

"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     _a     ):
     _lowerCamelCase     =       (UnCLIPScheduler,)







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1000,
          '''variance_type''': '''fixed_small_log''',
          '''clip_sample''': True,
          '''clip_sample_range''': 1.0,
          '''prediction_type''': '''epsilon''',
      }

      config.update(**snake_case_       )
      return config







     def 	lowerCAmelCase__    (      self       ):
      for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=snake_case_       )







     def 	lowerCAmelCase__    (      self       ):
      for variance in ["fixed_small_log", "learned_range"]:
       self.check_over_configs(variance_type=snake_case_       )







     def 	lowerCAmelCase__    (      self       ):
      for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=snake_case_       )







     def 	lowerCAmelCase__    (      self       ):
      for clip_sample_range in [1, 5, 10, 20]:
       self.check_over_configs(clip_sample_range=snake_case_       )







     def 	lowerCAmelCase__    (      self       ):
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(prediction_type=snake_case_       )







     def 	lowerCAmelCase__    (      self       ):
      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=snake_case_ ,						prev_timestep=snake_case_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(variance_type='''fixed_small_log'''       )
      __magic_name__     =      scheduler_class(**snake_case_       )

      assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0_0_0_0E-1_0       )       ) < 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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(variance_type='''learned_range'''       )
      __magic_name__     =      scheduler_class(**snake_case_       )

      __magic_name__     =      0.5

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**snake_case_       )

      __magic_name__     =      scheduler.timesteps

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

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

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

       __magic_name__     =      pred_prev_sample

      __magic_name__     =      torch.sum(torch.abs(snake_case_       )       )
      __magic_name__     =      torch.mean(torch.abs(snake_case_       )       )

      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 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**snake_case_       )

      scheduler.set_timesteps(25       )

      __magic_name__     =      scheduler.timesteps

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

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

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

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

       __magic_name__     =      pred_prev_sample

      __magic_name__     =      torch.sum(torch.abs(snake_case_       )       )
      __magic_name__     =      torch.mean(torch.abs(snake_case_       )       )

      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 	lowerCAmelCase__    (      self       ):
      pass







     def 	lowerCAmelCase__    (      self       ):
      pass








"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		{
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       '''fnet'''







     def __init__(      self ,						UpperCamelCase_=3_2000 ,						UpperCamelCase_=768 ,						UpperCamelCase_=12 ,						UpperCamelCase_=3072 ,						UpperCamelCase_="gelu_new" ,						UpperCamelCase_=0.1 ,						UpperCamelCase_=512 ,						UpperCamelCase_=4 ,						UpperCamelCase_=0.0_2 ,						UpperCamelCase_=1E-1_2 ,						UpperCamelCase_=False ,						UpperCamelCase_=512 ,						UpperCamelCase_=3 ,						UpperCamelCase_=1 ,						UpperCamelCase_=2 ,						**UpperCamelCase_ ,						):
      super().__init__(pad_token_id=UpperCamelCase_ ,						bos_token_id=UpperCamelCase_ ,						eos_token_id=UpperCamelCase_ ,						**UpperCamelCase_       )

      __magic_name__     =      vocab_size
      __magic_name__     =      max_position_embeddings
      __magic_name__     =      hidden_size
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      hidden_dropout_prob
      __magic_name__     =      initializer_range
      __magic_name__     =      type_vocab_size
      __magic_name__     =      layer_norm_eps
      __magic_name__     =      use_tpu_fourier_optimizations
      __magic_name__     =      tpu_short_seq_length

