





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class        snake_case_		(			lowercase__):
  lowerCamelCase    :List[str]						=     (DDPMScheduler,)







  def 			__lowercase   (		self			,     **__lowercase   )				->   Tuple:
    lowerCamelCase   : List[Any]								={
        "num_train_timesteps": 1_0_0_0,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
        "variance_type": "fixed_small",
        "clip_sample": True,
    }

    config.update(**__lowerCamelCase   )
    return config







  def 			__lowercase   (		self   )				->   Dict:



    for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
      self.check_over_configs(num_train_timesteps=__lowerCamelCase   )







  def 			__lowercase   (		self   )				->   Dict:



    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 			__lowercase   (		self   )				->   Optional[Any]:



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







  def 			__lowercase   (		self   )				->   Tuple:



    for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=__lowerCamelCase   )







  def 			__lowercase   (		self   )				->   Dict:



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







  def 			__lowercase   (		self   )				->   Optional[int]:
    self.check_over_configs(thresholding=__lowerCamelCase   )
    for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(
            thresholding=__lowerCamelCase			,     prediction_type=__lowerCamelCase			,     sample_max_value=__lowerCamelCase			,     )







  def 			__lowercase   (		self   )				->   int:



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







  def 			__lowercase   (		self   )				->   List[str]:



    for t in [0, 5_0_0, 9_9_9]:
      self.check_over_forward(time_step=__lowerCamelCase   )







  def 			__lowercase   (		self   )				->   int:
    lowerCamelCase   : Tuple								=self.scheduler_classes[0]
    lowerCamelCase   : Optional[int]								=self.get_scheduler_config()
    lowerCamelCase   : int								=scheduler_class(**__lowerCamelCase   )

    assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(4_8_7   ) - 0.0_0_9_7_9   )   ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(9_9_9   ) - 0.0_2   )   ) < 1e-5







  def 			__lowercase   (		self   )				->   List[Any]:
    lowerCamelCase   : List[str]								=self.scheduler_classes[0]
    lowerCamelCase   : Dict								=self.get_scheduler_config()
    lowerCamelCase   : Union[str, Any]								=scheduler_class(**__lowerCamelCase   )

    lowerCamelCase   : str								=len(__lowerCamelCase   )

    lowerCamelCase   : List[Any]								=self.dummy_model()
    lowerCamelCase   : Any								=self.dummy_sample_deter
    lowerCamelCase   : Any								=torch.manual_seed(0   )

    for t in reversed(range(__lowerCamelCase   )   ):
      # 1. predict noise residual
      lowerCamelCase   : Union[str, Any]								=model(__lowerCamelCase			,     __lowerCamelCase   )

      # 2. predict previous mean of sample x_t-1
      lowerCamelCase   : str								=scheduler.step(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     generator=__lowerCamelCase   ).prev_sample

      # if t > 0:
      #     noise = self.dummy_sample_deter
      #     variance = scheduler.get_variance(t) ** (0.5) * noise
      #
      # sample = pred_prev_sample + variance
      lowerCamelCase   : List[Any]								=pred_prev_sample

    lowerCamelCase   : Dict								=torch.sum(torch.abs(__lowerCamelCase   )   )
    lowerCamelCase   : int								=torch.mean(torch.abs(__lowerCamelCase   )   )

    assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
    assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







  def 			__lowercase   (		self   )				->   Optional[Any]:
    lowerCamelCase   : Any								=self.scheduler_classes[0]
    lowerCamelCase   : int								=self.get_scheduler_config(prediction_type='''v_prediction'''   )
    lowerCamelCase   : Union[str, Any]								=scheduler_class(**__lowerCamelCase   )

    lowerCamelCase   : Union[str, Any]								=len(__lowerCamelCase   )

    lowerCamelCase   : Union[str, Any]								=self.dummy_model()
    lowerCamelCase   : List[Any]								=self.dummy_sample_deter
    lowerCamelCase   : int								=torch.manual_seed(0   )

    for t in reversed(range(__lowerCamelCase   )   ):
      # 1. predict noise residual
      lowerCamelCase   : Optional[Any]								=model(__lowerCamelCase			,     __lowerCamelCase   )

      # 2. predict previous mean of sample x_t-1
      lowerCamelCase   : Dict								=scheduler.step(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     generator=__lowerCamelCase   ).prev_sample

