




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class       SCREAMING_SNAKE_CASE_							(					unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					int			  =		(ScoreSdeVeScheduler,)
	__magic_name__      :					Union[str, Any]			  =		()






	@property
	def 				lowerCamelCase__    (				self		:	str  )  ->					Optional[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			4
						__UpperCamelCase				:  Dict								=			3
						__UpperCamelCase				:  Dict								=			8
						__UpperCamelCase				:  Tuple								=			8

						__UpperCamelCase				:  int								=			torch.rand((batch_size, num_channels, height, width)  )

						return sample






	@property
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					str:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			4
						__UpperCamelCase				:  List[Any]								=			3
						__UpperCamelCase				:  Dict								=			8
						__UpperCamelCase				:  List[str]								=			8

						__UpperCamelCase				:  Tuple								=			batch_size * num_channels * height * width
						__UpperCamelCase				:  Any								=			torch.arange(lowerCAmelCase  )
						__UpperCamelCase				:  Dict								=			sample.reshape(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase  )
						__UpperCamelCase				:  str								=			sample / num_elems
						__UpperCamelCase				:  List[str]								=			sample.permute(3				,    0				,    1				,    2  )

						return sample






	def 				lowerCamelCase__    (				self		:	int  )  ->					Any:







						"""simple docstring"""







						def model(lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	Any				,    *lowerCAmelCase		:	str  ):
											return sample * t / (t + 1)

						return model






	def 				lowerCamelCase__    (				self		:	Any				,    **lowerCAmelCase		:	int  )  ->					Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase				:  Union[str, Any]								=			{
						    """num_train_timesteps""": 2000,
						    """snr""": 0.15,
						    """sigma_min""": 0.01,
						    """sigma_max""": 1348,
						    """sampling_eps""": 1E-5,
						}

						config.update(**lowerCAmelCase  )
						return config






	def 				lowerCamelCase__    (				self		:	int				,    lowerCAmelCase		:	Optional[Any]=0				,    **lowerCAmelCase		:	int  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			dict(self.forward_default_kwargs  )

						for scheduler_class in self.scheduler_classes:
											__UpperCamelCase				:  Union[str, Any]								=			self.dummy_sample
											__UpperCamelCase				:  Union[str, Any]								=			0.1 * sample

											__UpperCamelCase				:  Optional[Any]								=			self.get_scheduler_config(**lowerCAmelCase  )
											__UpperCamelCase				:  str								=			scheduler_class(**lowerCAmelCase  )

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCAmelCase  )
																__UpperCamelCase				:  List[str]								=			scheduler_class.from_pretrained(lowerCAmelCase  )

											__UpperCamelCase				:  Any								=			scheduler.step_pred(
											    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  Optional[int]								=			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"

											__UpperCamelCase				:  List[str]								=			scheduler.step_correct(lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  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 				lowerCamelCase__    (				self		:	Optional[Any]				,    lowerCAmelCase		:	List[Any]=0				,    **lowerCAmelCase		:	List[str]  )  ->					Dict:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			dict(self.forward_default_kwargs  )
						kwargs.update(lowerCAmelCase  )

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

											__UpperCamelCase				:  Dict								=			self.get_scheduler_config()
											__UpperCamelCase				:  str								=			scheduler_class(**lowerCAmelCase  )

											with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCAmelCase  )
																__UpperCamelCase				:  Optional[int]								=			scheduler_class.from_pretrained(lowerCAmelCase  )

											__UpperCamelCase				:  Any								=			scheduler.step_pred(
											    lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  int								=			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"

											__UpperCamelCase				:  Union[str, Any]								=			scheduler.step_correct(lowerCAmelCase				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  List[str]								=			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		:	Any  )  ->					str:







						"""simple docstring"""







						for timesteps in [10, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase  )






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					str:







						"""simple docstring"""







						for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]				,    [1, 100, 1000]  ):
											self.check_over_configs(sigma_min=lowerCAmelCase				,    sigma_max=lowerCAmelCase  )






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







						"""simple docstring"""







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






	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					str:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			dict(self.forward_default_kwargs  )

						__UpperCamelCase				:  List[Any]								=			self.scheduler_classes[0]
						__UpperCamelCase				:  Dict								=			self.get_scheduler_config()
						__UpperCamelCase				:  Any								=			scheduler_class(**lowerCAmelCase  )

						__UpperCamelCase				:  Dict								=			3

						__UpperCamelCase				:  List[str]								=			self.dummy_model()
						__UpperCamelCase				:  Union[str, Any]								=			self.dummy_sample_deter

						scheduler.set_sigmas(lowerCAmelCase  )
						scheduler.set_timesteps(lowerCAmelCase  )
						__UpperCamelCase				:  Any								=			torch.manual_seed(0  )

						for i, t in enumerate(scheduler.timesteps  ):
											__UpperCamelCase				:  Union[str, Any]								=			scheduler.sigmas[i]

											for _ in range(scheduler.config.correct_steps  ):
																with torch.no_grad():
																					__UpperCamelCase				:  Union[str, Any]								=			model(lowerCAmelCase				,    lowerCAmelCase  )
																__UpperCamelCase				:  Optional[int]								=			scheduler.step_correct(lowerCAmelCase				,    lowerCAmelCase				,    generator=lowerCAmelCase				,    **lowerCAmelCase  ).prev_sample

											with torch.no_grad():
																__UpperCamelCase				:  Dict								=			model(lowerCAmelCase				,    lowerCAmelCase  )

