


'''simple docstring'''



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class       a							(						_a			):


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					List[str]				  =  (EulerAncestralDiscreteScheduler,)
     SCREAMING_SNAKE_CASE			:					Dict				  =  1_0

     def 					lowerCamelCase__					(							self	:						List[str]				,					**snake_case	:						Optional[Any]     )		->				List[Any]:
          __UpperCAmelCase      :					Any    				=     {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.0_001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**snake_case     )
          return config

     def 					lowerCamelCase__					(							self	:						Any     )		->				Union[str, Any]:
          for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=snake_case     )

     def 					lowerCamelCase__					(							self	:						List[Any]     )		->				int:
          for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]				,					[0.0_002, 0.002, 0.02]     ):
               self.check_over_configs(beta_start=snake_case				,					beta_end=snake_case     )

     def 					lowerCamelCase__					(							self	:						Tuple     )		->				Optional[int]:
          for schedule in ["linear", "scaled_linear"]:
               self.check_over_configs(beta_schedule=snake_case     )

     def 					lowerCamelCase__					(							self	:						str     )		->				Dict:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=snake_case     )

     def 					lowerCamelCase__					(							self	:						Tuple     )		->				Optional[Any]:
          __UpperCAmelCase      :					List[Any]    				=     self.scheduler_classes[0]
          __UpperCAmelCase      :					Any    				=     self.get_scheduler_config()
          __UpperCAmelCase      :					Optional[int]    				=     scheduler_class(**snake_case     )

          scheduler.set_timesteps(self.num_inference_steps     )

          __UpperCAmelCase      :					Any    				=     torch.manual_seed(0     )

          __UpperCAmelCase      :					List[Any]    				=     self.dummy_model()
          __UpperCAmelCase      :					Any    				=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __UpperCAmelCase      :					Optional[int]    				=     sample.to(snake_case     )

          for i, t in enumerate(scheduler.timesteps     ):
               __UpperCAmelCase      :					Optional[Any]    				=     scheduler.scale_model_input(snake_case				,					snake_case     )

               __UpperCAmelCase      :					str    				=     model(snake_case				,					snake_case     )

               __UpperCAmelCase      :					Optional[Any]    				=     scheduler.step(snake_case				,					snake_case				,					snake_case				,					generator=snake_case     )
               __UpperCAmelCase      :					Optional[Any]    				=     output.prev_sample

          __UpperCAmelCase      :					int    				=     torch.sum(torch.abs(snake_case     )     )
          __UpperCAmelCase      :					List[Any]    				=     torch.mean(torch.abs(snake_case     )     )

          assert abs(result_sum.item() - 152.3_192     ) < 1E-2
          assert abs(result_mean.item() - 0.1_983     ) < 1E-3

     def 					lowerCamelCase__					(							self	:						Optional[Any]     )		->				int:
          __UpperCAmelCase      :					Union[str, Any]    				=     self.scheduler_classes[0]
          __UpperCAmelCase      :					Dict    				=     self.get_scheduler_config(prediction_type='''v_prediction'''     )
          __UpperCAmelCase      :					Union[str, Any]    				=     scheduler_class(**snake_case     )

          scheduler.set_timesteps(self.num_inference_steps     )

          __UpperCAmelCase      :					str    				=     torch.manual_seed(0     )

          __UpperCAmelCase      :					int    				=     self.dummy_model()
          __UpperCAmelCase      :					str    				=     self.dummy_sample_deter * scheduler.init_noise_sigma
          __UpperCAmelCase      :					Dict    				=     sample.to(snake_case     )

          for i, t in enumerate(scheduler.timesteps     ):
               __UpperCAmelCase      :					int    				=     scheduler.scale_model_input(snake_case				,					snake_case     )

               __UpperCAmelCase      :					List[Any]    				=     model(snake_case				,					snake_case     )

               __UpperCAmelCase      :					int    				=     scheduler.step(snake_case				,					snake_case				,					snake_case				,					generator=snake_case     )
               __UpperCAmelCase      :					Dict    				=     output.prev_sample

          __UpperCAmelCase      :					str    				=     torch.sum(torch.abs(snake_case     )     )
          __UpperCAmelCase      :					Tuple    				=     torch.mean(torch.abs(snake_case     )     )

          assert abs(result_sum.item() - 108.4_439     ) < 1E-2
          assert abs(result_mean.item() - 0.1_412     ) < 1E-3






     def 					lowerCamelCase__					(							self	:						Tuple     )		->				List[str]:
          __UpperCAmelCase      :					List[Any]    				=     self.scheduler_classes[0]
          __UpperCAmelCase      :					Any    				=     self.get_scheduler_config()
          __UpperCAmelCase      :					Dict    				=     scheduler_class(**snake_case     )

          scheduler.set_timesteps(self.num_inference_steps				,					device=snake_case     )
          __UpperCAmelCase      :					Any    				=     torch.manual_seed(0     )

          __UpperCAmelCase      :					str    				=     self.dummy_model()
          __UpperCAmelCase      :					List[Any]    				=     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __UpperCAmelCase      :					Any    				=     sample.to(snake_case     )

          for t in scheduler.timesteps:
               __UpperCAmelCase      :					str    				=     scheduler.scale_model_input(snake_case				,					snake_case     )

               __UpperCAmelCase      :					str    				=     model(snake_case				,					snake_case     )

               __UpperCAmelCase      :					List[Any]    				=     scheduler.step(snake_case				,					snake_case				,					snake_case				,					generator=snake_case     )
               __UpperCAmelCase      :					Union[str, Any]    				=     output.prev_sample

          __UpperCAmelCase      :					Any    				=     torch.sum(torch.abs(snake_case     )     )
          __UpperCAmelCase      :					str    				=     torch.mean(torch.abs(snake_case     )     )

          assert abs(result_sum.item() - 152.3_192     ) < 1E-2
          assert abs(result_mean.item() - 0.1_983     ) < 1E-3



'''simple docstring'''



import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir



@dataclass
class       a							:


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str



class       a							(						unittest.TestCase			):


     """simple docstring"""






     def 					lowerCamelCase__					(							self	:						str     )		->				Dict:
          __UpperCAmelCase      :					str    				=     get_tests_dir('''fixtures/test_sentencepiece.model'''     )
          __UpperCAmelCase      :					List[Any]    				=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''     )

          __UpperCAmelCase      :					Tuple    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					0     )

          __UpperCAmelCase      :					str    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					1     )

          self.assertIn(
              '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
              ''' which is not implemented in the fast tokenizers.'''				,					str(w[0].message     )				,					)