


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 				lowerCamelCase__       (	UpperCamelCase__):
     SCREAMING_SNAKE_CASE__				      =      (EulerAncestralDiscreteScheduler,)
     SCREAMING_SNAKE_CASE__				      =      10





     def 					__A (self		,					**UpperCAmelCase	)					->       Optional[Any]:
      _lowercase											={
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**__lowerCamelCase	)
      return config





     def 					__A (self	)					->       Optional[Any]:


      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=__lowerCamelCase	)





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


      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]		,					[0.0002, 0.002, 0.02]	):
       self.check_over_configs(beta_start=__lowerCamelCase		,					beta_end=__lowerCamelCase	)





     def 					__A (self	)					->       Any:


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





     def 					__A (self	)					->       int:


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





     def 					__A (self	)					->       Any:
      _lowercase											=self.scheduler_classes[0]
      _lowercase											=self.get_scheduler_config()
      _lowercase											=scheduler_class(**__lowerCamelCase	)

      scheduler.set_timesteps(self.num_inference_steps	)

      _lowercase											=torch.manual_seed(0	)

      _lowercase											=self.dummy_model()
      _lowercase											=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _lowercase											=sample.to(__lowerCamelCase	)

      for i, t in enumerate(scheduler.timesteps	):
       _lowercase											=scheduler.scale_model_input(__lowerCamelCase		,					__lowerCamelCase	)

       _lowercase											=model(__lowerCamelCase		,					__lowerCamelCase	)

       _lowercase											=scheduler.step(__lowerCamelCase		,					__lowerCamelCase		,					__lowerCamelCase		,					generator=__lowerCamelCase	)
       _lowercase											=output.prev_sample

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

      assert abs(result_sum.item() - 152.3192	) < 1e-2
      assert abs(result_mean.item() - 0.1983	) < 1e-3





     def 					__A (self	)					->       Optional[Any]:
      _lowercase											=self.scheduler_classes[0]
      _lowercase											=self.get_scheduler_config(prediction_type='''v_prediction'''	)
      _lowercase											=scheduler_class(**__lowerCamelCase	)

      scheduler.set_timesteps(self.num_inference_steps	)

      _lowercase											=torch.manual_seed(0	)

      _lowercase											=self.dummy_model()
      _lowercase											=self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowercase											=sample.to(__lowerCamelCase	)

      for i, t in enumerate(scheduler.timesteps	):
       _lowercase											=scheduler.scale_model_input(__lowerCamelCase		,					__lowerCamelCase	)

       _lowercase											=model(__lowerCamelCase		,					__lowerCamelCase	)

       _lowercase											=scheduler.step(__lowerCamelCase		,					__lowerCamelCase		,					__lowerCamelCase		,					generator=__lowerCamelCase	)
       _lowercase											=output.prev_sample

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

      assert abs(result_sum.item() - 108.4439	) < 1e-2
      assert abs(result_mean.item() - 0.1412	) < 1e-3





     def 					__A (self	)					->       Dict:
      _lowercase											=self.scheduler_classes[0]
      _lowercase											=self.get_scheduler_config()
      _lowercase											=scheduler_class(**__lowerCamelCase	)

      scheduler.set_timesteps(self.num_inference_steps		,					device=__lowerCamelCase	)
      _lowercase											=torch.manual_seed(0	)

      _lowercase											=self.dummy_model()
      _lowercase											=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _lowercase											=sample.to(__lowerCamelCase	)

      for t in scheduler.timesteps:
       _lowercase											=scheduler.scale_model_input(__lowerCamelCase		,					__lowerCamelCase	)

       _lowercase											=model(__lowerCamelCase		,					__lowerCamelCase	)

       _lowercase											=scheduler.step(__lowerCamelCase		,					__lowerCamelCase		,					__lowerCamelCase		,					generator=__lowerCamelCase	)
       _lowercase											=output.prev_sample

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

      assert abs(result_sum.item() - 152.3192	) < 1e-2
      assert abs(result_mean.item() - 0.1983	) < 1e-3





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


UpperCAmelCase__            =							logging.get_logger(__name__)

UpperCAmelCase__            =							{
    '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}







class 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''qdqbert'''





     def __init__(self		,					UpperCAmelCase=3_0_5_2_2		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=2		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1e-12		,					UpperCAmelCase=True		,					UpperCAmelCase=1		,					UpperCAmelCase=0		,					UpperCAmelCase=2		,					**UpperCAmelCase		,					)					->       str:
      super().__init__(pad_token_id=UpperCAmelCase		,					bos_token_id=UpperCAmelCase		,					eos_token_id=UpperCAmelCase		,					**UpperCAmelCase	)

      _lowercase											=vocab_size
      _lowercase											=max_position_embeddings
      _lowercase											=hidden_size
      _lowercase											=num_hidden_layers
      _lowercase											=num_attention_heads
      _lowercase											=intermediate_size
      _lowercase											=hidden_act
      _lowercase											=hidden_dropout_prob
      _lowercase											=attention_probs_dropout_prob
      _lowercase											=initializer_range
      _lowercase											=type_vocab_size
      _lowercase											=layer_norm_eps
      _lowercase											=use_cache

