





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 		_lowerCAmelCase(		__A):

  """simple docstring"""







  lowerCamelCase__     	=       (DDIMScheduler,)
  lowerCamelCase__     	=       (("""eta""", 0.0), ("""num_inference_steps""", 50))

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							**UpperCAmelCase      )->   Optional[int]:
     __A    =  {
         'num_train_timesteps': 10_00,
         'beta_start': 0.0001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'clip_sample': True,
     }

     config.update(**UpperCamelCase__      )
     return config

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							**UpperCAmelCase      )->   List[Any]:
     __A    =  self.scheduler_classes[0]
     __A    =  self.get_scheduler_config(**UpperCamelCase__      )
     __A    =  scheduler_class(**UpperCamelCase__      )

     __A    =  10, 0.0

     __A    =  self.dummy_model()
     __A    =  self.dummy_sample_deter

     scheduler.set_timesteps(UpperCamelCase__      )

     for t in scheduler.timesteps:
        __A    =  model(UpperCamelCase__    ,							UpperCamelCase__      )
        __A    =  scheduler.step(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__      ).prev_sample

     return sample

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     for timesteps in [1_00, 5_00, 10_00]:
        self.check_over_configs(num_train_timesteps=UpperCamelCase__      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=UpperCamelCase__      )

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

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]      ):
        self.check_over_configs(beta_start=UpperCamelCase__    ,							beta_end=UpperCamelCase__      )

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

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

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

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

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase__      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     self.check_over_configs(thresholding=UpperCamelCase__      )
     for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=UpperCamelCase__    ,							prediction_type=UpperCamelCase__    ,							sample_max_value=UpperCamelCase__    ,							)

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

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     for t, num_inference_steps in zip([1, 10, 50]    ,							[10, 50, 5_00]      ):
        self.check_over_forward(time_step=UpperCamelCase__    ,							num_inference_steps=UpperCamelCase__      )

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

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

     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      ) - 0.14771      )      ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,							9_60      ) - 0.32460      )      ) < 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      ) - 0.00979      )      ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,							9_98      ) - 0.02      )      ) < 1e-5

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     __A    =  self.full_loop()

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

     assert abs(result_sum.item() - 1_72.00_67      ) < 1e-2
     assert abs(result_mean.item() - 0.223967      ) < 1e-3

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     __A    =  self.full_loop(prediction_type='''v_prediction'''      )

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

     assert abs(result_sum.item() - 52.5302      ) < 1e-2
     assert abs(result_mean.item() - 0.0684      ) < 1e-3

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     # We specify different beta, so that the first alpha is 0.99
     __A    =  self.full_loop(set_alpha_to_one=UpperCamelCase__    ,							beta_start=0.01      )
     __A    =  torch.sum(torch.abs(UpperCamelCase__      )      )
     __A    =  torch.mean(torch.abs(UpperCamelCase__      )      )

     assert abs(result_sum.item() - 1_49.82_95      ) < 1e-2
     assert abs(result_mean.item() - 0.1951      ) < 1e-3





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     # We specify different beta, so that the first alpha is 0.99
     __A    =  self.full_loop(set_alpha_to_one=UpperCamelCase__    ,							beta_start=0.01      )
     __A    =  torch.sum(torch.abs(UpperCamelCase__      )      )
     __A    =  torch.mean(torch.abs(UpperCamelCase__      )      )

     assert abs(result_sum.item() - 1_49.07_84      ) < 1e-2
     assert abs(result_mean.item() - 0.1941      ) < 1e-3









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


A										=      logging.get_logger(__name__)

A										=      {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					str					     =    """tvlt"""


		def __init__(    self			,     UpperCamelCase__=224			,     UpperCamelCase__=2048			,     UpperCamelCase__=128			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=3			,     UpperCamelCase__=1			,     UpperCamelCase__=8			,     UpperCamelCase__=768			,     UpperCamelCase__=12			,     UpperCamelCase__=12			,     UpperCamelCase__=3072			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.02			,     UpperCamelCase__=1E-6			,     UpperCamelCase__=True			,     UpperCamelCase__=False			,     UpperCamelCase__=16			,     UpperCamelCase__=512			,     UpperCamelCase__=8			,     UpperCamelCase__=2048			,     UpperCamelCase__=0.75			,     UpperCamelCase__=0.15			,     UpperCamelCase__="frame-level"			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__="classification"			,     **UpperCamelCase__			,     ):
						super().__init__(**UpperCamelCase__							)

						if audio_mask_type not in ("frame-level", "patch_level"):
										raise ValueError(
										    'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
										    F'''got {audio_mask_type}'''							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= image_size
						SCREAMING_SNAKE_CASE_ :						int    				= spectrogram_length
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= frequency_length
						SCREAMING_SNAKE_CASE_ :						Any    				= image_patch_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= audio_patch_size
						SCREAMING_SNAKE_CASE_ :						int    				= num_image_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_audio_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_frames

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_size
						SCREAMING_SNAKE_CASE_ :						str    				= num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= num_attention_heads
						SCREAMING_SNAKE_CASE_ :						str    				= intermediate_size
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= hidden_act
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_dropout_prob
						SCREAMING_SNAKE_CASE_ :						int    				= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Dict    				= initializer_range
						SCREAMING_SNAKE_CASE_ :						Dict    				= layer_norm_eps
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= qkv_bias
						SCREAMING_SNAKE_CASE_ :						List[str]    				= use_mean_pooling

						SCREAMING_SNAKE_CASE_ :						str    				= decoder_num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= decoder_hidden_size
						SCREAMING_SNAKE_CASE_ :						Any    				= decoder_num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= decoder_intermediate_size
						SCREAMING_SNAKE_CASE_ :						List[str]    				= pixel_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= audio_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= audio_mask_type

						SCREAMING_SNAKE_CASE_ :						Any    				= task_matching
						SCREAMING_SNAKE_CASE_ :						Any    				= task_mae
						SCREAMING_SNAKE_CASE_ :						int    				= loss_type