



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


UpperCAmelCase   :							str							 =logging.get_logger(__name__)

UpperCAmelCase   :							Tuple							 ={
    """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       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						"""pegasus"""
				lowercase__   						=						["""past_key_values"""]
				lowercase__   						=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

				def __init__( self     , snake_case__=5_0265     , snake_case__=1024     , snake_case__=12     , snake_case__=4096     , snake_case__=16     , snake_case__=12     , snake_case__=4096     , snake_case__=16     , snake_case__=0.0     , snake_case__=0.0     , snake_case__=True     , snake_case__=True     , snake_case__="gelu"     , snake_case__=1024     , snake_case__=0.1     , snake_case__=0.0     , snake_case__=0.0     , snake_case__=0.02     , snake_case__=0     , snake_case__=False     , snake_case__=0     , snake_case__=1     , snake_case__=1     , **snake_case__     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   vocab_size
									UpperCamelCase_		  =   max_position_embeddings
									UpperCamelCase_		  =   d_model
									UpperCamelCase_		  =   encoder_ffn_dim
									UpperCamelCase_		  =   encoder_layers
									UpperCamelCase_		  =   encoder_attention_heads
									UpperCamelCase_		  =   decoder_ffn_dim
									UpperCamelCase_		  =   decoder_layers
									UpperCamelCase_		  =   decoder_attention_heads
									UpperCamelCase_		  =   dropout
									UpperCamelCase_		  =   attention_dropout
									UpperCamelCase_		  =   activation_dropout
									UpperCamelCase_		  =   activation_function
									UpperCamelCase_		  =   init_std
									UpperCamelCase_		  =   encoder_layerdrop
									UpperCamelCase_		  =   decoder_layerdrop
									UpperCamelCase_		  =   use_cache
									UpperCamelCase_		  =   encoder_layers
									UpperCamelCase_		  =   scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=snake_case__     , eos_token_id=snake_case__     , is_encoder_decoder=snake_case__     , decoder_start_token_id=snake_case__     , forced_eos_token_id=snake_case__     , **snake_case__     , )

				@property
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									return self.encoder_attention_heads

				@property
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									return self.d_model





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class       _lowercase   (a_      ):
				'''simple docstring'''







				lowercase__   						=						(CMStochasticIterativeScheduler,)
				lowercase__   						=						10

				def       _lowerCamelCase					( self     , **snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   {
									    "num_train_timesteps": 201,
									    "sigma_min": 0.002,
									    "sigma_max": 80.0,
									}

									config.update(**snake_case__    )
									return config

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   10

									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   self.scheduler_classes[0](**snake_case__    )

									scheduler.set_timesteps(snake_case__    )

									UpperCamelCase_		  =   scheduler.timesteps[0]
									UpperCamelCase_		  =   scheduler.timesteps[1]

									UpperCamelCase_		  =   self.dummy_sample
									UpperCamelCase_		  =   0.1 * sample

									UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__    ).prev_sample
									UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__    ).prev_sample

									self.assertEqual(output_a.shape     , sample.shape    )
									self.assertEqual(output_a.shape     , output_a.shape    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									for clip_denoised in [True, False]:
														self.check_over_configs(clip_denoised=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   1
									scheduler.set_timesteps(snake_case__    )
									UpperCamelCase_		  =   scheduler.timesteps

									UpperCamelCase_		  =   torch.manual_seed(0    )

									UpperCamelCase_		  =   self.dummy_model()
									UpperCamelCase_		  =   self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(snake_case__    ):
														# 1. scale model input
														UpperCamelCase_		  =   scheduler.scale_model_input(snake_case__     , snake_case__    )

														# 2. predict noise residual
														UpperCamelCase_		  =   model(snake_case__     , snake_case__    )

														# 3. predict previous sample x_t-1
														UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__     , generator=snake_case__    ).prev_sample

														UpperCamelCase_		  =   pred_prev_sample

									UpperCamelCase_		  =   torch.sum(torch.abs(snake_case__    )    )
									UpperCamelCase_		  =   torch.mean(torch.abs(snake_case__    )    )

									assert abs(result_sum.item() - 192.7_614    ) < 1e-2
									assert abs(result_mean.item() - 0.2_510    ) < 1e-3

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [106, 0]
									scheduler.set_timesteps(timesteps=snake_case__    )
									UpperCamelCase_		  =   scheduler.timesteps

									UpperCamelCase_		  =   torch.manual_seed(0    )

									UpperCamelCase_		  =   self.dummy_model()
									UpperCamelCase_		  =   self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
														# 1. scale model input
														UpperCamelCase_		  =   scheduler.scale_model_input(snake_case__     , snake_case__    )

														# 2. predict noise residual
														UpperCamelCase_		  =   model(snake_case__     , snake_case__    )

														# 3. predict previous sample x_t-1
														UpperCamelCase_		  =   scheduler.step(snake_case__     , snake_case__     , snake_case__     , generator=snake_case__    ).prev_sample

														UpperCamelCase_		  =   pred_prev_sample

									UpperCamelCase_		  =   torch.sum(torch.abs(snake_case__    )    )
									UpperCamelCase_		  =   torch.mean(torch.abs(snake_case__    )    )

									assert abs(result_sum.item() - 347.6_357    ) < 1e-2
									assert abs(result_mean.item() - 0.4_527    ) < 1e-3

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [39, 30, 12, 15, 0]

									with self.assertRaises(snake_case__     , msg="`timesteps` must be in descending order."    ):
														scheduler.set_timesteps(timesteps=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [39, 30, 12, 1, 0]
									UpperCamelCase_		  =   len(snake_case__    )

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

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.scheduler_classes[0]
									UpperCamelCase_		  =   self.get_scheduler_config()
									UpperCamelCase_		  =   scheduler_class(**snake_case__    )

									UpperCamelCase_		  =   [scheduler.config.num_train_timesteps]

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