


"""simple docstring"""


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =(DDPMParallelScheduler,)

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			{
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "variance_type": "fixed_small",
										    "clip_sample": True,
										}

										config.update(**__a	)
										return config

							def 			snake_case				(     self	):
										for timesteps in [1, 5, 1_00, 10_00]:
													self.check_over_configs(num_train_timesteps=__a	)

							def 			snake_case				(     self	):
										for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,   [0.0_0_2, 0.0_2, 0.2, 2]	):
													self.check_over_configs(beta_start=__a    ,   beta_end=__a	)

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

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

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

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

							def 			snake_case				(     self	):
										for t in [0, 5_00, 9_99]:
													self.check_over_forward(time_step=__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.0_0_9_7_9	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.0_2	)	) < 1e-5

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			len(__a	)

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

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

										__lowerCAmelCase				    =			model(samples.flatten(0    ,   1	)    ,   timesteps.flatten(0    ,   1	)	)
										__lowerCAmelCase				    =			scheduler.batch_step_no_noise(__a    ,   timesteps.flatten(0    ,   1	)    ,   samples.flatten(0    ,   1	)	)

										__lowerCAmelCase				    =			torch.sum(torch.abs(__a	)	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_sum.item() - 1_1_5_3.1_8_3_3	) < 1e-2
										assert abs(result_mean.item() - 0.5_0_0_5	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			len(__a	)

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

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

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

													__lowerCAmelCase				    =			pred_prev_sample

										__lowerCAmelCase				    =			torch.sum(torch.abs(__a	)	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_sum.item() - 2_5_8.9_6_0_6	) < 1e-2
										assert abs(result_mean.item() - 0.3_3_7_2	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			len(__a	)

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

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

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

													__lowerCAmelCase				    =			pred_prev_sample

										__lowerCAmelCase				    =			torch.sum(torch.abs(__a	)	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_sum.item() - 2_0_2.0_2_9_6	) < 1e-2
										assert abs(result_mean.item() - 0.2_6_3_1	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			[1_00, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=__a	)

										__lowerCAmelCase				    =			scheduler.timesteps

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

													__lowerCAmelCase				    =			scheduler.previous_timestep(__a	)
													__lowerCAmelCase				    =			prev_t.item()

													self.assertEqual(__a    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			[1_00, 87, 50, 51, 0]

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

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			[1_00, 87, 50, 1, 0]
										__lowerCAmelCase				    =			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 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config()
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			[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	)	)




