



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	List[str]												=     (DDPMScheduler,)


   def       UpperCAmelCase		(    self	:      Dict      ,					**lowerCAmelCase	:      List[Any])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	{
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


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




         """simple docstring"""

         for timesteps in [1, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					str:




         """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=lowerCAmelCase      ,					beta_end=lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Any)						->					int:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      Dict)						->					str:




         """simple docstring"""

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


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




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      str)						->					str:




         """simple docstring"""

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


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




         """simple docstring"""

         for t in [0, 5_00, 9_99]:
               self.check_over_forward(time_step=lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      str)						->					str:




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config()
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1E-5


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




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config()
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         lowercase__  =	len(lowerCAmelCase)

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

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

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

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               lowercase__  =	pred_prev_sample

         lowercase__  =	torch.sum(torch.abs(lowerCAmelCase))
         lowercase__  =	torch.mean(torch.abs(lowerCAmelCase))

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


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




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         lowercase__  =	len(lowerCAmelCase)

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

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

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

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               lowercase__  =	pred_prev_sample

         lowercase__  =	torch.sum(torch.abs(lowerCAmelCase))
         lowercase__  =	torch.mean(torch.abs(lowerCAmelCase))

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


   def       UpperCAmelCase		(    self	:      int)						->					int:




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config()
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         lowercase__  =	[1_00, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowerCAmelCase)

         lowercase__  =	scheduler.timesteps

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

               lowercase__  =	scheduler.previous_timestep(lowerCAmelCase)
               lowercase__  =	prev_t.item()

               self.assertEqual(lowerCAmelCase      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Dict)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config()
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         lowercase__  =	[1_00, 87, 50, 51, 0]

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


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




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config()
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         lowercase__  =	[1_00, 87, 50, 1, 0]
         lowercase__  =	len(lowerCAmelCase)

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



   def       UpperCAmelCase		(    self	:      List[str])						->					Dict:




         """simple docstring"""

         lowercase__  =	self.scheduler_classes[0]
         lowercase__  =	self.get_scheduler_config()
         lowercase__  =	scheduler_class(**lowerCAmelCase)

         lowercase__  =	[scheduler.config.num_train_timesteps]

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





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    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     CpmAntTokenizer
   A     :	Optional[int]												=     False


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

         super().setUp()

         lowercase__  =	[
             '<d>',
             '</d>',
             '<s>',
             '</s>',
             '</_>',
             '<unk>',
             '<pad>',
             '</n>',
             '我',
             '是',
             'C',
             'P',
             'M',
             'A',
             'n',
             't',
         ]
         lowercase__  =	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       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b')
         lowercase__  =	'今天天气真好！'
         lowercase__  =	['今天', '天气', '真', '好', '！']
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	'今天天气真好！'
         lowercase__  =	[tokenizer.bos_token] + tokens

         lowercase__  =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)

         lowercase__  =	tokenizer.decode(lowerCAmelCase)
         self.assertEqual(lowerCAmelCase      ,					lowerCAmelCase)
