



"""simple docstring"""





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 				lowercase__       (   __a				):







       '''simple docstring'''


       UpperCamelCase						=					(DPMSolverSinglestepScheduler,)
       UpperCamelCase						=					(('''num_inference_steps''', 25),)



       def 					lowercase__    (							self      :  Any		,    **_UpperCAmelCase      :  Optional[Any]   )			->						Dict:


            '''simple docstring'''




            UpperCAmelCase_      			=							{
                "num_train_timesteps": 1000,
                "beta_start": 0.0001,
                "beta_end": 0.02,
                "beta_schedule": "linear",
                "solver_order": 2,
                "prediction_type": "epsilon",
                "thresholding": False,
                "sample_max_value": 1.0,
                "algorithm_type": "dpmsolver++",
                "solver_type": "midpoint",
                "lambda_min_clipped": -float("inf"   ),
                "variance_type": None,
            }

            config.update(**UpperCamelCase__   )
            return config



       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  str=0		,    **_UpperCAmelCase      :  Union[str, Any]   )			->						Dict:


            '''simple docstring'''




            UpperCAmelCase_      			=							dict(self.forward_default_kwargs   )
            UpperCAmelCase_      			=							kwargs.pop("num_inference_steps"		,    UpperCamelCase__   )
            UpperCAmelCase_      			=							self.dummy_sample
            UpperCAmelCase_      			=							0.1 * sample
            UpperCAmelCase_      			=							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 UpperCAmelCase_      			=							self.get_scheduler_config(**UpperCamelCase__   )
                 UpperCAmelCase_      			=							scheduler_class(**UpperCamelCase__   )
                 scheduler.set_timesteps(UpperCamelCase__   )
                 # copy over dummy past residuals
                 UpperCAmelCase_      			=							dummy_past_residuals[: scheduler.config.solver_order]

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

                 UpperCAmelCase_			,   UpperCAmelCase_      			=							sample, sample
                 for t in range(UpperCamelCase__		,    time_step + scheduler.config.solver_order + 1   ):
                      UpperCAmelCase_      			=							scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__		,    **UpperCamelCase__   ).prev_sample
                      UpperCAmelCase_      			=							new_scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__		,    **UpperCamelCase__   ).prev_sample

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



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


            '''simple docstring'''




            pass



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  Dict=0		,    **_UpperCAmelCase      :  List[str]   )			->						Any:


            '''simple docstring'''




            UpperCAmelCase_      			=							dict(self.forward_default_kwargs   )
            UpperCAmelCase_      			=							kwargs.pop("num_inference_steps"		,    UpperCamelCase__   )
            UpperCAmelCase_      			=							self.dummy_sample
            UpperCAmelCase_      			=							0.1 * sample
            UpperCAmelCase_      			=							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 UpperCAmelCase_      			=							self.get_scheduler_config()
                 UpperCAmelCase_      			=							scheduler_class(**UpperCamelCase__   )
                 scheduler.set_timesteps(UpperCamelCase__   )

                 # copy over dummy past residuals (must be after setting timesteps)
                 UpperCAmelCase_      			=							dummy_past_residuals[: scheduler.config.solver_order]

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

                      # copy over dummy past residual (must be after setting timesteps)
                      UpperCAmelCase_      			=							dummy_past_residuals[: new_scheduler.config.solver_order]

                 UpperCAmelCase_      			=							scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__		,    **UpperCamelCase__   ).prev_sample
                 UpperCAmelCase_      			=							new_scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__		,    **UpperCamelCase__   ).prev_sample

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



       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  Dict=None		,    **_UpperCAmelCase      :  Optional[Any]   )			->						int:


            '''simple docstring'''




            if scheduler is None:
                 UpperCAmelCase_      			=							self.scheduler_classes[0]
                 UpperCAmelCase_      			=							self.get_scheduler_config(**UpperCamelCase__   )
                 UpperCAmelCase_      			=							scheduler_class(**UpperCamelCase__   )

            UpperCAmelCase_      			=							self.scheduler_classes[0]
            UpperCAmelCase_      			=							self.get_scheduler_config(**UpperCamelCase__   )
            UpperCAmelCase_      			=							scheduler_class(**UpperCamelCase__   )

