






import copy

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


__UpperCAmelCase      =				logging.get_logger(__name__)

__UpperCAmelCase      =				{
    "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_(		lowercase__	):



     """simple docstring"""



     def __init__(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	int=2_5_6  ,  lowerCAmelCase__					:	str=1_0_2_4  ,  lowerCAmelCase__					:	Optional[Any]=1_6  ,  lowerCAmelCase__					:	Tuple=1_6  ,  lowerCAmelCase__					:	Tuple=4  ,  lowerCAmelCase__					:	int="gelu"  ,  lowerCAmelCase__					:	int=1e-6  ,  **lowerCAmelCase__					:	List[Any]  ,  )							->	Any:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		hidden_size
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		patch_size
      SCREAMING_SNAKE_CASE				    =		image_size // patch_size
      SCREAMING_SNAKE_CASE				    =		mask_input_channels
      SCREAMING_SNAKE_CASE				    =		num_point_embeddings
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		layer_norm_eps

class 	a_(		lowercase__	):



     """simple docstring"""



     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	Union[str, Any]=2_5_6  ,  lowerCAmelCase__					:	str="relu"  ,  lowerCAmelCase__					:	Optional[Any]=2_0_4_8  ,  lowerCAmelCase__					:	Any=2  ,  lowerCAmelCase__					:	int=8  ,  lowerCAmelCase__					:	Optional[int]=2  ,  lowerCAmelCase__					:	str=3  ,  lowerCAmelCase__					:	str=3  ,  lowerCAmelCase__					:	List[str]=2_5_6  ,  lowerCAmelCase__					:	Optional[int]=1e-6  ,  **lowerCAmelCase__					:	List[Any]  ,  )							->	Dict:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		hidden_size
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		mlp_dim
      SCREAMING_SNAKE_CASE				    =		num_hidden_layers
      SCREAMING_SNAKE_CASE				    =		num_attention_heads
      SCREAMING_SNAKE_CASE				    =		attention_downsample_rate
      SCREAMING_SNAKE_CASE				    =		num_multimask_outputs
      SCREAMING_SNAKE_CASE				    =		iou_head_depth
      SCREAMING_SNAKE_CASE				    =		iou_head_hidden_dim
      SCREAMING_SNAKE_CASE				    =		layer_norm_eps

class 	a_(		lowercase__	):



     """simple docstring"""



     def __init__(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[int]=7_6_8  ,  lowerCAmelCase__					:	Dict=2_5_6  ,  lowerCAmelCase__					:	Optional[Any]=1_2  ,  lowerCAmelCase__					:	List[str]=1_2  ,  lowerCAmelCase__					:	int=3  ,  lowerCAmelCase__					:	int=1_0_2_4  ,  lowerCAmelCase__					:	Dict=1_6  ,  lowerCAmelCase__					:	Dict="gelu"  ,  lowerCAmelCase__					:	List[str]=1e-06  ,  lowerCAmelCase__					:	Tuple=0.0  ,  lowerCAmelCase__					:	Optional[int]=1e-10  ,  lowerCAmelCase__					:	Dict=True  ,  lowerCAmelCase__					:	List[Any]=4.0  ,  lowerCAmelCase__					:	List[Any]=True  ,  lowerCAmelCase__					:	Union[str, Any]=True  ,  lowerCAmelCase__					:	int=1_4  ,  lowerCAmelCase__					:	int=[2, 5, 8, 1_1]  ,  lowerCAmelCase__					:	Optional[int]=1_2_8  ,  lowerCAmelCase__					:	List[Any]=None  ,  **lowerCAmelCase__					:	Optional[Any]  ,  )							->	int:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		hidden_size
      SCREAMING_SNAKE_CASE				    =		output_channels
      SCREAMING_SNAKE_CASE				    =		num_hidden_layers
      SCREAMING_SNAKE_CASE				    =		num_attention_heads
      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		patch_size
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		layer_norm_eps
      SCREAMING_SNAKE_CASE				    =		attention_dropout
      SCREAMING_SNAKE_CASE				    =		initializer_range
      SCREAMING_SNAKE_CASE				    =		qkv_bias
      SCREAMING_SNAKE_CASE				    =		mlp_ratio
      SCREAMING_SNAKE_CASE				    =		use_abs_pos
      SCREAMING_SNAKE_CASE				    =		use_rel_pos
      SCREAMING_SNAKE_CASE				    =		window_size
      SCREAMING_SNAKE_CASE				    =		global_attn_indexes
      SCREAMING_SNAKE_CASE				    =		num_pos_feats
      SCREAMING_SNAKE_CASE				    =		int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim

