


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Optional[int]       					=			(DDIMParallelScheduler,)
 __lowercase			:							Any       					=			(("eta", 0.0), ("num_inference_steps", 50))
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__:       int        =       {
            'num_train_timesteps': 1_000,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
            'clip_sample': True,
        }

        config.update(**lowerCAmelCase__			)
        return config
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.scheduler_classes[0]
        lowercase__:       Dict        =       self.get_scheduler_config(**lowerCAmelCase__			)
        lowercase__:       List[str]        =       scheduler_class(**lowerCAmelCase__			)

        lowercase__      ,						lowercase__:       List[str]        =       10, 0.0

        lowercase__:       Tuple        =       self.dummy_model()
        lowercase__:       Optional[int]        =       self.dummy_sample_deter

        scheduler.set_timesteps(lowerCAmelCase__			)

        for t in scheduler.timesteps:
               lowercase__:       str        =       model(lowerCAmelCase__    ,			lowerCAmelCase__			)
               lowercase__:       Tuple        =       scheduler.step(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			).prev_sample

        return sample
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''




        for timesteps in [100, 500, 1_000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''




        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase__			)

        lowercase__:       int        =       self.scheduler_classes[0]
        lowercase__:       Optional[int]        =       self.get_scheduler_config(steps_offset=1			)
        lowercase__:       Any        =       scheduler_class(**lowerCAmelCase__			)
        scheduler.set_timesteps(5			)
        assert torch.equal(scheduler.timesteps    ,			torch.LongTensor([801, 601, 401, 201, 1]			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''




        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,			[0.0_0_2, 0.0_2, 0.2, 2]			):
               self.check_over_configs(beta_start=lowerCAmelCase__    ,			beta_end=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''




        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''




        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''




        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''




        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''




        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        self.check_over_configs(thresholding=lowerCAmelCase__			)
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=lowerCAmelCase__    ,			prediction_type=lowerCAmelCase__    ,			sample_max_value=lowerCAmelCase__    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''




        for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''




        for t, num_inference_steps in zip([1, 10, 50]    ,			[10, 50, 500]			):
               self.check_over_forward(time_step=lowerCAmelCase__    ,			num_inference_steps=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''




        for t, eta in zip([1, 10, 49]    ,			[0.0, 0.5, 1.0]			):
               self.check_over_forward(time_step=lowerCAmelCase__    ,			eta=lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       self.scheduler_classes[0]
        lowercase__:       Any        =       self.get_scheduler_config()
        lowercase__:       List[Any]        =       scheduler_class(**lowerCAmelCase__			)

        assert torch.sum(torch.abs(scheduler._get_variance(0    ,			0			) - 0.0			)			) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420    ,			400			) - 0.1_4_7_7_1			)			) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980    ,			960			) - 0.3_2_4_6_0			)			) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0    ,			0			) - 0.0			)			) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487    ,			486			) - 0.0_0_9_7_9			)			) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999    ,			998			) - 0.0_2			)			) < 1E-5
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.scheduler_classes[0]
        lowercase__:       str        =       self.get_scheduler_config()
        lowercase__:       Union[str, Any]        =       scheduler_class(**lowerCAmelCase__			)

        lowercase__      ,						lowercase__:       Optional[int]        =       10, 0.0
        scheduler.set_timesteps(lowerCAmelCase__			)

        lowercase__:       Any        =       self.dummy_model()
        lowercase__:       Dict        =       self.dummy_sample_deter
        lowercase__:       List[str]        =       self.dummy_sample_deter + 0.1
        lowercase__:       int        =       self.dummy_sample_deter - 0.1

        lowercase__:       Any        =       samplea.shape[0]
        lowercase__:       List[Any]        =       torch.stack([samplea, samplea, samplea]    ,			dim=0			)
        lowercase__:       int        =       torch.arange(lowerCAmelCase__			)[0:3, None].repeat(1    ,			lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       model(samples.flatten(0    ,			1			)    ,			timesteps.flatten(0    ,			1			)			)
        lowercase__:       Dict        =       scheduler.batch_step_no_noise(lowerCAmelCase__    ,			timesteps.flatten(0    ,			1			)    ,			samples.flatten(0    ,			1			)    ,			lowerCAmelCase__			)

        lowercase__:       Any        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       Tuple        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 1_1_4_7.7_9_0_4			) < 1E-2
        assert abs(result_mean.item() - 0.4_9_8_2			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       int        =       self.full_loop()

        lowercase__:       Optional[int]        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       Optional[int]        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1E-2
        assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Dict        =       self.full_loop(prediction_type='v_prediction'			)

        lowercase__:       List[str]        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       Optional[int]        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1E-2
        assert abs(result_mean.item() - 0.0_6_8_4			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       self.full_loop(set_alpha_to_one=lowerCAmelCase__    ,			beta_start=0.0_1			)
        lowercase__:       str        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       Any        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1E-2
        assert abs(result_mean.item() - 0.1_9_5_1			) < 1E-3
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       Any        =       self.full_loop(set_alpha_to_one=lowerCAmelCase__    ,			beta_start=0.0_1			)
        lowercase__:       Union[str, Any]        =       torch.sum(torch.abs(lowerCAmelCase__			)			)
        lowercase__:       str        =       torch.mean(torch.abs(lowerCAmelCase__			)			)

        assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1E-2
        assert abs(result_mean.item() - 0.1_9_4_1			) < 1E-3





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


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			'pegasus'
 __lowercase			:							Union[str, Any]       					=			['past_key_values']
 __lowercase			:							Any       					=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(       self    ,			lowerCAmelCase__=50_265    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			lowerCAmelCase__=1    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       vocab_size
        lowercase__:       Optional[int]        =       max_position_embeddings
        lowercase__:       List[str]        =       d_model
        lowercase__:       Optional[Any]        =       encoder_ffn_dim
        lowercase__:       Optional[Any]        =       encoder_layers
        lowercase__:       Union[str, Any]        =       encoder_attention_heads
        lowercase__:       Optional[int]        =       decoder_ffn_dim
        lowercase__:       Tuple        =       decoder_layers
        lowercase__:       Union[str, Any]        =       decoder_attention_heads
        lowercase__:       Dict        =       dropout
        lowercase__:       List[str]        =       attention_dropout
        lowercase__:       List[str]        =       activation_dropout
        lowercase__:       Optional[int]        =       activation_function
        lowercase__:       Dict        =       init_std
        lowercase__:       Optional[Any]        =       encoder_layerdrop
        lowercase__:       List[str]        =       decoder_layerdrop
        lowercase__:       Union[str, Any]        =       use_cache
        lowercase__:       Any        =       encoder_layers
        lowercase__:       List[str]        =       scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			is_encoder_decoder=lowerCAmelCase__    ,			decoder_start_token_id=lowerCAmelCase__    ,			forced_eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.encoder_attention_heads
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.d_model


