



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  (DDPMScheduler,)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    {
        '''num_train_timesteps''': 1000,
        '''beta_start''': 0.0_001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''variance_type''': '''fixed_small''',
        '''clip_sample''': True,
    }

    config.update(**__UpperCAmelCase				)
    return config




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]	,				[0.002, 0.02, 0.2, 2]				):
       self.check_over_configs(beta_start=__UpperCAmelCase	,				beta_end=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for variance in ["fixed_small", "fixed_large", "other"]:
       self.check_over_configs(variance_type=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(prediction_type=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for t in [0, 500, 999]:
       self.check_over_forward(time_step=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.00_979				)				) < 1E-5
    assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    len(__UpperCAmelCase				)

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

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

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

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

    __lowerCamelCase													=    torch.sum(torch.abs(__UpperCAmelCase				)				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_sum.item() - 258.9_606				) < 1E-2
    assert abs(result_mean.item() - 0.3_372				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config(prediction_type='''v_prediction'''				)
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    len(__UpperCAmelCase				)

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

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

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

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

    __lowerCamelCase													=    torch.sum(torch.abs(__UpperCAmelCase				)				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_sum.item() - 202.0_296				) < 1E-2
    assert abs(result_mean.item() - 0.2_631				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=__UpperCAmelCase				)

    __lowerCamelCase													=    scheduler.timesteps

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

       __lowerCamelCase													=    scheduler.previous_timestep(__UpperCAmelCase				)
       __lowerCamelCase													=    prev_t.item()

       self.assertEqual(__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [100, 87, 50, 51, 0]

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [100, 87, 50, 1, 0]
    __lowerCamelCase													=    len(__UpperCAmelCase				)

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [scheduler.config.num_train_timesteps]

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











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  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  RoFormerTokenizer
 lowerCAmelCase__       					=  RoFormerTokenizerFast
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''永和服装饰品有限公司,今天天气非常好'''
    __lowerCamelCase													=    '''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				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)

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

    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_rust_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)
    self.assertListEqual(__UpperCAmelCase	,				output_text.split()				)
    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass






