

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
				__lowerCamelCase						:	Union[str, Any]														=					(KDPMaDiscreteScheduler,)
				__lowerCamelCase						:	Tuple														=					10





				def 				snake_case_    (				self						,       **a):
									lowercase__   :       str				=	{
									    'num_train_timesteps': 1100,
									    'beta_start': 0.0_001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									}

									config.update(**a)
									return config





				def 				snake_case_    (				self):
									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=a)





				def 				snake_case_    (				self):
									for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]):
														self.check_over_configs(beta_start=a						,       beta_end=a)





				def 				snake_case_    (				self):
									for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=a)





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





				def 				snake_case_    (				self):
									lowercase__   :       List[str]				=	self.scheduler_classes[0]
									lowercase__   :       Dict				=	self.get_scheduler_config(prediction_type='v_prediction')
									lowercase__   :       Optional[Any]				=	scheduler_class(**a)

									scheduler.set_timesteps(self.num_inference_steps)

									lowercase__   :       Optional[int]				=	self.dummy_model()
									lowercase__   :       Union[str, Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__   :       List[Any]				=	sample.to(a)

									for i, t in enumerate(scheduler.timesteps):
														lowercase__   :       List[str]				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       int				=	model(a						,       a)

														lowercase__   :       Union[str, Any]				=	scheduler.step(a						,       a						,       a)
														lowercase__   :       str				=	output.prev_sample

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

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6934e-07) < 1e-2
														assert abs(result_mean.item() - 6.1112e-10) < 1e-3
									else:
														# CUDA
														assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
														assert abs(result_mean.item() - 0.0_002) < 1e-3





				def 				snake_case_    (				self):
									if torch_device == "mps":
														return
									lowercase__   :       Tuple				=	self.scheduler_classes[0]
									lowercase__   :       Optional[int]				=	self.get_scheduler_config()
									lowercase__   :       Optional[Any]				=	scheduler_class(**a)

									scheduler.set_timesteps(self.num_inference_steps)

									lowercase__   :       int				=	self.dummy_model()
									lowercase__   :       Optional[int]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__   :       List[Any]				=	sample.to(a)

									for i, t in enumerate(scheduler.timesteps):
														lowercase__   :       List[str]				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       Union[str, Any]				=	model(a						,       a)

														lowercase__   :       Dict				=	scheduler.step(a						,       a						,       a)
														lowercase__   :       Tuple				=	output.prev_sample

									lowercase__   :       Any				=	torch.sum(torch.abs(a))
									lowercase__   :       Any				=	torch.mean(torch.abs(a))

									if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 20.4_125) < 1e-2
														assert abs(result_mean.item() - 0.0_266) < 1e-3
									else:
														# CUDA
														assert abs(result_sum.item() - 20.4_125) < 1e-2
														assert abs(result_mean.item() - 0.0_266) < 1e-3





				def 				snake_case_    (				self):
									if torch_device == "mps":
														return
									lowercase__   :       str				=	self.scheduler_classes[0]
									lowercase__   :       Optional[Any]				=	self.get_scheduler_config()
									lowercase__   :       str				=	scheduler_class(**a)

									scheduler.set_timesteps(self.num_inference_steps						,       device=a)

									lowercase__   :       Optional[Any]				=	self.dummy_model()
									lowercase__   :       Optional[Any]				=	self.dummy_sample_deter.to(a) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
														lowercase__   :       Any				=	scheduler.scale_model_input(a						,       a)

														lowercase__   :       str				=	model(a						,       a)

														lowercase__   :       Dict				=	scheduler.step(a						,       a						,       a)
														lowercase__   :       Union[str, Any]				=	output.prev_sample

									lowercase__   :       Optional[Any]				=	torch.sum(torch.abs(a))
									lowercase__   :       Optional[int]				=	torch.mean(torch.abs(a))

									if str(a).startswith('cpu'):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 20.4_125) < 1e-2
														assert abs(result_mean.item() - 0.0_266) < 1e-3
									else:
														# CUDA
														assert abs(result_sum.item() - 20.4_125) < 1e-2
														assert abs(result_mean.item() - 0.0_266) < 1e-3




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 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor""", """tokenizer"""]
    __lowerCamelCase						:	int														=					"""ViltImageProcessor"""
    __lowerCamelCase						:	Union[str, Any]														=					("""BertTokenizer""", """BertTokenizerFast""")





    def __init__(				self						,       a=None						,       a=None						,       **a):
         lowercase__   :       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.'						,       a						,       )
              lowercase__   :       Union[str, Any]				=	kwargs.pop('feature_extractor')

         lowercase__   :       List[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__(a						,       a)
         lowercase__   :       str				=	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						,       ):
         lowercase__   :       Optional[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
         lowercase__   :       str				=	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):
         lowercase__   :       int				=	self.tokenizer.model_input_names
         lowercase__   :       List[str]				=	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