      # if t > 0:
      #     noise = self.dummy_sample_deter
      #     variance = scheduler.get_variance(t) ** (0.5) * noise
      #
      # sample = pred_prev_sample + variance
      lowerCamelCase   : Union[str, Any]								=pred_prev_sample

    lowerCamelCase   : Any								=torch.sum(torch.abs(__lowerCamelCase   )   )
    lowerCamelCase   : Union[str, Any]								=torch.mean(torch.abs(__lowerCamelCase   )   )

    assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
    assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







  def 			__lowercase   (		self   )				->   Optional[Any]:
    lowerCamelCase   : Union[str, Any]								=self.scheduler_classes[0]
    lowerCamelCase   : List[str]								=self.get_scheduler_config()
    lowerCamelCase   : List[str]								=scheduler_class(**__lowerCamelCase   )

    lowerCamelCase   : Optional[Any]								=[1_0_0, 8_7, 5_0, 1, 0]

    scheduler.set_timesteps(timesteps=__lowerCamelCase   )

    lowerCamelCase   : Union[str, Any]								=scheduler.timesteps

    for i, timestep in enumerate(__lowerCamelCase   ):
      if i == len(__lowerCamelCase   ) - 1:
        lowerCamelCase   : Dict								=-1
      else:
        lowerCamelCase   : Tuple								=timesteps[i + 1]

      lowerCamelCase   : Dict								=scheduler.previous_timestep(__lowerCamelCase   )
      lowerCamelCase   : Dict								=prev_t.item()

      self.assertEqual(__lowerCamelCase			,     __lowerCamelCase   )







  def 			__lowercase   (		self   )				->   int:
    lowerCamelCase   : List[Any]								=self.scheduler_classes[0]
    lowerCamelCase   : Union[str, Any]								=self.get_scheduler_config()
    lowerCamelCase   : List[Any]								=scheduler_class(**__lowerCamelCase   )

    lowerCamelCase   : List[str]								=[1_0_0, 8_7, 5_0, 5_1, 0]

    with self.assertRaises(__lowerCamelCase			,     msg='''`custom_timesteps` must be in descending order.'''   ):
      scheduler.set_timesteps(timesteps=__lowerCamelCase   )







  def 			__lowercase   (		self   )				->   Optional[int]:
    lowerCamelCase   : Optional[int]								=self.scheduler_classes[0]
    lowerCamelCase   : Dict								=self.get_scheduler_config()
    lowerCamelCase   : Dict								=scheduler_class(**__lowerCamelCase   )

    lowerCamelCase   : Any								=[1_0_0, 8_7, 5_0, 1, 0]
    lowerCamelCase   : List[Any]								=len(__lowerCamelCase   )

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







  def 			__lowercase   (		self   )				->   List[str]:
    lowerCamelCase   : List[Any]								=self.scheduler_classes[0]
    lowerCamelCase   : Tuple								=self.get_scheduler_config()
    lowerCamelCase   : Dict								=scheduler_class(**__lowerCamelCase   )

    lowerCamelCase   : Optional[Any]								=[scheduler.config.num_train_timesteps]

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








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


snake_case_							=			logging.get_logger(__name__)

snake_case_							=			{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}
class        snake_case_		(			_A):
		lowerCamelCase    :Union[str, Any]						=     "timesformer"







		def __init__(		self			,     __lowercase=2_2_4			,     __lowercase=1_6			,     __lowercase=3			,     __lowercase=8			,     __lowercase=7_6_8			,     __lowercase=1_2			,     __lowercase=1_2			,     __lowercase=3_0_7_2			,     __lowercase="gelu"			,     __lowercase=0.0			,     __lowercase=0.0			,     __lowercase=0.0_2			,     __lowercase=1e-6			,     __lowercase=True			,     __lowercase="divided_space_time"			,     __lowercase=0			,     **__lowercase			,     )				->   List[Any]:
				super().__init__(**__lowercase   )

				lowerCamelCase   : int								=image_size
				lowerCamelCase   : List[str]								=patch_size
				lowerCamelCase   : Union[str, Any]								=num_channels
				lowerCamelCase   : str								=num_frames

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

				lowerCamelCase   : Tuple								=attention_type
				lowerCamelCase   : List[Any]								=drop_path_rate

