





import copy

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


__lowercase				    =      logging.get_logger(__name__)

__lowercase				    =      {
    """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 	__snake_case					(      __SCREAMING_SNAKE_CASE ):






    '''simple docstring'''





    def __init__( self    :		Dict      ,    snake_case    :		Optional[Any]=256      ,    snake_case    :		List[str]=1_024      ,    snake_case    :		List[str]=16      ,    snake_case    :		Tuple=16      ,    snake_case    :		Tuple=4      ,    snake_case    :		Optional[Any]="gelu"      ,    snake_case    :		Tuple=1e-6      ,    **snake_case    :		Union[str, Any]      ,    ):

      """simple docstring"""





      super().__init__(**__UpperCamelCase				)
      __lowerCAmelCase								=					hidden_size
      __lowerCAmelCase								=					image_size
      __lowerCAmelCase								=					patch_size
      __lowerCAmelCase								=					image_size // patch_size
      __lowerCAmelCase								=					mask_input_channels
      __lowerCAmelCase								=					num_point_embeddings
      __lowerCAmelCase								=					hidden_act
      __lowerCAmelCase								=					layer_norm_eps


class 	__snake_case					(      __SCREAMING_SNAKE_CASE ):






    '''simple docstring'''





    def __init__( self    :		Any      ,    snake_case    :		int=256      ,    snake_case    :		Optional[Any]="relu"      ,    snake_case    :		Optional[int]=2_048      ,    snake_case    :		Dict=2      ,    snake_case    :		List[str]=8      ,    snake_case    :		Union[str, Any]=2      ,    snake_case    :		str=3      ,    snake_case    :		Tuple=3      ,    snake_case    :		Any=256      ,    snake_case    :		Optional[int]=1e-6      ,    **snake_case    :		str      ,    ):

      """simple docstring"""





      super().__init__(**__UpperCamelCase				)
      __lowerCAmelCase								=					hidden_size
      __lowerCAmelCase								=					hidden_act
      __lowerCAmelCase								=					mlp_dim
      __lowerCAmelCase								=					num_hidden_layers
      __lowerCAmelCase								=					num_attention_heads
      __lowerCAmelCase								=					attention_downsample_rate
      __lowerCAmelCase								=					num_multimask_outputs
      __lowerCAmelCase								=					iou_head_depth
      __lowerCAmelCase								=					iou_head_hidden_dim
      __lowerCAmelCase								=					layer_norm_eps


class 	__snake_case					(      __SCREAMING_SNAKE_CASE ):






    '''simple docstring'''





    def __init__( self    :		Optional[int]      ,    snake_case    :		Optional[int]=768      ,    snake_case    :		Tuple=256      ,    snake_case    :		Optional[int]=12      ,    snake_case    :		Optional[Any]=12      ,    snake_case    :		List[str]=3      ,    snake_case    :		Any=1_024      ,    snake_case    :		List[Any]=16      ,    snake_case    :		Any="gelu"      ,    snake_case    :		Optional[Any]=1e-06      ,    snake_case    :		Tuple=0.0      ,    snake_case    :		Union[str, Any]=1e-10      ,    snake_case    :		int=True      ,    snake_case    :		List[str]=4.0      ,    snake_case    :		Optional[int]=True      ,    snake_case    :		Optional[int]=True      ,    snake_case    :		List[Any]=14      ,    snake_case    :		int=[2, 5, 8, 11]      ,    snake_case    :		Any=128      ,    snake_case    :		Union[str, Any]=None      ,    **snake_case    :		List[str]      ,    ):

      """simple docstring"""





      super().__init__(**__UpperCamelCase				)

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


class 	__snake_case					(      __SCREAMING_SNAKE_CASE ):






    '''simple docstring'''
    _lowerCAmelCase  =						"""sam"""
    _lowerCAmelCase  =						True





    def __init__( self    :		Dict      ,    snake_case    :		int=None      ,    snake_case    :		List[Any]=None      ,    snake_case    :		str=None      ,    snake_case    :		Any=0.02      ,    **snake_case    :		Any      ,    ):

      """simple docstring"""





      super().__init__(**__UpperCamelCase				)
      __lowerCAmelCase								=					vision_config if vision_config is not None else {}
      __lowerCAmelCase								=					prompt_encoder_config if prompt_encoder_config is not None else {}
      __lowerCAmelCase								=					mask_decoder_config if mask_decoder_config is not None else {}

      if isinstance(__UpperCamelCase      ,    __UpperCamelCase				):
        __lowerCAmelCase								=					vision_config.to_dict()
      if isinstance(__UpperCamelCase      ,    __UpperCamelCase				):
        __lowerCAmelCase								=					prompt_encoder_config.to_dict()
      if isinstance(__UpperCamelCase      ,    __UpperCamelCase				):
        __lowerCAmelCase								=					mask_decoder_config.to_dict()

      __lowerCAmelCase								=					SamVisionConfig(**__UpperCamelCase				)
      __lowerCAmelCase								=					SamPromptEncoderConfig(**__UpperCamelCase				)
      __lowerCAmelCase								=					SamMaskDecoderConfig(**__UpperCamelCase				)
      __lowerCAmelCase								=					initializer_range





    def 						A		( self    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					copy.deepcopy(self.__dict__				)
      __lowerCAmelCase								=					self.vision_config.to_dict()
      __lowerCAmelCase								=					self.prompt_encoder_config.to_dict()
      __lowerCAmelCase								=					self.mask_decoder_config.to_dict()
      __lowerCAmelCase								=					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  )



