






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 	lowerCamelCase__				(							UpperCAmelCase ):
      lowerCamelCase_							:   Union[str, Any]											=	(DDIMScheduler,)
      lowerCamelCase_							:   Optional[int]											=	(('eta', 0.0), ('num_inference_steps', 50))






      def      UpperCAmelCase_     (self					:    Any		,		**_snake_case					:    Dict		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    str							=     {
           'num_train_timesteps': 1000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'clip_sample': True,
       }

       config.update(**_snake_case		)
       return config






      def      UpperCAmelCase_     (self					:    int		,		**_snake_case					:    List[Any]		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     self.scheduler_classes[0]
       lowerCamelCase_							:    Optional[int]							=     self.get_scheduler_config(**_snake_case		)
       lowerCamelCase_							:    Any							=     scheduler_class(**_snake_case		)

       lowerCamelCase_		,      lowerCamelCase_							:    List[str]							=     10, 0.0

       lowerCamelCase_							:    int							=     self.dummy_model()
       lowerCamelCase_							:    Tuple							=     self.dummy_sample_deter

       scheduler.set_timesteps(_snake_case		)

       for t in scheduler.timesteps:
        lowerCamelCase_							:    Any							=     model(_snake_case		,		_snake_case		)
        lowerCamelCase_							:    Tuple							=     scheduler.step(_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		).prev_sample

       return sample






      def      UpperCAmelCase_     (self					:    str		)       ->   int:


       """simple docstring"""






       for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=_snake_case		)






      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   Union[str, Any]:


       """simple docstring"""






       for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=_snake_case		)

       lowerCamelCase_							:    Tuple							=     self.scheduler_classes[0]
       lowerCamelCase_							:    List[Any]							=     self.get_scheduler_config(steps_offset=1		)
       lowerCamelCase_							:    Union[str, Any]							=     scheduler_class(**_snake_case		)
       scheduler.set_timesteps(5		)
       assert torch.equal(scheduler.timesteps		,		torch.LongTensor([801, 601, 401, 201, 1]		)		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   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=_snake_case		,		beta_end=_snake_case		)






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Union[str, Any]:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   List[str]:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   List[Any]:


       """simple docstring"""






       for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=_snake_case		)






      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   str:


       """simple docstring"""






       for timestep_spacing in ["trailing", "leading"]:
        self.check_over_configs(timestep_spacing=_snake_case		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   Optional[Any]:


       """simple docstring"""






       for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=_snake_case		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Dict:


       """simple docstring"""






       self.check_over_configs(thresholding=_snake_case		)
       for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(
             thresholding=_snake_case		,		prediction_type=_snake_case		,		sample_max_value=_snake_case		,		)






      def      UpperCAmelCase_     (self					:    Dict		)       ->   List[str]:


       """simple docstring"""






       for t in [1, 10, 49]:
        self.check_over_forward(time_step=_snake_case		)






      def      UpperCAmelCase_     (self					:    int		)       ->   List[Any]:


       """simple docstring"""






       for t, num_inference_steps in zip([1, 10, 50]		,		[10, 50, 500]		):
        self.check_over_forward(time_step=_snake_case		,		num_inference_steps=_snake_case		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Dict:


       """simple docstring"""






       for t, eta in zip([1, 10, 49]		,		[0.0, 0.5, 1.0]		):
        self.check_over_forward(time_step=_snake_case		,		eta=_snake_case		)






      def      UpperCAmelCase_     (self					:    Dict		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     self.scheduler_classes[0]
       lowerCamelCase_							:    Optional[Any]							=     self.get_scheduler_config()
       lowerCamelCase_							:    Optional[Any]							=     scheduler_class(**_snake_case		)

       assert torch.sum(torch.abs(scheduler._get_variance(0		,		0		) - 0.0		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(420		,		400		) - 0.1_4771		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(980		,		960		) - 0.3_2460		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(0		,		0		) - 0.0		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(487		,		486		) - 0.0_0979		)		) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(999		,		998		) - 0.02		)		) < 1e-5






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     self.full_loop()

       lowerCamelCase_							:    Dict							=     torch.sum(torch.abs(_snake_case		)		)
       lowerCamelCase_							:    Optional[int]							=     torch.mean(torch.abs(_snake_case		)		)

       assert abs(result_sum.item() - 172.0067		) < 1e-2
       assert abs(result_mean.item() - 0.22_3967		) < 1e-3






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[Any]							=     self.full_loop(prediction_type='v_prediction'		)

       lowerCamelCase_							:    List[Any]							=     torch.sum(torch.abs(_snake_case		)		)
       lowerCamelCase_							:    Union[str, Any]							=     torch.mean(torch.abs(_snake_case		)		)

       assert abs(result_sum.item() - 52.5302		) < 1e-2
       assert abs(result_mean.item() - 0.0684		) < 1e-3






      def      UpperCAmelCase_     (self					:    str		)       ->   int:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     self.full_loop(set_alpha_to_one=_snake_case		,		beta_start=0.01		)
       lowerCamelCase_							:    Tuple							=     torch.sum(torch.abs(_snake_case		)		)
       lowerCamelCase_							:    List[Any]							=     torch.mean(torch.abs(_snake_case		)		)

       assert abs(result_sum.item() - 149.8295		) < 1e-2
       assert abs(result_mean.item() - 0.1951		) < 1e-3






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Tuple							=     self.full_loop(set_alpha_to_one=_snake_case		,		beta_start=0.01		)
       lowerCamelCase_							:    Tuple							=     torch.sum(torch.abs(_snake_case		)		)
       lowerCamelCase_							:    List[Any]							=     torch.mean(torch.abs(_snake_case		)		)

       assert abs(result_sum.item() - 149.0784		) < 1e-2
       assert abs(result_mean.item() - 0.1941		) < 1e-3








import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types




class 	lowerCamelCase__				(							unittest.TestCase,   UpperCAmelCase ):






      def      UpperCAmelCase_     (self					:    Any		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    str							=     load_tool('translation'		)
       self.tool.setup()
       lowerCamelCase_							:    Optional[int]							=     load_tool('translation'		,		remote=_snake_case		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     self.tool('Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.remote_tool('Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self.tool(text='Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.remote_tool(text='Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    str							=     ['Hey, what\'s up?', 'English', 'Spanish']
       lowerCamelCase_							:    Any							=     self.tool(*_snake_case		)

       # There is a single output
       if len(self.tool.outputs		) == 1:
        lowerCamelCase_							:    Any							=     [outputs]

       self.assertListEqual(output_types(_snake_case		)		,		self.tool.outputs		)






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     ['Hey, what\'s up?', 'English', 'Spanish']
       lowerCamelCase_							:    Optional[Any]							=     self.tool(*_snake_case		)

       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    List[str]							=     [outputs]

       self.assertEqual(len(_snake_case		)		,		len(self.tool.outputs		)		)

       for output, output_type in zip(_snake_case		,		self.tool.outputs		):
        lowerCamelCase_							:    Optional[int]							=     AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(_snake_case		,		_snake_case		)		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     ['Hey, what\'s up?', 'English', 'Spanish']

       lowerCamelCase_							:    List[str]							=     []

       for _input, input_type in zip(_snake_case		,		self.tool.inputs		):
        if isinstance(_snake_case		,		_snake_case		):
         _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
        else:
         _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
       lowerCamelCase_							:    Any							=     self.tool(*_snake_case		)

       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    Optional[Any]							=     [outputs]

       self.assertEqual(len(_snake_case		)		,		len(self.tool.outputs		)		)
