
"""simple docstring"""

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	['image_processor', 'tokenizer']
	lowercase_        =	'AutoImageProcessor'
	lowercase_        =	'AutoTokenizer'




	def __init__( self		, UpperCAmelCase_=None		, UpperCAmelCase_=None		, **UpperCAmelCase_					)		->							str:
			lowerCamelCase     :			Any		=    None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'		, UpperCAmelCase_		, )
					lowerCamelCase     :			List[str]		=    kwargs.pop('feature_extractor'					)

			lowerCamelCase     :			Any		=    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__(UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    self.image_processor
			lowerCamelCase     :			Union[str, Any]		=    False




	def __call__( self		, *UpperCAmelCase_		, **UpperCAmelCase_					)		->							str:
			# For backward compatibility
			if self._in_target_context_manager:
					return self.current_processor(*UpperCAmelCase_		, **UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    kwargs.pop('images'		, UpperCAmelCase_					)
			lowerCamelCase     :			List[str]		=    kwargs.pop('text'		, UpperCAmelCase_					)
			if len(UpperCAmelCase_					) > 0:
					lowerCamelCase     :			Any		=    args[0]
					lowerCamelCase     :			Tuple		=    args[1:]

			if images is None and text is None:
					raise ValueError('You need to specify either an `images` or `text` input to process.'					)

			if images is not None:
					lowerCamelCase     :			Dict		=    self.image_processor(UpperCAmelCase_		, *UpperCAmelCase_		, **UpperCAmelCase_					)
			if text is not None:
					lowerCamelCase     :			Optional[Any]		=    self.tokenizer(UpperCAmelCase_		, **UpperCAmelCase_					)

			if text is None:
					return inputs
			elif images is None:
					return encodings
			else:
					lowerCamelCase     :			Optional[Any]		=    encodings['input_ids']
					return inputs




	def 			_UpperCamelCase    ( self		, *UpperCAmelCase_		, **UpperCAmelCase_					)		->							str:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		, **UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, *UpperCAmelCase_		, **UpperCAmelCase_					)		->							List[str]:
			return self.tokenizer.decode(*UpperCAmelCase_		, **UpperCAmelCase_					)




	@contextmanager
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			warnings.warn(
			    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
			    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
			    'your images inputs, or in a separate call.'					)
			lowerCamelCase     :			List[Any]		=    True
			lowerCamelCase     :			Dict		=    self.tokenizer
			yield
			lowerCamelCase     :			str		=    self.image_processor
			lowerCamelCase     :			List[Any]		=    False




	@property
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		, UpperCAmelCase_		, )
			return self.image_processor_class




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		, UpperCAmelCase_		, )
			return self.image_processor








"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(KDPMaAncestralDiscreteScheduler,)
	lowercase_        =	1_0




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Optional[int]:
			lowerCamelCase     :			Any		=    {
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]					):
					self.check_over_configs(beta_start=UpperCAmelCase_		, beta_end=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Any		=    self.scheduler_classes[0]
			lowerCamelCase     :			List[str]		=    self.get_scheduler_config()
			lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Optional[Any]		=    torch.manual_seed(0					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Tuple		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Union[str, Any]		=    sample.to(UpperCAmelCase_					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Tuple		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    output.prev_sample

			lowerCamelCase     :			Optional[int]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3877					) < 1E-2
			assert abs(result_mean.item() - 18.0331					) < 5E-3




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(prediction_type='v_prediction'					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Dict		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Optional[int]		=    sample.to(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Optional[Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Optional[Any]		=    output.prev_sample

			lowerCamelCase     :			Union[str, Any]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 328.9970					) < 1E-2
			assert abs(result_mean.item() - 0.4284					) < 1E-3




	def 			_UpperCamelCase    ( self					)		->							Any:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Union[str, Any]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Dict		=    self.get_scheduler_config()
			lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps		, device=UpperCAmelCase_					)
			lowerCamelCase     :			int		=    torch.manual_seed(0					)

			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					lowerCamelCase     :			Optional[int]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			str		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Dict		=    output.prev_sample

			lowerCamelCase     :			List[str]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3818					) < 1E-1
			assert abs(result_mean.item() - 18.0331					) < 1E-3






