
'''simple docstring'''
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 			__A					(			unittest.TestCase      ):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 lowerCamelCase												=(ScoreSdeVeScheduler,)
 lowerCamelCase												=()
 @property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  Dict       				=		4
   __A		:  Optional[int]       				=		3
   __A		:  Dict       				=		8
   __A		:  Optional[int]       				=		8

   __A		:  Optional[int]       				=		torch.rand((batch_size, num_channels, height, width)							)

   return sample
 @property
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  List[Any]       				=		4
   __A		:  Tuple       				=		3
   __A		:  List[str]       				=		8
   __A		:  Union[str, Any]       				=		8

   __A		:  Optional[int]       				=		batch_size * num_channels * height * width
   __A		:  List[Any]       				=		torch.arange(lowerCamelCase							)
   __A		:  Dict       				=		sample.reshape(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, lowerCamelCase							)
   __A		:  int       				=		sample / num_elems
   __A		:  Optional[int]       				=		sample.permute(3					, 0					, 1					, 2							)

   return sample
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   def model(lowerCamelCase      :     Tuple					, lowerCamelCase      :     Optional[Any]					, *lowerCamelCase      :     str							):
     return sample * t / (t + 1)

   return model
 def 		lowercase_( self      :     Optional[Any]					, **lowerCamelCase      :     List[Any]							):



   """simple docstring"""





   __A		:  List[Any]       				=		{
       """num_train_timesteps""": 20_00,
       """snr""": 0.15,
       """sigma_min""": 0.01,
       """sigma_max""": 13_48,
       """sampling_eps""": 1e-5,
   }

   config.update(**lowerCamelCase							)
   return config
 def 		lowercase_( self      :     str					, lowerCamelCase      :     str=0					, **lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		dict(self.forward_default_kwargs							)

   for scheduler_class in self.scheduler_classes:
     __A		:  Tuple       				=		self.dummy_sample
     __A		:  List[str]       				=		0.1 * sample

     __A		:  Union[str, Any]       				=		self.get_scheduler_config(**lowerCamelCase							)
     __A		:  List[str]       				=		scheduler_class(**lowerCamelCase							)

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(lowerCamelCase							)
       __A		:  Any       				=		scheduler_class.from_pretrained(lowerCamelCase							)

     __A		:  int       				=		scheduler.step_pred(
         lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample
     __A		:  List[str]       				=		new_scheduler.step_pred(
         lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

     __A		:  Dict       				=		scheduler.step_correct(lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample
     __A		:  Union[str, Any]       				=		new_scheduler.step_correct(
         lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler correction are not identical"
 def 		lowercase_( self      :     Tuple					, lowerCamelCase      :     int=0					, **lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  str       				=		dict(self.forward_default_kwargs							)
   kwargs.update(lowerCamelCase							)

   for scheduler_class in self.scheduler_classes:
     __A		:  List[Any]       				=		self.dummy_sample
     __A		:  int       				=		0.1 * sample

     __A		:  List[Any]       				=		self.get_scheduler_config()
     __A		:  int       				=		scheduler_class(**lowerCamelCase							)

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(lowerCamelCase							)
       __A		:  Optional[Any]       				=		scheduler_class.from_pretrained(lowerCamelCase							)

     __A		:  Tuple       				=		scheduler.step_pred(
         lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample
     __A		:  List[str]       				=		new_scheduler.step_pred(
         lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

     __A		:  Optional[Any]       				=		scheduler.step_correct(lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample
     __A		:  Any       				=		new_scheduler.step_correct(
         lowerCamelCase					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler correction are not identical"
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   for timesteps in [10, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=lowerCamelCase							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]					, [1, 1_00, 10_00]							):
     self.check_over_configs(sigma_min=lowerCamelCase					, sigma_max=lowerCamelCase							)
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   for t in [0.1, 0.5, 0.75]:
     self.check_over_forward(time_step=lowerCamelCase							)
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  Tuple       				=		dict(self.forward_default_kwargs							)

