
"""simple docstring"""



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class       lowerCAmelCase__				(						lowercase_	):
     __a						=					(UnCLIPScheduler,)







     def      lowercase	(							self			:			Tuple    ,      **_lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       {
           '''num_train_timesteps''': 1000,
           '''variance_type''': '''fixed_small_log''',
           '''clip_sample''': True,
           '''clip_sample_range''': 1.0,
           '''prediction_type''': '''epsilon''',
       }

       config.update(**__UpperCamelCase			)
       return config







     def      lowercase	(							self			:			List[str]			):
       for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__UpperCamelCase			)







     def      lowercase	(							self			:			str			):
       for variance in ["fixed_small_log", "learned_range"]:
         self.check_over_configs(variance_type=__UpperCamelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__UpperCamelCase			)







     def      lowercase	(							self			:			Optional[Any]			):
       for clip_sample_range in [1, 5, 10, 20]:
         self.check_over_configs(clip_sample_range=__UpperCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       for prediction_type in ["epsilon", "sample"]:
         self.check_over_configs(prediction_type=__UpperCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       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      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config(variance_type='''fixed_small_log'''			)
       _snake_case					     =       scheduler_class(**__UpperCamelCase			)

       assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_5_4_9_6_2_5			)			) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_9_9_4_9_8_7			)			) < 1e-5







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config(variance_type='''learned_range'''			)
       _snake_case					     =       scheduler_class(**__UpperCamelCase			)

       _snake_case					     =       0.5

       assert scheduler._get_variance(1    ,      predicted_variance=__UpperCamelCase			) - -1_0.1_7_1_2_7_9_0 < 1e-5
       assert scheduler._get_variance(487    ,      predicted_variance=__UpperCamelCase			) - -5.7_9_9_8_0_5_2 < 1e-5
       assert scheduler._get_variance(999    ,      predicted_variance=__UpperCamelCase			) - -0.0_0_1_0_0_1_1 < 1e-5







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config()
       _snake_case					     =       scheduler_class(**__UpperCamelCase			)

       _snake_case					     =       scheduler.timesteps

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

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

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

         _snake_case					     =       pred_prev_sample

       _snake_case					     =       torch.sum(torch.abs(__UpperCamelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(__UpperCamelCase			)			)

       assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5			) < 1e-2
       assert abs(result_mean.item() - 0.3_2_8_4_7_4_3			) < 1e-3







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.scheduler_classes[0]
       _snake_case					     =       self.get_scheduler_config()
       _snake_case					     =       scheduler_class(**__UpperCamelCase			)

       scheduler.set_timesteps(25			)

       _snake_case					     =       scheduler.timesteps

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

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

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

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

         _snake_case					     =       pred_prev_sample

       _snake_case					     =       torch.sum(torch.abs(__UpperCamelCase			)			)
       _snake_case					     =       torch.mean(torch.abs(__UpperCamelCase			)			)

       assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3			) < 1e-2
       assert abs(result_mean.item() - 0.3_3_6_2_0_3_8			) < 1e-3







     def      lowercase	(							self			:			Any			):
       pass







     def      lowercase	(							self			:			Dict			):
       pass










"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


