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
				self.assertEqual(dset.info   ,  dset_from_dict.info)






		def _snake_case		(						self				:     int):  # checks what happens with missing columns
				SCREAMING_SNAKE_CASE_				:Any				     =						[{"col_1": 1}, {"col_2": "x"}]
				SCREAMING_SNAKE_CASE_				:Any				     =						Dataset.from_list(UpperCAmelCase)
				self.assertDictEqual(dset[0]   ,  {"col_1": 1})
				self.assertDictEqual(dset[1]   ,  {"col_1": None})  # NB: first record is used for columns






		def _snake_case		(						self				:     List[Any]):  # checks if the type can be inferred from the second record
				SCREAMING_SNAKE_CASE_				:Tuple				     =						[{"col_1": []}, {"col_1": [1, 2]}]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Dataset.from_list(UpperCAmelCase)
				self.assertEqual(dset.info.features["col_1"]   ,  Sequence(Value("int64")))







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:int				     =						Dataset.from_list([])
				self.assertEqual(len(UpperCAmelCase)   ,  0)
				self.assertListEqual(dset.column_names   ,  [])










import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

				# train with a DDPM scheduler
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						ddpm_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))