   __A		:  List[str]       				=		self.scheduler_classes[0]
   __A		:  Optional[int]       				=		self.get_scheduler_config()
   __A		:  List[str]       				=		scheduler_class(**lowerCamelCase							)

   __A		:  int       				=		3

   __A		:  Optional[Any]       				=		self.dummy_model()
   __A		:  Tuple       				=		self.dummy_sample_deter

   scheduler.set_sigmas(lowerCamelCase							)
   scheduler.set_timesteps(lowerCamelCase							)
   __A		:  List[str]       				=		torch.manual_seed(0							)

   for i, t in enumerate(scheduler.timesteps							):
     __A		:  Tuple       				=		scheduler.sigmas[i]

     for _ in range(scheduler.config.correct_steps							):
       with torch.no_grad():
         __A		:  str       				=		model(lowerCamelCase					, lowerCamelCase							)
       __A		:  Tuple       				=		scheduler.step_correct(lowerCamelCase					, lowerCamelCase					, generator=lowerCamelCase					, **lowerCamelCase							).prev_sample

     with torch.no_grad():
       __A		:  str       				=		model(lowerCamelCase					, lowerCamelCase							)

     __A		:  str       				=		scheduler.step_pred(lowerCamelCase					, lowerCamelCase					, lowerCamelCase					, generator=lowerCamelCase					, **lowerCamelCase							)
     __A				,       __A		:  List[str]       				=		output.prev_sample, output.prev_sample_mean

   __A		:  Optional[Any]       				=		torch.sum(torch.abs(lowerCamelCase							)							)
   __A		:  List[Any]       				=		torch.mean(torch.abs(lowerCamelCase							)							)

   assert np.isclose(result_sum.item()					, 14_372_758_528.0							)
   assert np.isclose(result_mean.item()					, 18_714_530.0							)






 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Any       				=		dict(self.forward_default_kwargs							)

   __A		:  List[str]       				=		kwargs.pop("""num_inference_steps"""					, lowerCamelCase							)

   for scheduler_class in self.scheduler_classes:
     __A		:  List[str]       				=		self.get_scheduler_config()
     __A		:  Optional[int]       				=		scheduler_class(**lowerCamelCase							)

     __A		:  List[str]       				=		self.dummy_sample
     __A		:  List[Any]       				=		0.1 * sample

     if num_inference_steps is not None and hasattr(lowerCamelCase					, """set_timesteps"""							):
       scheduler.set_timesteps(lowerCamelCase							)
     elif num_inference_steps is not None and not hasattr(lowerCamelCase					, """set_timesteps"""							):
       __A		:  Any       				=		num_inference_steps

     __A		:  Tuple       				=		scheduler.step_pred(lowerCamelCase					, 0					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample
     __A		:  Tuple       				=		scheduler.step_pred(lowerCamelCase					, 1					, lowerCamelCase					, generator=torch.manual_seed(0							)					, **lowerCamelCase							).prev_sample

     self.assertEqual(output_a.shape					, sample.shape							)
     self.assertEqual(output_a.shape					, output_a.shape							)





'''simple docstring'''
import copy

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


A__ :		str					=logging.get_logger(__name__)

