



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				(PNDMScheduler,)
				__lowerCamelCase =				(('''num_inference_steps''', 50),)




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    {
									    """num_train_timesteps""": 1000,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**_snake_case		)
									return config




				def 							snake_case   (							self						,				_snake_case=0						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)
									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)
									_lowerCAmelCase						=    self.dummy_sample
									_lowerCAmelCase						=    0.1 * sample
									_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config(**_snake_case		)
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)
														scheduler.set_timesteps(_snake_case		)
														# copy over dummy past residuals
														_lowerCAmelCase						=    dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_snake_case		)
																			_lowerCAmelCase						=    scheduler_class.from_pretrained(_snake_case		)
																			new_scheduler.set_timesteps(_snake_case		)
																			# copy over dummy past residuals
																			_lowerCAmelCase						=    dummy_past_residuals[:]

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									pass




				def 							snake_case   (							self						,				_snake_case=0						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)
									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)
									_lowerCAmelCase						=    self.dummy_sample
									_lowerCAmelCase						=    0.1 * sample
									_lowerCAmelCase						=    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)
														scheduler.set_timesteps(_snake_case		)

														# copy over dummy past residuals (must be after setting timesteps)
														_lowerCAmelCase						=    dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(_snake_case		)
																			_lowerCAmelCase						=    scheduler_class.from_pretrained(_snake_case		)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(_snake_case		)

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

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    new_scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case						,				**_snake_case		).prev_sample

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




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.scheduler_classes[0]
									_lowerCAmelCase						=    self.get_scheduler_config(**_snake_case		)
									_lowerCAmelCase						=    scheduler_class(**_snake_case		)

									_lowerCAmelCase						=    10
									_lowerCAmelCase						=    self.dummy_model()
									_lowerCAmelCase						=    self.dummy_sample_deter
									scheduler.set_timesteps(_snake_case		)

									for i, t in enumerate(scheduler.prk_timesteps		):
														_lowerCAmelCase						=    model(_snake_case						,				_snake_case		)
														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case		).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps		):
														_lowerCAmelCase						=    model(_snake_case						,				_snake_case		)
														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				_snake_case						,				_snake_case		).prev_sample

									return sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    dict(self.forward_default_kwargs		)

									_lowerCAmelCase						=    kwargs.pop("""num_inference_steps"""						,				_snake_case		)

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														_lowerCAmelCase						=    self.dummy_sample
														_lowerCAmelCase						=    0.1 * sample

														if num_inference_steps is not None and hasattr(_snake_case						,				"""set_timesteps"""		):
																			scheduler.set_timesteps(_snake_case		)
														elif num_inference_steps is not None and not hasattr(_snake_case						,				"""set_timesteps"""		):
																			_lowerCAmelCase						=    num_inference_steps

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

														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				0						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				1						,				_snake_case						,				**_snake_case		).prev_sample

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

														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				0						,				_snake_case						,				**_snake_case		).prev_sample
														_lowerCAmelCase						=    scheduler.step_plms(_snake_case						,				1						,				_snake_case						,				**_snake_case		).prev_sample

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




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





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

									_lowerCAmelCase						=    self.scheduler_classes[0]
									_lowerCAmelCase						=    self.get_scheduler_config(steps_offset=1		)
									_lowerCAmelCase						=    scheduler_class(**_snake_case		)
									scheduler.set_timesteps(10		)
									assert torch.equal(
									    scheduler.timesteps						,				torch.LongTensor(
									        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)						,				)




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





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




				def 							snake_case   (							self		):



									"""simple docstring"""





									for t in [1, 5, 10]:
														self.check_over_forward(time_step=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									for t, num_inference_steps in zip([1, 5, 10]						,				[10, 50, 100]		):
														self.check_over_forward(num_inference_steps=_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    27

									for scheduler_class in self.scheduler_classes:
														_lowerCAmelCase						=    self.dummy_sample
														_lowerCAmelCase						=    0.1 * sample

														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

														scheduler.set_timesteps(_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]		):
																			_lowerCAmelCase						=    scheduler.step_prk(_snake_case						,				_snake_case						,				_snake_case		).prev_sample




				def 							snake_case   (							self		):



									"""simple docstring"""





									with self.assertRaises(_snake_case		):
														_lowerCAmelCase						=    self.scheduler_classes[0]
														_lowerCAmelCase						=    self.get_scheduler_config()
														_lowerCAmelCase						=    scheduler_class(**_snake_case		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop()
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(prediction_type="""v_prediction"""		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(set_alpha_to_one=_snake_case						,				beta_start=0.01		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.full_loop(set_alpha_to_one=_snake_case						,				beta_start=0.01		)
									_lowerCAmelCase						=    torch.sum(torch.abs(_snake_case		)		)
									_lowerCAmelCase						=    torch.mean(torch.abs(_snake_case		)		)

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





'''simple docstring'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"		):


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

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

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_start.T		).sum(1		).softmax(0		)
																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_end.T		).sum(1		).softmax(0		)

																	if p_starts is not None:
																							lowercase_	:     Tuple													=       torch.vstack((p_starts, p_start)		)
																							lowercase_	:     Optional[Any]													=       torch.vstack((p_ends, p_end)		)
																	else:
																							lowercase_	:     str													=       p_start
																							lowercase_	:     int													=       p_end

											return p_starts, p_ends



