



"""simple docstring"""
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					(CMStochasticIterativeScheduler,)
							__lowerCAmelCase    					=					1_0






							def snake_case_  (   self			,     **a_      ):
									a_     :      List[Any] 						=     {
									    "num_train_timesteps": 2_0_1,
									    "sigma_min": 0.002,
									    "sigma_max": 80.0,
									}

									config.update(**a_      )
									return config






							def snake_case_  (   self      ):
									a_     :      Dict 						=     1_0

									a_     :      str 						=     self.get_scheduler_config()
									a_     :      Optional[int] 						=     self.scheduler_classes[0](**a_      )

									scheduler.set_timesteps(a_      )

									a_     :      List[str] 						=     scheduler.timesteps[0]
									a_     :      str 						=     scheduler.timesteps[1]

									a_     :      Optional[int] 						=     self.dummy_sample
									a_     :      Any 						=     0.1 * sample

									a_     :      Any 						=     scheduler.step(a_			,     a_			,     a_      ).prev_sample
									a_     :      Dict 						=     scheduler.step(a_			,     a_			,     a_      ).prev_sample

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






							def snake_case_  (   self      ):
									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=a_      )






							def snake_case_  (   self      ):
									for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=a_      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.scheduler_classes[0]
									a_     :      List[str] 						=     self.get_scheduler_config()
									a_     :      int 						=     scheduler_class(**a_      )

									a_     :      Optional[int] 						=     1
									scheduler.set_timesteps(a_      )
									a_     :      Optional[Any] 						=     scheduler.timesteps

									a_     :      Dict 						=     torch.manual_seed(0      )

									a_     :      Union[str, Any] 						=     self.dummy_model()
									a_     :      Dict 						=     self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(a_      ):
											# 1. scale model input
											a_     :      List[str] 						=     scheduler.scale_model_input(a_			,     a_      )

											# 2. predict noise residual
											a_     :      Dict 						=     model(a_			,     a_      )

											# 3. predict previous sample x_t-1
											a_     :      Optional[Any] 						=     scheduler.step(a_			,     a_			,     a_			,     generator=a_      ).prev_sample

											a_     :      Dict 						=     pred_prev_sample

									a_     :      Optional[Any] 						=     torch.sum(torch.abs(a_      )      )
									a_     :      List[Any] 						=     torch.mean(torch.abs(a_      )      )

									assert abs(result_sum.item() - 192.7_614      ) < 1e-2
									assert abs(result_mean.item() - 0.2_510      ) < 1e-3






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.scheduler_classes[0]
									a_     :      Union[str, Any] 						=     self.get_scheduler_config()
									a_     :      List[str] 						=     scheduler_class(**a_      )

									a_     :      Union[str, Any] 						=     [1_0_6, 0]
									scheduler.set_timesteps(timesteps=a_      )
									a_     :      Optional[Any] 						=     scheduler.timesteps

									a_     :      Tuple 						=     torch.manual_seed(0      )

									a_     :      Optional[Any] 						=     self.dummy_model()
									a_     :      List[Any] 						=     self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
											# 1. scale model input
											a_     :      str 						=     scheduler.scale_model_input(a_			,     a_      )

											# 2. predict noise residual
											a_     :      List[str] 						=     model(a_			,     a_      )

											# 3. predict previous sample x_t-1
											a_     :      List[Any] 						=     scheduler.step(a_			,     a_			,     a_			,     generator=a_      ).prev_sample

											a_     :      Any 						=     pred_prev_sample

									a_     :      int 						=     torch.sum(torch.abs(a_      )      )
									a_     :      Any 						=     torch.mean(torch.abs(a_      )      )

									assert abs(result_sum.item() - 347.6_357      ) < 1e-2
									assert abs(result_mean.item() - 0.4_527      ) < 1e-3






							def snake_case_  (   self      ):
									a_     :      str 						=     self.scheduler_classes[0]
									a_     :      Dict 						=     self.get_scheduler_config()
									a_     :      Any 						=     scheduler_class(**a_      )

									a_     :      List[str] 						=     [3_9, 3_0, 1_2, 1_5, 0]

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






							def snake_case_  (   self      ):
									a_     :      str 						=     self.scheduler_classes[0]
									a_     :      List[Any] 						=     self.get_scheduler_config()
									a_     :      List[str] 						=     scheduler_class(**a_      )

									a_     :      str 						=     [3_9, 3_0, 1_2, 1, 0]
									a_     :      Tuple 						=     len(a_      )

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






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.scheduler_classes[0]
									a_     :      Tuple 						=     self.get_scheduler_config()
									a_     :      Tuple 						=     scheduler_class(**a_      )

									a_     :      Tuple 						=     [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 typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"ViltImageProcessor"
							__lowerCAmelCase    					=					("BertTokenizer", "BertTokenizerFast")






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     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_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     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_      )
									a_     :      Dict 						=     self.image_processor






							def __call__(   self			,     a_			,     a_ = None			,     a_ = True			,     a_ = False			,     a_ = None			,     a_ = None			,     a_ = 0			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = False			,     a_ = True			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     self.tokenizer(
									    text=a_			,     add_special_tokens=a_			,     padding=a_			,     truncation=a_			,     max_length=a_			,     stride=a_			,     pad_to_multiple_of=a_			,     return_token_type_ids=a_			,     return_attention_mask=a_			,     return_overflowing_tokens=a_			,     return_special_tokens_mask=a_			,     return_offsets_mapping=a_			,     return_length=a_			,     verbose=a_			,     return_tensors=a_			,     **a_			,     )
									# add pixel_values + pixel_mask
									a_     :      List[Any] 						=     self.image_processor(a_			,     return_tensors=a_      )
									encoding.update(a_      )

									return encoding






							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      ):
									a_     :      Union[str, Any] 						=     self.tokenizer.model_input_names
									a_     :      Dict 						=     self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor