





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 			__UpperCamelCase    (							A__    ):
     __A	:  Union[str, Any]							  =   (DDPMParallelScheduler,)





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   {
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''variance_type''': '''fixed_small''',
            '''clip_sample''': True,
        }

        config.update(**_UpperCamelCase					)
        return config





     def 		UpperCamelCase(     self					):
        for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,							[0.002, 0.02, 0.2, 2]					):
           self.check_over_configs(beta_start=_UpperCamelCase				,							beta_end=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        self.check_over_configs(thresholding=_UpperCamelCase					)
        for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_UpperCamelCase				,							prediction_type=_UpperCamelCase				,							sample_max_value=_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        for t in [0, 500, 999]:
           self.check_over_forward(time_step=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.00979					)					) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.02					)					) < 1e-5





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   len(_UpperCamelCase					)

        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        _UpperCAmelCase           =   self.dummy_sample_deter + 0.1
        _UpperCAmelCase           =   self.dummy_sample_deter - 0.1

        _UpperCAmelCase           =   samplea.shape[0]
        _UpperCAmelCase           =   torch.stack([samplea, samplea, samplea]				,							dim=0					)
        _UpperCAmelCase           =   torch.arange(_UpperCamelCase					)[0:3, None].repeat(1				,							_UpperCamelCase					)

        _UpperCAmelCase           =   model(samples.flatten(0				,							1					)				,							timesteps.flatten(0				,							1					)					)
        _UpperCAmelCase           =   scheduler.batch_step_no_noise(_UpperCamelCase				,							timesteps.flatten(0				,							1					)				,							samples.flatten(0				,							1					)					)

        _UpperCAmelCase           =   torch.sum(torch.abs(_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_sum.item() - 1153.1833					) < 1e-2
        assert abs(result_mean.item() - 0.5005					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   len(_UpperCamelCase					)

        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        _UpperCAmelCase           =   torch.manual_seed(0					)

        for t in reversed(range(_UpperCamelCase					)					):
           # 1. predict noise residual
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					)

           # 2. predict previous mean of sample x_t-1
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					).prev_sample

           _UpperCAmelCase           =   pred_prev_sample

        _UpperCAmelCase           =   torch.sum(torch.abs(_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_sum.item() - 258.9606					) < 1e-2
        assert abs(result_mean.item() - 0.3372					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config(prediction_type='''v_prediction'''					)
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   len(_UpperCamelCase					)

        _UpperCAmelCase           =   self.dummy_model()
        _UpperCAmelCase           =   self.dummy_sample_deter
        _UpperCAmelCase           =   torch.manual_seed(0					)

        for t in reversed(range(_UpperCamelCase					)					):
           # 1. predict noise residual
           _UpperCAmelCase           =   model(_UpperCamelCase				,							_UpperCamelCase					)

           # 2. predict previous mean of sample x_t-1
           _UpperCAmelCase           =   scheduler.step(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							generator=_UpperCamelCase					).prev_sample

           _UpperCAmelCase           =   pred_prev_sample

        _UpperCAmelCase           =   torch.sum(torch.abs(_UpperCamelCase					)					)
        _UpperCAmelCase           =   torch.mean(torch.abs(_UpperCamelCase					)					)

        assert abs(result_sum.item() - 202.0296					) < 1e-2
        assert abs(result_mean.item() - 0.2631					) < 1e-3





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=_UpperCamelCase					)

        _UpperCAmelCase           =   scheduler.timesteps

        for i, timestep in enumerate(_UpperCamelCase					):
           if i == len(_UpperCamelCase					) - 1:
              _UpperCAmelCase           =   -1
           else:
              _UpperCAmelCase           =   timesteps[i + 1]

           _UpperCAmelCase           =   scheduler.previous_timestep(_UpperCamelCase					)
           _UpperCAmelCase           =   prev_t.item()

           self.assertEqual(_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [100, 87, 50, 51, 0]

        with self.assertRaises(_UpperCamelCase				,							msg='''`custom_timesteps` must be in descending order.'''					):
           scheduler.set_timesteps(timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [100, 87, 50, 1, 0]
        _UpperCAmelCase           =   len(_UpperCamelCase					)

        with self.assertRaises(_UpperCamelCase				,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''					):
           scheduler.set_timesteps(num_inference_steps=_UpperCamelCase				,							timesteps=_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.scheduler_classes[0]
        _UpperCAmelCase           =   self.get_scheduler_config()
        _UpperCAmelCase           =   scheduler_class(**_UpperCamelCase					)

        _UpperCAmelCase           =   [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            _UpperCamelCase				,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,							):
           scheduler.set_timesteps(timesteps=_UpperCamelCase					)



"""simple docstring"""






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


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					if depth_multiplier <= 0:
								raise ValueError('''depth_multiplier must be greater than zero.'''   )

					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Tuple					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	depth_multiplier
					_UpperCamelCase					:Any					     =	depth_divisible_by
					_UpperCamelCase					:Optional[int]					     =	min_depth
					_UpperCamelCase					:Union[str, Any]					     =	expand_ratio
					_UpperCamelCase					:str					     =	output_stride
					_UpperCamelCase					:Tuple					     =	first_layer_is_expansion
					_UpperCamelCase					:Any					     =	finegrained_output
					_UpperCamelCase					:Union[str, Any]					     =	hidden_act
					_UpperCamelCase					:Any					     =	tf_padding
					_UpperCamelCase					:str					     =	classifier_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	initializer_range
					_UpperCamelCase					:int					     =	layer_norm_eps
					_UpperCamelCase					:Union[str, Any]					     =	semantic_loss_ignore_index






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )
		@property
		def   _UpperCamelCase( self   )      ->	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   _UpperCamelCase( self   )      ->	float:

					"""simple docstring"""



					return 1E-4

