code: 
  
  
  import unittest
  
  from transformers import load_tool
  
  from .test_tools_common import ToolTesterMixin
  
  
  UpperCamelCase__				       =			'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
  
  class 						UpperCAmelCase__   (      unittest.TestCase      ,						A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			load_tool('''summarization'''		)
              self.tool.setup()
              _lowercase					:				str           =			load_tool('''summarization'''	,      remote=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			self.tool(UpperCamelCase		)
              self.assertEqual(
                  UpperCamelCase	,      '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			self.remote_tool(UpperCamelCase		)
              self.assertEqual(
                  UpperCamelCase	,      '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			self.tool(text=UpperCamelCase		)
              self.assertEqual(
                  UpperCamelCase	,      '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			self.remote_tool(text=UpperCamelCase		)
              self.assertEqual(
                  UpperCamelCase	,      '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''	,      )
style_context: 
  
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  (DDIMScheduler,)
       UpperCAmelCase_         =  (('''eta''', 0.0), ('''num_inference_steps''', 5_0))
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]	,      **UpperCamelCase       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			{
                  '''num_train_timesteps''': 10_00,
                  '''beta_start''': 0.0001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''clip_sample''': True,
              }
  
              config.update(**UpperCamelCase		)
              return config
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str	,      **UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			self.scheduler_classes[0]
              _lowercase					:				Dict           =			self.get_scheduler_config(**UpperCamelCase		)
              _lowercase					:				List[str]           =			scheduler_class(**UpperCamelCase		)
  
              _lowercase       ,	_lowercase					:				int           =			10, 0.0
  
              _lowercase					:				List[str]           =			self.dummy_model()
              _lowercase					:				int           =			self.dummy_sample_deter
  
              scheduler.set_timesteps(UpperCamelCase		)
  
              for t in scheduler.timesteps:
                     _lowercase					:				Union[str, Any]           =			model(UpperCamelCase	,      UpperCamelCase		)
                     _lowercase					:				List[Any]           =			scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              return sample
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for timesteps in [1_00, 5_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for steps_offset in [0, 1]:
                     self.check_over_configs(steps_offset=UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			self.scheduler_classes[0]
              _lowercase					:				Tuple           =			self.get_scheduler_config(steps_offset=1		)
              _lowercase					:				Union[str, Any]           =			scheduler_class(**UpperCamelCase		)
              scheduler.set_timesteps(5		)
              assert torch.equal(scheduler.timesteps	,      torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]		)		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """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=UpperCamelCase	,      beta_end=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for schedule in ["linear", "squaredcos_cap_v2"]:
                     self.check_over_configs(beta_schedule=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for clip_sample in [True, False]:
                     self.check_over_configs(clip_sample=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              self.check_over_configs(thresholding=UpperCamelCase		)
              for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "v_prediction"]:
                            self.check_over_configs(
                                thresholding=UpperCamelCase	,      prediction_type=UpperCamelCase	,      sample_max_value=UpperCamelCase	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t, num_inference_steps in zip([1, 10, 50]	,      [10, 50, 5_00]		):
                     self.check_over_forward(time_step=UpperCamelCase	,      num_inference_steps=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              for t, eta in zip([1, 10, 49]	,      [0.0, 0.5, 1.0]		):
                     self.check_over_forward(time_step=UpperCamelCase	,      eta=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.scheduler_classes[0]
              _lowercase					:				str           =			self.get_scheduler_config()
              _lowercase					:				int           =			scheduler_class(**UpperCamelCase		)
  
              assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_20	,      4_00		) - 0.14771		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_80	,      9_60		) - 0.32460		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(0	,      0		) - 0.0		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87	,      4_86		) - 0.00979		)		) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99	,      9_98		) - 0.02		)		) < 1E-5
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			self.full_loop()
  
              _lowercase					:				Optional[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 172.0067		) < 1E-2
              assert abs(result_mean.item() - 0.223967		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			self.full_loop(prediction_type='''v_prediction'''		)
  
              _lowercase					:				int           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Union[str, Any]           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 52.5302		) < 1E-2
              assert abs(result_mean.item() - 0.0684		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
              _lowercase					:				List[Any]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Any           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 149.8295		) < 1E-2
              assert abs(result_mean.item() - 0.1951		) < 1E-3
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			self.full_loop(set_alpha_to_one=UpperCamelCase	,      beta_start=0.01		)
              _lowercase					:				Optional[int]           =			torch.sum(torch.abs(UpperCamelCase		)		)
              _lowercase					:				Dict           =			torch.mean(torch.abs(UpperCamelCase		)		)
  
              assert abs(result_sum.item() - 149.0784		) < 1E-2
              assert abs(result_mean.item() - 0.1941		) < 1E-3