											__UpperCamelCase				:  Optional[int]								=			scheduler.step_pred(lowerCAmelCase				,    lowerCAmelCase				,    lowerCAmelCase				,    generator=lowerCAmelCase				,    **lowerCAmelCase  )
											__UpperCamelCase			, __UpperCamelCase				:  Optional[int]								=			output.prev_sample, output.prev_sample_mean

						__UpperCamelCase				:  Any								=			torch.sum(torch.abs(lowerCAmelCase  )  )
						__UpperCamelCase				:  Dict								=			torch.mean(torch.abs(lowerCAmelCase  )  )

						assert np.isclose(result_sum.item()				,    1_43_72_75_85_28.0  )
						assert np.isclose(result_mean.item()				,    18_71_45_30.0  )






	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Tuple:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			dict(self.forward_default_kwargs  )

						__UpperCamelCase				:  List[Any]								=			kwargs.pop("""num_inference_steps"""				,    lowerCAmelCase  )

						for scheduler_class in self.scheduler_classes:
											__UpperCamelCase				:  List[str]								=			self.get_scheduler_config()
											__UpperCamelCase				:  str								=			scheduler_class(**lowerCAmelCase  )

											__UpperCamelCase				:  Any								=			self.dummy_sample
											__UpperCamelCase				:  Tuple								=			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"""  ):
																__UpperCamelCase				:  Union[str, Any]								=			num_inference_steps

											__UpperCamelCase				:  List[str]								=			scheduler.step_pred(lowerCAmelCase				,    0				,    lowerCAmelCase				,    generator=torch.manual_seed(0  )				,    **lowerCAmelCase  ).prev_sample
											__UpperCamelCase				:  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  )












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Any			  =		'mobilevit'






	def __init__(				self		:	int				,    lowerCAmelCase		:	Dict=3				,    lowerCAmelCase		:	Dict=256				,    lowerCAmelCase		:	Any=2				,    lowerCAmelCase		:	Any=[144, 192, 240]				,    lowerCAmelCase		:	int=[16, 32, 64, 96, 128, 160, 640]				,    lowerCAmelCase		:	List[Any]=4				,    lowerCAmelCase		:	Tuple=2.0				,    lowerCAmelCase		:	Tuple=4.0				,    lowerCAmelCase		:	List[str]="silu"				,    lowerCAmelCase		:	Tuple=3				,    lowerCAmelCase		:	Tuple=32				,    lowerCAmelCase		:	Any=0.1				,    lowerCAmelCase		:	List[str]=0.0				,    lowerCAmelCase		:	Union[str, Any]=0.1				,    lowerCAmelCase		:	List[str]=0.02				,    lowerCAmelCase		:	Optional[Any]=1E-5				,    lowerCAmelCase		:	Union[str, Any]=True				,    lowerCAmelCase		:	List[str]=256				,    lowerCAmelCase		:	List[Any]=[6, 12, 18]				,    lowerCAmelCase		:	Tuple=0.1				,    lowerCAmelCase		:	List[str]=255				,    **lowerCAmelCase		:	Union[str, Any]				,    )  ->					Optional[Any]:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase  )

						__UpperCamelCase				:  Tuple								=			num_channels
						__UpperCamelCase				:  Union[str, Any]								=			image_size
						__UpperCamelCase				:  List[Any]								=			patch_size
						__UpperCamelCase				:  Optional[int]								=			hidden_sizes
						__UpperCamelCase				:  Union[str, Any]								=			neck_hidden_sizes
						__UpperCamelCase				:  List[str]								=			num_attention_heads
						__UpperCamelCase				:  Union[str, Any]								=			mlp_ratio
						__UpperCamelCase				:  Tuple								=			expand_ratio
						__UpperCamelCase				:  Optional[Any]								=			hidden_act
						__UpperCamelCase				:  int								=			conv_kernel_size
						__UpperCamelCase				:  Any								=			output_stride
						__UpperCamelCase				:  int								=			hidden_dropout_prob
						__UpperCamelCase				:  Union[str, Any]								=			attention_probs_dropout_prob
						__UpperCamelCase				:  Dict								=			classifier_dropout_prob
						__UpperCamelCase				:  Any								=			initializer_range
						__UpperCamelCase				:  List[str]								=			layer_norm_eps
						__UpperCamelCase				:  Union[str, Any]								=			qkv_bias

						# decode head attributes for semantic segmentation
						__UpperCamelCase				:  Any								=			aspp_out_channels
						__UpperCamelCase				:  Union[str, Any]								=			atrous_rates
						__UpperCamelCase				:  Union[str, Any]								=			aspp_dropout_prob
						__UpperCamelCase				:  List[str]								=			semantic_loss_ignore_index






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Optional[Any]			  =		version.parse('1.11'      )






	@property
	def 				lowerCamelCase__    (				self		:	Any  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						if self.task == "image-classification":
											return OrderedDict([("""logits""", {0: """batch"""})]  )
						else:
											return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					float:







						"""simple docstring"""







						return 1E-4