A__ :		Optional[int]					={
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Tuple					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Union[str, Any]=10_24					, lowerCamelCase      :     List[str]=16					, lowerCamelCase      :     str=16					, lowerCamelCase      :     Union[str, Any]=4					, lowerCamelCase      :     Optional[Any]="gelu"					, lowerCamelCase      :     Union[str, Any]=1e-6					, **lowerCamelCase      :     Union[str, Any]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[str]       				=		hidden_size
   __A		:  Any       				=		image_size
   __A		:  Dict       				=		patch_size
   __A		:  Union[str, Any]       				=		image_size // patch_size
   __A		:  List[Any]       				=		mask_input_channels
   __A		:  str       				=		num_point_embeddings
   __A		:  Tuple       				=		hidden_act
   __A		:  List[str]       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     int=2_56					, lowerCamelCase      :     Tuple="relu"					, lowerCamelCase      :     Optional[Any]=20_48					, lowerCamelCase      :     int=2					, lowerCamelCase      :     Tuple=8					, lowerCamelCase      :     int=2					, lowerCamelCase      :     List[str]=3					, lowerCamelCase      :     Dict=3					, lowerCamelCase      :     Any=2_56					, lowerCamelCase      :     int=1e-6					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[Any]       				=		hidden_size
   __A		:  Union[str, Any]       				=		hidden_act
   __A		:  Tuple       				=		mlp_dim
   __A		:  Any       				=		num_hidden_layers
   __A		:  Dict       				=		num_attention_heads
   __A		:  Optional[Any]       				=		attention_downsample_rate
   __A		:  List[Any]       				=		num_multimask_outputs
   __A		:  Optional[Any]       				=		iou_head_depth
   __A		:  Optional[int]       				=		iou_head_hidden_dim
   __A		:  Any       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     List[str]=7_68					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Optional[Any]=12					, lowerCamelCase      :     Tuple=12					, lowerCamelCase      :     int=3					, lowerCamelCase      :     Dict=10_24					, lowerCamelCase      :     int=16					, lowerCamelCase      :     Any="gelu"					, lowerCamelCase      :     Tuple=1e-0_6					, lowerCamelCase      :     Optional[int]=0.0					, lowerCamelCase      :     List[str]=1e-1_0					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Union[str, Any]=4.0					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[Any]=True					, lowerCamelCase      :     str=14					, lowerCamelCase      :     str=[2, 5, 8, 11]					, lowerCamelCase      :     Dict=1_28					, lowerCamelCase      :     Any=None					, **lowerCamelCase      :     Optional[int]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)

   __A		:  Any       				=		hidden_size
   __A		:  Union[str, Any]       				=		output_channels
   __A		:  int       				=		num_hidden_layers
   __A		:  Any       				=		num_attention_heads
   __A		:  Tuple       				=		num_channels
   __A		:  Tuple       				=		image_size
   __A		:  Optional[Any]       				=		patch_size
   __A		:  List[str]       				=		hidden_act
   __A		:  Dict       				=		layer_norm_eps
   __A		:  Optional[int]       				=		attention_dropout
   __A		:  Dict       				=		initializer_range
   __A		:  Union[str, Any]       				=		qkv_bias
   __A		:  Optional[Any]       				=		mlp_ratio
   __A		:  Optional[int]       				=		use_abs_pos
   __A		:  int       				=		use_rel_pos
   __A		:  str       				=		window_size
   __A		:  Union[str, Any]       				=		global_attn_indexes
   __A		:  Dict       				=		num_pos_feats
   __A		:  int       				=		int(hidden_size * mlp_ratio							) if mlp_dim is None else mlp_dim







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''sam'''
 lowerCamelCase												=True
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Optional[Any]=None					, lowerCamelCase      :     List[Any]=0.02					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  str       				=		vision_config if vision_config is not None else {}
   __A		:  int       				=		prompt_encoder_config if prompt_encoder_config is not None else {}
   __A		:  Dict       				=		mask_decoder_config if mask_decoder_config is not None else {}

   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  List[str]       				=		vision_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		prompt_encoder_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		mask_decoder_config.to_dict()

   __A		:  Any       				=		SamVisionConfig(**lowerCamelCase							)
   __A		:  List[Any]       				=		SamPromptEncoderConfig(**lowerCamelCase							)
   __A		:  Union[str, Any]       				=		SamMaskDecoderConfig(**lowerCamelCase							)
   __A		:  Any       				=		initializer_range






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  str       				=		copy.deepcopy(self.__dict__							)
   __A		:  Dict       				=		self.vision_config.to_dict()
   __A		:  Any       				=		self.prompt_encoder_config.to_dict()
   __A		:  str       				=		self.mask_decoder_config.to_dict()
   __A		:  Optional[int]       				=		self.__class__.model_type
   return output



