

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       __A (							lowerCAmelCase       ):
    lowerCAmelCase_							:						Tuple						 =							(PNDMScheduler,)
    lowerCAmelCase_							:						Optional[int]						 =							(("num_inference_steps", 50),)






    def 		lowercase__       (		self	:			List[str]			, **UpperCAmelCase_	:			List[Any]			):
        lowerCAmelCase :	Optional[int]				=					{
            'num_train_timesteps': 1000,
            'beta_start': 0.00_01,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_snake_case			)
        return config






    def 		lowercase__       (		self	:			Union[str, Any]			, UpperCAmelCase_	:			Tuple=0			, **UpperCAmelCase_	:			int			):
        lowerCAmelCase :	Union[str, Any]				=					dict(self.forward_default_kwargs			)
        lowerCAmelCase :	Tuple				=					kwargs.pop('num_inference_steps'			, _snake_case			)
        lowerCAmelCase :	int				=					self.dummy_sample
        lowerCAmelCase :	Tuple				=					0.1 * sample
        lowerCAmelCase :	List[Any]				=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
            lowerCAmelCase :	str				=					self.get_scheduler_config(**_snake_case			)
            lowerCAmelCase :	Dict				=					scheduler_class(**_snake_case			)
            scheduler.set_timesteps(_snake_case			)
            # copy over dummy past residuals
            lowerCAmelCase :	str				=					dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(_snake_case			)
                lowerCAmelCase :	Optional[Any]				=					scheduler_class.from_pretrained(_snake_case			)
                new_scheduler.set_timesteps(_snake_case			)
                # copy over dummy past residuals
                lowerCAmelCase :	int				=					dummy_past_residuals[:]

            lowerCAmelCase :	List[Any]				=					scheduler.step_prk(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample
            lowerCAmelCase :	Dict				=					new_scheduler.step_prk(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample

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

            lowerCAmelCase :	Optional[int]				=					scheduler.step_plms(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample
            lowerCAmelCase :	Optional[Any]				=					new_scheduler.step_plms(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample

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






    def 		lowercase__       (		self	:			str			):
        pass






    def 		lowercase__       (		self	:			int			, UpperCAmelCase_	:			str=0			, **UpperCAmelCase_	:			List[str]			):
        lowerCAmelCase :	List[Any]				=					dict(self.forward_default_kwargs			)
        lowerCAmelCase :	Tuple				=					kwargs.pop('num_inference_steps'			, _snake_case			)
        lowerCAmelCase :	Optional[Any]				=					self.dummy_sample
        lowerCAmelCase :	Union[str, Any]				=					0.1 * sample
        lowerCAmelCase :	List[str]				=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
            lowerCAmelCase :	List[str]				=					self.get_scheduler_config()
            lowerCAmelCase :	Any				=					scheduler_class(**_snake_case			)
            scheduler.set_timesteps(_snake_case			)

            # copy over dummy past residuals (must be after setting timesteps)
            lowerCAmelCase :	List[str]				=					dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(_snake_case			)
                lowerCAmelCase :	List[Any]				=					scheduler_class.from_pretrained(_snake_case			)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(_snake_case			)

                # copy over dummy past residual (must be after setting timesteps)
                lowerCAmelCase :	List[str]				=					dummy_past_residuals[:]

            lowerCAmelCase :	int				=					scheduler.step_prk(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample
            lowerCAmelCase :	str				=					new_scheduler.step_prk(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample

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

            lowerCAmelCase :	Any				=					scheduler.step_plms(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample
            lowerCAmelCase :	int				=					new_scheduler.step_plms(_snake_case			, _snake_case			, _snake_case			, **_snake_case			).prev_sample

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






    def 		lowercase__       (		self	:			List[Any]			, **UpperCAmelCase_	:			Tuple			):
        lowerCAmelCase :	List[str]				=					self.scheduler_classes[0]
        lowerCAmelCase :	str				=					self.get_scheduler_config(**_snake_case			)
        lowerCAmelCase :	Dict				=					scheduler_class(**_snake_case			)

        lowerCAmelCase :	Tuple				=					10
        lowerCAmelCase :	int				=					self.dummy_model()
        lowerCAmelCase :	str				=					self.dummy_sample_deter
        scheduler.set_timesteps(_snake_case			)

        for i, t in enumerate(scheduler.prk_timesteps			):
            lowerCAmelCase :	Dict				=					model(_snake_case			, _snake_case			)
            lowerCAmelCase :	Tuple				=					scheduler.step_prk(_snake_case			, _snake_case			, _snake_case			).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps			):
            lowerCAmelCase :	Tuple				=					model(_snake_case			, _snake_case			)
            lowerCAmelCase :	List[str]				=					scheduler.step_plms(_snake_case			, _snake_case			, _snake_case			).prev_sample

        return sample






    def 		lowercase__       (		self	:			Dict			):
        lowerCAmelCase :	str				=					dict(self.forward_default_kwargs			)

        lowerCAmelCase :	List[str]				=					kwargs.pop('num_inference_steps'			, _snake_case			)

        for scheduler_class in self.scheduler_classes:
            lowerCAmelCase :	Tuple				=					self.get_scheduler_config()
            lowerCAmelCase :	List[Any]				=					scheduler_class(**_snake_case			)

            lowerCAmelCase :	List[Any]				=					self.dummy_sample
            lowerCAmelCase :	Any				=					0.1 * sample

            if num_inference_steps is not None and hasattr(_snake_case			, 'set_timesteps'			):
                scheduler.set_timesteps(_snake_case			)
            elif num_inference_steps is not None and not hasattr(_snake_case			, 'set_timesteps'			):
                lowerCAmelCase :	Any				=					num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            lowerCAmelCase :	int				=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            lowerCAmelCase :	int				=					dummy_past_residuals[:]

            lowerCAmelCase :	List[str]				=					scheduler.step_prk(_snake_case			, 0			, _snake_case			, **_snake_case			).prev_sample
            lowerCAmelCase :	int				=					scheduler.step_prk(_snake_case			, 1			, _snake_case			, **_snake_case			).prev_sample

