


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class A  (   lowerCAmelCase__     ):
      UpperCamelCase__							:  Any      =(DDPMParallelScheduler,)






      def        lowerCamelCase       (					self						:     Any			,   **lowercase_						:     List[str] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		int		={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'variance_type': 'fixed_small',
                 'clip_sample': True,
             }

             config.update(**__a )
             return config






      def        lowerCamelCase       (					self						:     Tuple )					->						List[str]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     int )					->						List[str]:
             """simple docstring"""




             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=__a			,   beta_end=__a )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						str:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Optional[Any]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     List[Any] )					->						Optional[int]:
             """simple docstring"""




             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=__a )






      def        lowerCamelCase       (					self						:     Any )					->						Optional[Any]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     str )					->						Optional[Any]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Any )					->						List[Any]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     str )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.scheduler_classes[0]
             _lowerCamelCase       :		int		=self.get_scheduler_config()
             _lowerCamelCase       :		Any		=scheduler_class(**__a )

             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        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.scheduler_classes[0]
             _lowerCamelCase       :		Any		=self.get_scheduler_config()
             _lowerCamelCase       :		str		=scheduler_class(**__a )

             _lowerCamelCase       :		Dict		=len(__a )

             _lowerCamelCase       :		Union[str, Any]		=self.dummy_model()
             _lowerCamelCase       :		int		=self.dummy_sample_deter
             _lowerCamelCase       :		str		=self.dummy_sample_deter + 0.1
             _lowerCamelCase       :		List[str]		=self.dummy_sample_deter - 0.1

             _lowerCamelCase       :		List[str]		=samplea.shape[0]
             _lowerCamelCase       :		List[str]		=torch.stack([samplea, samplea, samplea]			,   dim=0 )
             _lowerCamelCase       :		List[str]		=torch.arange(__a )[0:3, None].repeat(1			,   __a )

             _lowerCamelCase       :		Union[str, Any]		=model(samples.flatten(0			,   1 )			,   timesteps.flatten(0			,   1 ) )
             _lowerCamelCase       :		Dict		=scheduler.batch_step_no_noise(__a			,   timesteps.flatten(0			,   1 )			,   samples.flatten(0			,   1 ) )

             _lowerCamelCase       :		str		=torch.sum(torch.abs(__a ) )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(__a ) )

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






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=self.scheduler_classes[0]
             _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config()
             _lowerCamelCase       :		List[str]		=scheduler_class(**__a )

             _lowerCamelCase       :		List[Any]		=len(__a )

             _lowerCamelCase       :		List[Any]		=self.dummy_model()
             _lowerCamelCase       :		Union[str, Any]		=self.dummy_sample_deter
             _lowerCamelCase       :		Optional[int]		=torch.manual_seed(0 )

             for t in reversed(range(__a ) ):
                    # 1. predict noise residual
                    _lowerCamelCase       :		int		=model(__a			,   __a )

                    # 2. predict previous mean of sample x_t-1
                    _lowerCamelCase       :		Union[str, Any]		=scheduler.step(__a			,   __a			,   __a			,   generator=__a ).prev_sample

                    _lowerCamelCase       :		Optional[Any]		=pred_prev_sample

             _lowerCamelCase       :		str		=torch.sum(torch.abs(__a ) )
             _lowerCamelCase       :		Optional[Any]		=torch.mean(torch.abs(__a ) )

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






      def        lowerCamelCase       (					self						:     List[str] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.scheduler_classes[0]
             _lowerCamelCase       :		Union[str, Any]		=self.get_scheduler_config(prediction_type='v_prediction' )
             _lowerCamelCase       :		Any		=scheduler_class(**__a )

             _lowerCamelCase       :		Any		=len(__a )

             _lowerCamelCase       :		List[Any]		=self.dummy_model()
             _lowerCamelCase       :		Any		=self.dummy_sample_deter
             _lowerCamelCase       :		Optional[int]		=torch.manual_seed(0 )

             for t in reversed(range(__a ) ):
                    # 1. predict noise residual
                    _lowerCamelCase       :		List[str]		=model(__a			,   __a )

                    # 2. predict previous mean of sample x_t-1
                    _lowerCamelCase       :		Any		=scheduler.step(__a			,   __a			,   __a			,   generator=__a ).prev_sample

                    _lowerCamelCase       :		str		=pred_prev_sample

             _lowerCamelCase       :		List[str]		=torch.sum(torch.abs(__a ) )
             _lowerCamelCase       :		Dict		=torch.mean(torch.abs(__a ) )

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






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=self.scheduler_classes[0]
             _lowerCamelCase       :		str		=self.get_scheduler_config()
             _lowerCamelCase       :		List[Any]		=scheduler_class(**__a )

             _lowerCamelCase       :		Tuple		=[100, 87, 50, 1, 0]

             scheduler.set_timesteps(timesteps=__a )

             _lowerCamelCase       :		Dict		=scheduler.timesteps

             for i, timestep in enumerate(__a ):
                    if i == len(__a ) - 1:
                           _lowerCamelCase       :		Any		=-1
                    else:
                           _lowerCamelCase       :		str		=timesteps[i + 1]

                    _lowerCamelCase       :		Union[str, Any]		=scheduler.previous_timestep(__a )
                    _lowerCamelCase       :		List[Any]		=prev_t.item()

                    self.assertEqual(__a			,   __a )






      def        lowerCamelCase       (					self						:     Dict )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.scheduler_classes[0]
             _lowerCamelCase       :		Dict		=self.get_scheduler_config()
             _lowerCamelCase       :		str		=scheduler_class(**__a )

             _lowerCamelCase       :		Dict		=[100, 87, 50, 51, 0]

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






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.scheduler_classes[0]
             _lowerCamelCase       :		List[Any]		=self.get_scheduler_config()
             _lowerCamelCase       :		Any		=scheduler_class(**__a )

             _lowerCamelCase       :		Optional[Any]		=[100, 87, 50, 1, 0]
             _lowerCamelCase       :		Optional[Any]		=len(__a )

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






      def        lowerCamelCase       (					self						:     str )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.scheduler_classes[0]
             _lowerCamelCase       :		Optional[int]		=self.get_scheduler_config()
             _lowerCamelCase       :		Optional[Any]		=scheduler_class(**__a )

             _lowerCamelCase       :		Any		=[scheduler.config.num_train_timesteps]

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






"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

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

										__lowerCAmelCase				    =			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__(__a    ,   __a	)

							def __call__(     self    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):

										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:
													__lowerCAmelCase				    =			self.tokenizer(__a    ,   return_tensors=__a    ,   **__a	)

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

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

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




