import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class __snake_case							(		_lowerCAmelCase						):
   lowerCAmelCase_								=				(DDIMScheduler,)
   lowerCAmelCase_								=				(("eta", 0.0), ("num_inference_steps", 50))




   def       __a		(       self     :							Any			,      **_lowercase     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {
         """num_train_timesteps""": 10_00,
         """beta_start""": 0.00_01,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
         """clip_sample""": True,
     }

     config.update(**_lowerCamelCase )
     return config




   def       __a		(       self     :							Optional[Any]			,      **_lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__     				= self.get_scheduler_config(**_lowerCamelCase )
     SCREAMING_SNAKE_CASE__     				= scheduler_class(**_lowerCamelCase )

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= 10, 0.0

     SCREAMING_SNAKE_CASE__     				= self.dummy_model()
     SCREAMING_SNAKE_CASE__     				= self.dummy_sample_deter

     scheduler.set_timesteps(_lowerCamelCase )

     for t in scheduler.timesteps:
       SCREAMING_SNAKE_CASE__     				= model(_lowerCamelCase			,      _lowerCamelCase )
       SCREAMING_SNAKE_CASE__     				= scheduler.step(_lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase			,      _lowerCamelCase ).prev_sample

     return sample




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     for timesteps in [1_00, 5_00, 10_00]:
       self.check_over_configs(num_train_timesteps=_lowerCamelCase )




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__     				= self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__     				= self.get_scheduler_config(steps_offset=1 )
     SCREAMING_SNAKE_CASE__     				= scheduler_class(**_lowerCamelCase )
     scheduler.set_timesteps(5 )
     assert torch.equal(scheduler.timesteps			,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,      [0.0_02, 0.02, 0.2, 2] ):
       self.check_over_configs(beta_start=_lowerCamelCase			,      beta_end=_lowerCamelCase )




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



     """simple docstring"""






     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=_lowerCamelCase )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






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




   def       __a		(       self     :							int ):



     """simple docstring"""






     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=_lowerCamelCase )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     for timestep_spacing in ["trailing", "leading"]:
       self.check_over_configs(timestep_spacing=_lowerCamelCase )




   def       __a		(       self     :							str ):



     """simple docstring"""






     for rescale_betas_zero_snr in [True, False]:
       self.check_over_configs(rescale_betas_zero_snr=_lowerCamelCase )




   def       __a		(       self     :							List[str] ):



     """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       __a		(       self     :							List[str] ):



     """simple docstring"""






     for t in [1, 10, 49]:
       self.check_over_forward(time_step=_lowerCamelCase )




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



     """simple docstring"""






     for t, num_inference_steps in zip([1, 10, 50]			,      [10, 50, 5_00] ):
       self.check_over_forward(time_step=_lowerCamelCase			,      num_inference_steps=_lowerCamelCase )




   def       __a		(       self     :							str ):



     """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       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE__     				= self.get_scheduler_config()
     SCREAMING_SNAKE_CASE__     				= 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(4_20			,      4_00 ) - 0.1_47_71 ) ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80			,      9_60 ) - 0.3_24_60 ) ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(0			,      0 ) - 0.0 ) ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_87			,      4_86 ) - 0.0_09_79 ) ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99			,      9_98 ) - 0.02 ) ) < 1E-5




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.full_loop()

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

     assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.full_loop(prediction_type="""v_prediction""" )

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

     assert abs(result_sum.item() - 52.53_02 ) < 1E-2
     assert abs(result_mean.item() - 0.06_84 ) < 1E-3




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.full_loop(set_alpha_to_one=_lowerCamelCase			,      beta_start=0.01 )
     SCREAMING_SNAKE_CASE__     				= torch.sum(torch.abs(_lowerCamelCase ) )
     SCREAMING_SNAKE_CASE__     				= torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
     assert abs(result_mean.item() - 0.19_51 ) < 1E-3




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.full_loop(set_alpha_to_one=_lowerCamelCase			,      beta_start=0.01 )
     SCREAMING_SNAKE_CASE__     				= torch.sum(torch.abs(_lowerCamelCase ) )
     SCREAMING_SNAKE_CASE__     				= torch.mean(torch.abs(_lowerCamelCase ) )

     assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
     assert abs(result_mean.item() - 0.19_41 ) < 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,			)