class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				Optional[Any]  					='''sam'''
     __snake_case		:				Optional[Any]  					=True





     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	Any=None  ,  lowerCAmelCase__					:	Union[str, Any]=0.02  ,  **lowerCAmelCase__					:	Optional[int]  ,  )							->	List[str]:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		vision_config if vision_config is not None else {}
      SCREAMING_SNAKE_CASE				    =		prompt_encoder_config if prompt_encoder_config is not None else {}
      SCREAMING_SNAKE_CASE				    =		mask_decoder_config if mask_decoder_config is not None else {}

      if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		vision_config.to_dict()
      if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		prompt_encoder_config.to_dict()
      if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
       SCREAMING_SNAKE_CASE				    =		mask_decoder_config.to_dict()

      SCREAMING_SNAKE_CASE				    =		SamVisionConfig(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		SamPromptEncoderConfig(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		SamMaskDecoderConfig(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		initializer_range





     def     __UpperCamelCase						(		self					:	Any)							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		copy.deepcopy(self.__dict__)
      SCREAMING_SNAKE_CASE				    =		self.vision_config.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.prompt_encoder_config.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.mask_decoder_config.to_dict()
      SCREAMING_SNAKE_CASE				    =		self.__class__.model_type
      return output






import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 		lowercase_							(							unittest.TestCase					):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 A__    :					int    =     (ScoreSdeVeScheduler,)
 A__    :					Union[str, Any]    =     ()

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							torch.rand((batch_size, num_channels, height, width)  )

        return sample

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							4
        UpperCamelCase_											=							3
        UpperCamelCase_											=							8
        UpperCamelCase_											=							8

        UpperCamelCase_											=							batch_size * num_channels * height * width
        UpperCamelCase_											=							torch.arange(__UpperCamelCase  )
        UpperCamelCase_											=							sample.reshape(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  )
        UpperCamelCase_											=							sample / num_elems
        UpperCamelCase_											=							sample.permute(3  ,						0  ,						1  ,						2  )

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        def model(__UpperCamelCase  ,						__UpperCamelCase  ,						*__UpperCamelCase  ):
               return sample * t / (t + 1)

        return model

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 2_0_0_0,
            """snr""": 0.15,
            """sigma_min""": 0.01,
            """sigma_max""": 1_3_4_8,
            """sampling_eps""": 1e-5,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        kwargs.update(__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_pred(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

               UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step_correct(
                   __UpperCamelCase  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [1_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,						[1, 1_0_0, 1_0_0_0]  ):
               self.check_over_configs(sigma_min=__UpperCamelCase  ,						sigma_max=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config()
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							3

        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter

        scheduler.set_sigmas(__UpperCamelCase  )
        scheduler.set_timesteps(__UpperCamelCase  )
        UpperCamelCase_											=							torch.manual_seed(0  )

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

               for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                             UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler.step_correct(__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               with torch.no_grad():
                      UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						generator=__UpperCamelCase  ,						**__UpperCamelCase  )
               UpperCamelCase_							,					UpperCamelCase_											=							output.prev_sample, output.prev_sample_mean

        UpperCamelCase_											=							torch.sum(torch.abs(__UpperCamelCase  )  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

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

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						0  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step_pred(__UpperCamelCase  ,						1  ,						__UpperCamelCase  ,						generator=torch.manual_seed(0  )  ,						**__UpperCamelCase  ).prev_sample

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



