
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (PNDMScheduler,)
     SCREAMING_SNAKE_CASE_		   =  (('''num_inference_steps''', 50),)






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**__SCREAMING_SNAKE_CASE  )
           return config






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				dict(self.forward_default_kwargs  )
           UpperCamelCase__    :   str					=				kwargs.pop('''num_inference_steps'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.dummy_sample
           UpperCamelCase__    :   Optional[Any]					=				0.1 * sample
           UpperCamelCase__    :   Union[str, Any]					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   int					=				self.get_scheduler_config(**__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[str]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )
                 # copy over dummy past residuals
                 UpperCamelCase__    :   str					=				dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   List[Any]					=				scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE  )
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )
                       # copy over dummy past residuals
                       UpperCamelCase__    :   Union[str, Any]					=				dummy_past_residuals[:]

                 UpperCamelCase__    :   Union[str, Any]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   Tuple					=				new_scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

                 UpperCamelCase__    :   int					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   str					=				new_scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0						,						**__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				dict(self.forward_default_kwargs  )
           UpperCamelCase__    :   Tuple					=				kwargs.pop('''num_inference_steps'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				self.dummy_sample
           UpperCamelCase__    :   Optional[Any]					=				0.1 * sample
           UpperCamelCase__    :   Dict					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   List[Any]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   Optional[Any]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

                 # copy over dummy past residuals (must be after setting timesteps)
                 UpperCamelCase__    :   Union[str, Any]					=				dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Union[str, Any]					=				scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE  )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

                       # copy over dummy past residual (must be after setting timesteps)
                       UpperCamelCase__    :   str					=				dummy_past_residuals[:]

                 UpperCamelCase__    :   Optional[Any]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   Optional[int]					=				new_scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

                 UpperCamelCase__    :   Optional[int]					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   Optional[Any]					=				new_scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.scheduler_classes[0]
           UpperCamelCase__    :   List[str]					=				self.get_scheduler_config(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				1_0
           UpperCamelCase__    :   List[str]					=				self.dummy_model()
           UpperCamelCase__    :   Dict					=				self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

           for i, t in enumerate(scheduler.prk_timesteps  ):
                 UpperCamelCase__    :   Any					=				model(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[Any]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps  ):
                 UpperCamelCase__    :   Union[str, Any]					=				model(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Dict					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample

           return sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				dict(self.forward_default_kwargs  )

           UpperCamelCase__    :   List[Any]					=				kwargs.pop('''num_inference_steps'''						,						__SCREAMING_SNAKE_CASE  )

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   Optional[int]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   List[str]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

                 UpperCamelCase__    :   Tuple					=				self.dummy_sample
                 UpperCamelCase__    :   Optional[int]					=				0.1 * sample

                 if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE						,						'''set_timesteps'''  ):
                       scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )
                 elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE						,						'''set_timesteps'''  ):
                       UpperCamelCase__    :   List[Any]					=				num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 UpperCamelCase__    :   Optional[Any]					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                 UpperCamelCase__    :   Optional[Any]					=				dummy_past_residuals[:]

                 UpperCamelCase__    :   List[str]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						0						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   int					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						1						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

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

                 UpperCamelCase__    :   Optional[Any]					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						0						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   int					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						1						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           for timesteps in [1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







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

           UpperCamelCase__    :   Dict					=				self.scheduler_classes[0]
           UpperCamelCase__    :   List[Any]					=				self.get_scheduler_config(steps_offset=1  )
           UpperCamelCase__    :   Any					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
           scheduler.set_timesteps(1_0  )
           assert torch.equal(
               scheduler.timesteps						,						torch.LongTensor(
                   [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]  )						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           for beta_start, beta_end in zip([0.0001, 0.001]						,						[0.002, 0.02]  ):
                 self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE						,						beta_end=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







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






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




           """simple docstring"""







           for t in [1, 5, 1_0]:
                 self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           for t, num_inference_steps in zip([1, 5, 1_0]						,						[1_0, 5_0, 1_0_0]  ):
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   Any					=				2_7

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   List[Any]					=				self.dummy_sample
                 UpperCamelCase__    :   str					=				0.1 * sample

                 UpperCamelCase__    :   Union[str, Any]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   str					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

                 # before power of 3 fix, would error on first step, so we only need to do two
                 for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
                       UpperCamelCase__    :   Tuple					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           with self.assertRaises(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Tuple					=				self.scheduler_classes[0]
                 UpperCamelCase__    :   Optional[Any]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   Optional[Any]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

                 scheduler.step_plms(self.dummy_sample						,						1						,						self.dummy_sample  ).prev_sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.full_loop()
           UpperCamelCase__    :   Tuple					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Any					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 198.1318  ) < 1e-2
           assert abs(result_mean.item() - 0.2580  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.full_loop(prediction_type='''v_prediction'''  )
           UpperCamelCase__    :   Any					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Tuple					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 67.3986  ) < 1e-2
           assert abs(result_mean.item() - 0.0878  ) < 1e-3






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




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						beta_start=0.01  )
           UpperCamelCase__    :   List[str]					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Union[str, Any]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 230.0399  ) < 1e-2
           assert abs(result_mean.item() - 0.2995  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						beta_start=0.01  )
           UpperCamelCase__    :   Any					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Optional[int]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 186.9482  ) < 1e-2
           assert abs(result_mean.item() - 0.2434  ) < 1e-3




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

           UpperCamelCase__    :   Tuple					=				AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,						return_dict=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.CosineSimilarity(3						,						1e-08  )
           UpperCamelCase__    :   List[Any]					=				torch.nn.Softmax(dim=1  )






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




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







           return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

           for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
                 if i == 0:
                       UpperCamelCase__    :   int					=				0
                 else:
                       UpperCamelCase__    :   Optional[int]					=				support_sizes[i - 1]

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

                 UpperCamelCase__    :   Tuple					=				torch.matmul(q[i]						,						s_start.T  ).sum(1  ).softmax(0  )
                 UpperCamelCase__    :   Optional[Any]					=				torch.matmul(q[i]						,						s_end.T  ).sum(1  ).softmax(0  )

                 if p_starts is not None:
                       UpperCamelCase__    :   Union[str, Any]					=				torch.vstack((p_starts, p_start)  )
                       UpperCamelCase__    :   str					=				torch.vstack((p_ends, p_end)  )
                 else:
                       UpperCamelCase__    :   List[Any]					=				p_start
                       UpperCamelCase__    :   List[str]					=				p_end

           return p_starts, p_ends


