






"""simple docstring"""


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Any							=					(EulerAncestralDiscreteScheduler,)
   A_		:      Any							=					10


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			**_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					{
         '''num_train_timesteps''': 11_00,
         '''beta_start''': 0.00_01,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
     }

     config.update(**_lowerCamelCase    )
     return config


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01],			[0.00_02, 0.0_02, 0.02]    ):
       self.check_over_configs(beta_start=_lowerCamelCase,			beta_end=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     for schedule in ["linear", "scaled_linear"]:
       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					:	List[str]    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config()
     __A		     =					scheduler_class(**_lowerCamelCase    )

     scheduler.set_timesteps(self.num_inference_steps    )

     __A		     =					torch.manual_seed(0    )

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __A		     =					sample.to(_lowerCamelCase    )

     for i, t in enumerate(scheduler.timesteps    ):
       __A		     =					scheduler.scale_model_input(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			generator=_lowerCamelCase    )
       __A		     =					output.prev_sample

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_52.31_92    ) < 1e-2
     assert abs(result_mean.item() - 0.19_83    ) < 1e-3


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

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

     scheduler.set_timesteps(self.num_inference_steps    )

     __A		     =					torch.manual_seed(0    )

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter * scheduler.init_noise_sigma
     __A		     =					sample.to(_lowerCamelCase    )

     for i, t in enumerate(scheduler.timesteps    ):
       __A		     =					scheduler.scale_model_input(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			generator=_lowerCamelCase    )
       __A		     =					output.prev_sample

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_08.44_39    ) < 1e-2
     assert abs(result_mean.item() - 0.14_12    ) < 1e-3






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.scheduler_classes[0]
     __A		     =					self.get_scheduler_config()
     __A		     =					scheduler_class(**_lowerCamelCase    )

     scheduler.set_timesteps(self.num_inference_steps,			device=_lowerCamelCase    )
     __A		     =					torch.manual_seed(0    )

     __A		     =					self.dummy_model()
     __A		     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     __A		     =					sample.to(_lowerCamelCase    )

     for t in scheduler.timesteps:
       __A		     =					scheduler.scale_model_input(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					model(_lowerCamelCase,			_lowerCamelCase    )

       __A		     =					scheduler.step(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			generator=_lowerCamelCase    )
       __A		     =					output.prev_sample

     __A		     =					torch.sum(torch.abs(_lowerCamelCase    )    )
     __A		     =					torch.mean(torch.abs(_lowerCamelCase    )    )

     assert abs(result_sum.item() - 1_52.31_92    ) < 1e-2
     assert abs(result_mean.item() - 0.19_83    ) < 1e-3












"""simple docstring"""


from typing import List, Union

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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					"led"
   A_		:      Any							=					{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }


   def __init__(      self					:	Dict,			_lowerCamelCase					:	str=5_02_65,			_lowerCamelCase					:	List[Any]=1_63_84,			_lowerCamelCase					:	int=10_24,			_lowerCamelCase					:	List[Any]=12,			_lowerCamelCase					:	int=40_96,			_lowerCamelCase					:	str=16,			_lowerCamelCase					:	Dict=12,			_lowerCamelCase					:	Optional[int]=40_96,			_lowerCamelCase					:	Any=16,			_lowerCamelCase					:	int=0.0,			_lowerCamelCase					:	str=0.0,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Tuple="gelu",			_lowerCamelCase					:	Tuple=10_24,			_lowerCamelCase					:	Dict=0.1,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	Optional[int]=0.02,			_lowerCamelCase					:	List[str]=2,			_lowerCamelCase					:	Dict=0.0,			_lowerCamelCase					:	Dict=1,			_lowerCamelCase					:	str=0,			_lowerCamelCase					:	Union[str, Any]=2,			_lowerCamelCase					:	Union[List[int], int] = 5_12,			**_lowerCamelCase					:	Any,			):

     '''simple docstring'''
     __A		     =					vocab_size
     __A		     =					max_encoder_position_embeddings
     __A		     =					max_decoder_position_embeddings
     __A		     =					d_model
     __A		     =					encoder_ffn_dim
     __A		     =					encoder_layers
     __A		     =					encoder_attention_heads
     __A		     =					decoder_ffn_dim
     __A		     =					decoder_layers
     __A		     =					decoder_attention_heads
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					activation_dropout
     __A		     =					activation_function
     __A		     =					init_std
     __A		     =					encoder_layerdrop
     __A		     =					decoder_layerdrop
     __A		     =					classifier_dropout
     __A		     =					use_cache
     __A		     =					encoder_layers
     __A		     =					attention_window

     super().__init__(
         pad_token_id=_lowerCamelCase,			bos_token_id=_lowerCamelCase,			eos_token_id=_lowerCamelCase,			is_encoder_decoder=_lowerCamelCase,			decoder_start_token_id=_lowerCamelCase,			**_lowerCamelCase,			)