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

            lowerCAmelCase :	str				=					scheduler.step_plms(_snake_case			, 0			, _snake_case			, **_snake_case			).prev_sample
            lowerCAmelCase :	Any				=					scheduler.step_plms(_snake_case			, 1			, _snake_case			, **_snake_case			).prev_sample

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






    def 		lowercase__       (		self	:			Union[str, Any]			):




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






    def 		lowercase__       (		self	:			Dict			):




        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=_snake_case			)

        lowerCAmelCase :	Dict				=					self.scheduler_classes[0]
        lowerCAmelCase :	List[str]				=					self.get_scheduler_config(steps_offset=1			)
        lowerCAmelCase :	Optional[Any]				=					scheduler_class(**_snake_case			)
        scheduler.set_timesteps(10			)
        assert torch.equal(
            scheduler.timesteps			, torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]			)			, )






    def 		lowercase__       (		self	:			Dict			):




        for beta_start, beta_end in zip([0.00_01, 0.0_01]			, [0.0_02, 0.02]			):
            self.check_over_configs(beta_start=_snake_case			, beta_end=_snake_case			)






    def 		lowercase__       (		self	:			int			):




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






    def 		lowercase__       (		self	:			Optional[Any]			):




        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=_snake_case			)






    def 		lowercase__       (		self	:			List[Any]			):




        for t in [1, 5, 10]:
            self.check_over_forward(time_step=_snake_case			)






    def 		lowercase__       (		self	:			List[Any]			):




        for t, num_inference_steps in zip([1, 5, 10]			, [10, 50, 100]			):
            self.check_over_forward(num_inference_steps=_snake_case			)






    def 		lowercase__       (		self	:			Any			):
        lowerCAmelCase :	List[Any]				=					27

        for scheduler_class in self.scheduler_classes:
            lowerCAmelCase :	Dict				=					self.dummy_sample
            lowerCAmelCase :	int				=					0.1 * sample

            lowerCAmelCase :	Optional[Any]				=					self.get_scheduler_config()
            lowerCAmelCase :	int				=					scheduler_class(**_snake_case			)

            scheduler.set_timesteps(_snake_case			)

            # before power of 3 fix, would error on first step, so we only need to do two
            for i, t in enumerate(scheduler.prk_timesteps[:2]			):
                lowerCAmelCase :	Tuple				=					scheduler.step_prk(_snake_case			, _snake_case			, _snake_case			).prev_sample






    def 		lowercase__       (		self	:			List[str]			):




        with self.assertRaises(_snake_case			):
            lowerCAmelCase :	str				=					self.scheduler_classes[0]
            lowerCAmelCase :	List[Any]				=					self.get_scheduler_config()
            lowerCAmelCase :	Any				=					scheduler_class(**_snake_case			)

            scheduler.step_plms(self.dummy_sample			, 1			, self.dummy_sample			).prev_sample






    def 		lowercase__       (		self	:			List[Any]			):
        lowerCAmelCase :	Optional[int]				=					self.full_loop()
        lowerCAmelCase :	Dict				=					torch.sum(torch.abs(_snake_case			)			)
        lowerCAmelCase :	List[Any]				=					torch.mean(torch.abs(_snake_case			)			)

        assert abs(result_sum.item() - 1_98.13_18			) < 1E-2
        assert abs(result_mean.item() - 0.25_80			) < 1E-3






    def 		lowercase__       (		self	:			List[Any]			):
        lowerCAmelCase :	int				=					self.full_loop(prediction_type='v_prediction'			)
        lowerCAmelCase :	List[Any]				=					torch.sum(torch.abs(_snake_case			)			)
        lowerCAmelCase :	Union[str, Any]				=					torch.mean(torch.abs(_snake_case			)			)

        assert abs(result_sum.item() - 67.39_86			) < 1E-2
        assert abs(result_mean.item() - 0.08_78			) < 1E-3






    def 		lowercase__       (		self	:			List[str]			):
        lowerCAmelCase :	List[Any]				=					self.full_loop(set_alpha_to_one=_snake_case			, beta_start=0.01			)
        lowerCAmelCase :	Union[str, Any]				=					torch.sum(torch.abs(_snake_case			)			)
        lowerCAmelCase :	Dict				=					torch.mean(torch.abs(_snake_case			)			)

        assert abs(result_sum.item() - 2_30.03_99			) < 1E-2
        assert abs(result_mean.item() - 0.29_95			) < 1E-3






    def 		lowercase__       (		self	:			Union[str, Any]			):
        lowerCAmelCase :	Optional[Any]				=					self.full_loop(set_alpha_to_one=_snake_case			, beta_start=0.01			)
        lowerCAmelCase :	Dict				=					torch.sum(torch.abs(_snake_case			)			)
        lowerCAmelCase :	int				=					torch.mean(torch.abs(_snake_case			)			)

        assert abs(result_sum.item() - 1_86.94_82			) < 1E-2
        assert abs(result_mean.item() - 0.24_34			) < 1E-3







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''				)
								if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''				)

								super().__init__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

								if text is not None:
												__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if images is not None:
												__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if text is not None and images is not None:
												__a										=    image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_snake_case				)		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								return self.tokenizer.batch_decode(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




								return self.tokenizer.decode(*_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




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


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class