"""simple docstring"""




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 		__A							(				SCREAMING_SNAKE_CASE_	):
     _UpperCamelCase							:    List[Any]	      =	(DPMSolverSDEScheduler,)
     _UpperCamelCase							:    List[str]	      =	10






     def 					__A						(	self       ,  **a__						):
       _lowerCAmelCase		: Dict						    =  {
           """num_train_timesteps""": 1100,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
           """noise_sampler_seed""": 0,
       }

       config.update(**a__						)
       return config






     def 					__A						(	self						):
       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=a__						)






     def 					__A						(	self						):
       for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]       ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]						):
         self.check_over_configs(beta_start=a__       ,  beta_end=a__						)






     def 					__A						(	self						):
       for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=a__						)






     def 					__A						(	self						):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: List[str]						    =  self.get_scheduler_config()
       _lowerCAmelCase		: str						    =  scheduler_class(**a__						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _lowerCAmelCase		: Optional[int]						    =  self.dummy_model()
       _lowerCAmelCase		: str						    =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _lowerCAmelCase		: List[str]						    =  sample.to(a__						)

       for i, t in enumerate(scheduler.timesteps						):
         _lowerCAmelCase		: List[str]						    =  scheduler.scale_model_input(a__       ,  a__						)

         _lowerCAmelCase		: Optional[int]						    =  model(a__       ,  a__						)

         _lowerCAmelCase		: Optional[int]						    =  scheduler.step(a__       ,  a__       ,  a__						)
         _lowerCAmelCase		: Union[str, Any]						    =  output.prev_sample

       _lowerCAmelCase		: int						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: int						    =  torch.mean(torch.abs(a__						)						)

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5						) < 1e-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7						) < 1e-3
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6						) < 1e-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2						) < 1e-3
       else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2						) < 1e-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6						) < 1e-3






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: List[Any]						    =  self.get_scheduler_config(prediction_type="""v_prediction"""						)
       _lowerCAmelCase		: Tuple						    =  scheduler_class(**a__						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _lowerCAmelCase		: Union[str, Any]						    =  self.dummy_model()
       _lowerCAmelCase		: Union[str, Any]						    =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _lowerCAmelCase		: int						    =  sample.to(a__						)

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

         _lowerCAmelCase		: List[Any]						    =  model(a__       ,  a__						)

         _lowerCAmelCase		: List[str]						    =  scheduler.step(a__       ,  a__       ,  a__						)
         _lowerCAmelCase		: List[Any]						    =  output.prev_sample

       _lowerCAmelCase		: List[str]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Any						    =  torch.mean(torch.abs(a__						)						)

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3						) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4						) < 1e-3
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3						) < 1e-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7						) < 1e-3
       else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5						) < 1e-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1						) < 1e-3






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: str						    =  self.get_scheduler_config()
       _lowerCAmelCase		: Dict						    =  scheduler_class(**a__						)

       scheduler.set_timesteps(self.num_inference_steps       ,  device=a__						)

       _lowerCAmelCase		: Dict						    =  self.dummy_model()
       _lowerCAmelCase		: Optional[Any]						    =  self.dummy_sample_deter.to(a__						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
         _lowerCAmelCase		: Optional[Any]						    =  scheduler.scale_model_input(a__       ,  a__						)

         _lowerCAmelCase		: Dict						    =  model(a__       ,  a__						)

         _lowerCAmelCase		: int						    =  scheduler.step(a__       ,  a__       ,  a__						)
         _lowerCAmelCase		: str						    =  output.prev_sample

       _lowerCAmelCase		: Union[str, Any]						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Any						    =  torch.mean(torch.abs(a__						)						)

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8						) < 1e-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5						) < 1e-3
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2						) < 1e-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1						) < 1e-3
       else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2						) < 1e-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6						) < 1e-3






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.scheduler_classes[0]
       _lowerCAmelCase		: List[Any]						    =  self.get_scheduler_config()
       _lowerCAmelCase		: Union[str, Any]						    =  scheduler_class(**a__       ,  use_karras_sigmas=a__						)

       scheduler.set_timesteps(self.num_inference_steps       ,  device=a__						)

       _lowerCAmelCase		: Optional[int]						    =  self.dummy_model()
       _lowerCAmelCase		: Union[str, Any]						    =  self.dummy_sample_deter.to(a__						) * scheduler.init_noise_sigma
       _lowerCAmelCase		: List[str]						    =  sample.to(a__						)

       for t in scheduler.timesteps:
         _lowerCAmelCase		: Any						    =  scheduler.scale_model_input(a__       ,  a__						)

         _lowerCAmelCase		: str						    =  model(a__       ,  a__						)

         _lowerCAmelCase		: str						    =  scheduler.step(a__       ,  a__       ,  a__						)
         _lowerCAmelCase		: Union[str, Any]						    =  output.prev_sample

       _lowerCAmelCase		: int						    =  torch.sum(torch.abs(a__						)						)
       _lowerCAmelCase		: Dict						    =  torch.mean(torch.abs(a__						)						)

       if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8						) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2
       elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5						) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2
       else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2						) < 1e-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2





"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    str	      =	CpmAntTokenizer
     _UpperCamelCase							:    List[Any]	      =	False






     def 					__A						(	self						):
       super().setUp()

       _lowerCAmelCase		: Dict						    =  [
           """<d>""",
           """</d>""",
           """<s>""",
           """</s>""",
           """</_>""",
           """<unk>""",
           """<pad>""",
           """</n>""",
           """我""",
           """是""",
           """C""",
           """P""",
           """M""",
           """A""",
           """n""",
           """t""",
       ]
       _lowerCAmelCase		: int						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as vocab_writer:
         vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)






     @tooslow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b"""						)
       _lowerCAmelCase		: Optional[Any]						    =  """今天天气真好！"""
       _lowerCAmelCase		: Any						    =  ["""今天""", """天气""", """真""", """好""", """！"""]
       _lowerCAmelCase		: str						    =  tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)
       _lowerCAmelCase		: Tuple						    =  """今天天气真好！"""
       _lowerCAmelCase		: Optional[Any]						    =  [tokenizer.bos_token] + tokens

       _lowerCAmelCase		: Optional[int]						    =  [6, 9802, 14962, 2082, 831, 244]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokenizer.decode(a__						)
       self.assertEqual(a__       ,  a__						)




