code: import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					(KDPMaAncestralDiscreteScheduler,)
  							lowerCAmelCase__       =					10
  
  
  
  							def     _UpperCAmelCase       (    self     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	{
  														    """num_train_timesteps""": 1100,
  														    """beta_start""": 0.0_001,
  														    """beta_end""": 0.02,
  														    """beta_schedule""": """linear""",
  														}
  
  														config.update(**UpperCAmelCase  )
  														return config
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for timesteps in [10, 50, 100, 1000]:
  																					self.check_over_configs(num_train_timesteps=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														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=UpperCAmelCase     ,   beta_end=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for schedule in ["linear", "scaled_linear"]:
  																					self.check_over_configs(beta_schedule=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if torch_device == "mps":
  																					return
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config()
  														_lowercase				=	scheduler_class(**UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps  )
  
  														_lowercase				=	torch.manual_seed(0  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter * scheduler.init_noise_sigma
  														_lowercase				=	sample.to(UpperCAmelCase  )
  
  														for i, t in enumerate(scheduler.timesteps  ):
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   generator=UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														assert abs(result_sum.item() - 13_849.3_877  ) < 1e-2
  														assert abs(result_mean.item() - 18.0_331  ) < 5e-3
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for prediction_type in ["epsilon", "v_prediction"]:
  																					self.check_over_configs(prediction_type=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if torch_device == "mps":
  																					return
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
  														_lowercase				=	scheduler_class(**UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter * scheduler.init_noise_sigma
  														_lowercase				=	sample.to(UpperCAmelCase  )
  
  														_lowercase				=	torch.manual_seed(0  )
  
  														for i, t in enumerate(scheduler.timesteps  ):
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   generator=UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														assert abs(result_sum.item() - 328.9_970  ) < 1e-2
  														assert abs(result_mean.item() - 0.4_284  ) < 1e-3
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if torch_device == "mps":
  																					return
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config()
  														_lowercase				=	scheduler_class(**UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps     ,   device=UpperCAmelCase  )
  														_lowercase				=	torch.manual_seed(0  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter.to(UpperCAmelCase  ) * scheduler.init_noise_sigma
  
  														for t in scheduler.timesteps:
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   generator=UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														assert abs(result_sum.item() - 13_849.3_818  ) < 1e-1
  														assert abs(result_mean.item() - 18.0_331  ) < 1e-3
  
  
  
style_context: import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  @require_vision
  class _lowercase					(       unittest.TestCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	tempfile.mkdtemp()
  
  														_lowercase				=	CLIPImageProcessor()
  														_lowercase				=	BertTokenizer.from_pretrained(
  														    """hf-internal-testing/tiny-random-BertModel"""     ,   model_input_names=["""input_ids""", """attention_mask"""]  )
  
  														_lowercase				=	GitProcessor(UpperCAmelCase     ,   UpperCAmelCase  )
  
  														processor.save_pretrained(self.tmpdirname  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return AutoProcessor.from_pretrained(self.tmpdirname     ,   **UpperCAmelCase  ).tokenizer
  
  
  
  							def     _UpperCAmelCase       (    self     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return AutoProcessor.from_pretrained(self.tmpdirname     ,   **UpperCAmelCase  ).image_processor
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														shutil.rmtree(self.tmpdirname  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	[np.random.randint(255     ,   size=(3, 30, 400)     ,   dtype=np.uinta  )]
  
  														_lowercase				=	[Image.fromarray(np.moveaxis(UpperCAmelCase     ,   0     ,   -1  )  ) for x in image_inputs]
  
  														return image_inputs
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	GitProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()  )
  														processor.save_pretrained(self.tmpdirname  )
  
  														_lowercase				=	self.get_tokenizer(bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""  )
  														_lowercase				=	self.get_image_processor(do_normalize=UpperCAmelCase     ,   padding_value=1.0  )
  
  														_lowercase				=	GitProcessor.from_pretrained(
  														    self.tmpdirname     ,   bos_token="""(BOS)"""     ,   eos_token="""(EOS)"""     ,   do_normalize=UpperCAmelCase     ,   padding_value=1.0  )
  
  														self.assertEqual(processor.tokenizer.get_vocab()     ,   tokenizer_add_kwargs.get_vocab()  )
  														self.assertIsInstance(processor.tokenizer     ,   UpperCAmelCase  )
  
  														self.assertEqual(processor.image_processor.to_json_string()     ,   image_processor_add_kwargs.to_json_string()  )
  														self.assertIsInstance(processor.image_processor     ,   UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	self.prepare_image_inputs()
  
  														_lowercase				=	image_processor(UpperCAmelCase     ,   return_tensors="""np"""  )
  														_lowercase				=	processor(images=UpperCAmelCase     ,   return_tensors="""np"""  )
  
  														for key in input_feat_extract.keys():
  																					self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1e-2  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	"""lower newer"""
  
  														_lowercase				=	processor(text=UpperCAmelCase  )
  
  														_lowercase				=	tokenizer(UpperCAmelCase     ,   return_token_type_ids=UpperCAmelCase  )
  
  														for key in encoded_tok.keys():
  																					self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key]  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	"""lower newer"""
  														_lowercase				=	self.prepare_image_inputs()
  
  														_lowercase				=	processor(text=UpperCAmelCase     ,   images=UpperCAmelCase  )
  
  														self.assertListEqual(list(inputs.keys()  )     ,   ["""input_ids""", """attention_mask""", """pixel_values"""]  )
  
  														# test if it raises when no input is passed
  														with pytest.raises(UpperCAmelCase  ):
  																					processor()
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  														_lowercase				=	processor.batch_decode(UpperCAmelCase  )
  														_lowercase				=	tokenizer.batch_decode(UpperCAmelCase  )
  
  														self.assertListEqual(UpperCAmelCase     ,   UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.get_image_processor()
  														_lowercase				=	self.get_tokenizer()
  
  														_lowercase				=	GitProcessor(tokenizer=UpperCAmelCase     ,   image_processor=UpperCAmelCase  )
  
  														_lowercase				=	"""lower newer"""
  														_lowercase				=	self.prepare_image_inputs()
  
  														_lowercase				=	processor(text=UpperCAmelCase     ,   images=UpperCAmelCase  )
  
  														# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
  														self.assertListEqual(list(inputs.keys()  )     ,   ["""input_ids""", """attention_mask""", """pixel_values"""]  )
  
  
  