



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 	lowerCamelCase__	(       __snake_case):



 """simple docstring"""

 a__					: List[Any] =  (EulerDiscreteScheduler,)
 a__					: str =  10

 def     snake_case_      (    self					:					int			,						**__lowerCAmelCase					:					Optional[int]      )   ->	Optional[int]:
    _A      =       {
        '''num_train_timesteps''': 11_00,
        '''beta_start''': 0.0001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
    }

    config.update(**__lowerCAmelCase      )
    return config

 def     snake_case_      (    self					:					int      )   ->	Tuple:






    for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=__lowerCAmelCase      )

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	List[Any]:






    for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,						[0.0002, 0.002, 0.02]      ):
       self.check_over_configs(beta_start=__lowerCAmelCase			,						beta_end=__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[Any]:






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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	int:






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

 def     snake_case_      (    self					:					Any      )   ->	Tuple:
    _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
    _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() - 10.0807      ) < 1E-2
    assert abs(result_mean.item() - 0.0131      ) < 1E-3

 def     snake_case_      (    self					:					Any      )   ->	Any:
    _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() - 0.0002      ) < 1E-2
    assert abs(result_mean.item() - 2.26_76E-06      ) < 1E-3

 def     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _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() - 10.0807      ) < 1E-2
    assert abs(result_mean.item() - 0.0131      ) < 1E-3

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[Any]:
    _A      =       self.scheduler_classes[0]
    _A      =       self.get_scheduler_config()
    _A      =       scheduler_class(**__lowerCAmelCase			,						use_karras_sigmas=__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() - 124.52_2994_9951_1719      ) < 1E-2
    assert abs(result_mean.item() - 0.1_6213_9326_3339_9963      ) < 1E-3





'''simple docstring'''




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


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

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

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




