



import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler





class 		UpperCAmelCase    (    unittest.TestCase					):
   # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
   SCREAMING_SNAKE_CASE__					      =  (ScoreSdeVeScheduler,)
   SCREAMING_SNAKE_CASE__					      =  ()





   @property
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	4
         _lowerCAmelCase			      =	3
         _lowerCAmelCase			      =	8
         _lowerCAmelCase			      =	8

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

         return sample





   @property
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	4
         _lowerCAmelCase			      =	3
         _lowerCAmelCase			      =	8
         _lowerCAmelCase			      =	8

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

         return sample





   def 						__lowerCAmelCase				(						self      ):
         def model(_lowerCAmelCase							,  _lowerCAmelCase							,  *_lowerCAmelCase      ):
               return sample * t / (t + 1)

         return model





   def 						__lowerCAmelCase				(						self							,  **_lowerCAmelCase      ):
         _lowerCAmelCase			      =	{
             '''num_train_timesteps''': 2_000,
             '''snr''': 0.15,
             '''sigma_min''': 0.01,
             '''sigma_max''': 1_348,
             '''sampling_eps''': 1E-5,
         }

         config.update(**_lowerCAmelCase      )
         return config





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase=0							,  **_lowerCAmelCase      ):
         _lowerCAmelCase			      =	dict(self.forward_default_kwargs      )

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

               _lowerCAmelCase			      =	self.get_scheduler_config(**_lowerCAmelCase      )
               _lowerCAmelCase			      =	scheduler_class(**_lowerCAmelCase      )

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

               _lowerCAmelCase			      =	scheduler.step_pred(
                   _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  generator=torch.manual_seed(0      )							,  **_lowerCAmelCase      ).prev_sample
               _lowerCAmelCase			      =	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"

               _lowerCAmelCase			      =	scheduler.step_correct(_lowerCAmelCase							,  _lowerCAmelCase							,  generator=torch.manual_seed(0      )							,  **_lowerCAmelCase      ).prev_sample
               _lowerCAmelCase			      =	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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase=0							,  **_lowerCAmelCase      ):
         _lowerCAmelCase			      =	dict(self.forward_default_kwargs      )
         kwargs.update(_lowerCAmelCase      )

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

               _lowerCAmelCase			      =	self.get_scheduler_config()
               _lowerCAmelCase			      =	scheduler_class(**_lowerCAmelCase      )

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

               _lowerCAmelCase			      =	scheduler.step_pred(
                   _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  generator=torch.manual_seed(0      )							,  **_lowerCAmelCase      ).prev_sample
               _lowerCAmelCase			      =	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"

               _lowerCAmelCase			      =	scheduler.step_correct(_lowerCAmelCase							,  _lowerCAmelCase							,  generator=torch.manual_seed(0      )							,  **_lowerCAmelCase      ).prev_sample
               _lowerCAmelCase			      =	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 						__lowerCAmelCase				(						self      ):
         for timesteps in [10, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self      ):
         for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]							,  [1, 100, 1_000]      ):
               self.check_over_configs(sigma_min=_lowerCAmelCase							,  sigma_max=_lowerCAmelCase      )





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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	dict(self.forward_default_kwargs      )

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

         _lowerCAmelCase			      =	3

         _lowerCAmelCase			      =	self.dummy_model()
         _lowerCAmelCase			      =	self.dummy_sample_deter

         scheduler.set_sigmas(_lowerCAmelCase      )
         scheduler.set_timesteps(_lowerCAmelCase      )
         _lowerCAmelCase			      =	torch.manual_seed(0      )

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

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

               with torch.no_grad():
                     _lowerCAmelCase			      =	model(_lowerCAmelCase							,  _lowerCAmelCase      )

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

         _lowerCAmelCase			      =	torch.sum(torch.abs(_lowerCAmelCase      )      )
         _lowerCAmelCase			      =	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 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	dict(self.forward_default_kwargs      )

         _lowerCAmelCase			      =	kwargs.pop('''num_inference_steps'''							,  _lowerCAmelCase      )

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

               _lowerCAmelCase			      =	self.dummy_sample
               _lowerCAmelCase			      =	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'''      ):
                     _lowerCAmelCase			      =	num_inference_steps

               _lowerCAmelCase			      =	scheduler.step_pred(_lowerCAmelCase							,  0							,  _lowerCAmelCase							,  generator=torch.manual_seed(0      )							,  **_lowerCAmelCase      ).prev_sample
               _lowerCAmelCase			      =	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      )




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 		UpperCAmelCase    (    snake_case_					):





   def __init__(						self							,  _lowerCAmelCase=256							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=16							,  _lowerCAmelCase=16							,  _lowerCAmelCase=4							,  _lowerCAmelCase="gelu"							,  _lowerCAmelCase=1E-6							,  **_lowerCAmelCase							,  ):
         super().__init__(**_lowerCAmelCase      )
         _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 		UpperCAmelCase    (    snake_case_					):





   def __init__(						self							,  _lowerCAmelCase=256							,  _lowerCAmelCase="relu"							,  _lowerCAmelCase=2_048							,  _lowerCAmelCase=2							,  _lowerCAmelCase=8							,  _lowerCAmelCase=2							,  _lowerCAmelCase=3							,  _lowerCAmelCase=3							,  _lowerCAmelCase=256							,  _lowerCAmelCase=1E-6							,  **_lowerCAmelCase							,  ):
         super().__init__(**_lowerCAmelCase      )
         _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 		UpperCAmelCase    (    snake_case_					):





   def __init__(						self							,  _lowerCAmelCase=768							,  _lowerCAmelCase=256							,  _lowerCAmelCase=12							,  _lowerCAmelCase=12							,  _lowerCAmelCase=3							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=16							,  _lowerCAmelCase="gelu"							,  _lowerCAmelCase=1E-06							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=1E-10							,  _lowerCAmelCase=True							,  _lowerCAmelCase=4.0							,  _lowerCAmelCase=True							,  _lowerCAmelCase=True							,  _lowerCAmelCase=14							,  _lowerCAmelCase=[2, 5, 8, 11]							,  _lowerCAmelCase=128							,  _lowerCAmelCase=None							,  **_lowerCAmelCase							,  ):
         super().__init__(**_lowerCAmelCase      )

         _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 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''sam'''
   SCREAMING_SNAKE_CASE__					      =  True





   def __init__(						self							,  _lowerCAmelCase=None							,  _lowerCAmelCase=None							,  _lowerCAmelCase=None							,  _lowerCAmelCase=0.02							,  **_lowerCAmelCase							,  ):
         super().__init__(**_lowerCAmelCase      )
         _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(_lowerCAmelCase							,  _lowerCAmelCase      ):
               _lowerCAmelCase			      =	vision_config.to_dict()
         if isinstance(_lowerCAmelCase							,  _lowerCAmelCase      ):
               _lowerCAmelCase			      =	prompt_encoder_config.to_dict()
         if isinstance(_lowerCAmelCase							,  _lowerCAmelCase      ):
               _lowerCAmelCase			      =	mask_decoder_config.to_dict()

         _lowerCAmelCase			      =	SamVisionConfig(**_lowerCAmelCase      )
         _lowerCAmelCase			      =	SamPromptEncoderConfig(**_lowerCAmelCase      )
         _lowerCAmelCase			      =	SamMaskDecoderConfig(**_lowerCAmelCase      )
         _lowerCAmelCase			      =	initializer_range





   def 						__lowerCAmelCase				(						self      ):
         _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