            UpperCAmelCase_      			=							10
            UpperCAmelCase_      			=							self.dummy_model()
            UpperCAmelCase_      			=							self.dummy_sample_deter
            scheduler.set_timesteps(UpperCamelCase__   )

            for i, t in enumerate(scheduler.timesteps   ):
                 UpperCAmelCase_      			=							model(UpperCamelCase__		,    UpperCamelCase__   )
                 UpperCAmelCase_      			=							scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__   ).prev_sample

            return sample



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


            '''simple docstring'''




            UpperCAmelCase_      			=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()   )
            UpperCAmelCase_      			=							50
            UpperCAmelCase_      			=							self.dummy_model()
            UpperCAmelCase_      			=							self.dummy_sample_deter
            scheduler.set_timesteps(UpperCamelCase__   )

            # make sure that the first t is uneven
            for i, t in enumerate(scheduler.timesteps[3:]   ):
                 UpperCAmelCase_      			=							model(UpperCamelCase__		,    UpperCamelCase__   )
                 UpperCAmelCase_      			=							scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__   ).prev_sample

            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.2574   ) < 1e-3



       def 					lowercase__    (							self      :  List[Any]   )			->						Any:


            '''simple docstring'''




            for timesteps in [25, 50, 100, 999, 1000]:
                 self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



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


            '''simple docstring'''




            UpperCAmelCase_      			=							DPMSolverSinglestepScheduler(**self.get_scheduler_config()   )
            UpperCAmelCase_      			=							self.full_loop(scheduler=UpperCamelCase__   )
            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.2791   ) < 1e-3

            UpperCAmelCase_      			=							DEISMultistepScheduler.from_config(scheduler.config   )
            UpperCAmelCase_      			=							DPMSolverMultistepScheduler.from_config(scheduler.config   )
            UpperCAmelCase_      			=							UniPCMultistepScheduler.from_config(scheduler.config   )
            UpperCAmelCase_      			=							DPMSolverSinglestepScheduler.from_config(scheduler.config   )

            UpperCAmelCase_      			=							self.full_loop(scheduler=UpperCamelCase__   )
            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.2791   ) < 1e-3



       def 					lowercase__    (							self      :  int   )			->						int:


            '''simple docstring'''




            self.check_over_configs(thresholding=UpperCamelCase__   )
            for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=UpperCamelCase__		,    prediction_type=UpperCamelCase__		,    sample_max_value=UpperCamelCase__		,    algorithm_type="dpmsolver++"		,    solver_order=UpperCamelCase__		,    solver_type=UpperCamelCase__		,    )



       def 					lowercase__    (							self      :  Dict   )			->						int:


            '''simple docstring'''




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



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


            '''simple docstring'''




            for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=UpperCamelCase__		,    solver_type=UpperCamelCase__		,    prediction_type=UpperCamelCase__		,    algorithm_type=UpperCamelCase__		,    )
                                UpperCAmelCase_      			=							self.full_loop(
                                    solver_order=UpperCamelCase__		,    solver_type=UpperCamelCase__		,    prediction_type=UpperCamelCase__		,    algorithm_type=UpperCamelCase__		,    )
                                assert not torch.isnan(UpperCamelCase__   ).any(), "Samples have nan numbers"



       def 					lowercase__    (							self      :  Optional[int]   )			->						Any:


            '''simple docstring'''




            self.check_over_configs(lower_order_final=UpperCamelCase__   )
            self.check_over_configs(lower_order_final=UpperCamelCase__   )



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


            '''simple docstring'''




            self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
            self.check_over_configs(lambda_min_clipped=-5.1   )



       def 					lowercase__    (							self      :  List[Any]   )			->						Dict:


            '''simple docstring'''




            self.check_over_configs(variance_type=UpperCamelCase__   )
            self.check_over_configs(variance_type="learned_range"   )



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


            '''simple docstring'''




            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                 self.check_over_forward(num_inference_steps=UpperCamelCase__		,    time_step=0   )



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


            '''simple docstring'''




            UpperCAmelCase_      			=							self.full_loop()
            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.2791   ) < 1e-3



       def 					lowercase__    (							self      :  Optional[Any]   )			->						Optional[int]:


            '''simple docstring'''




            UpperCAmelCase_      			=							self.full_loop(use_karras_sigmas=UpperCamelCase__   )
            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.2248   ) < 1e-3



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


            '''simple docstring'''




            UpperCAmelCase_      			=							self.full_loop(prediction_type="v_prediction"   )
            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.1453   ) < 1e-3



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


            '''simple docstring'''




            UpperCAmelCase_      			=							self.full_loop(prediction_type="v_prediction"		,    use_karras_sigmas=UpperCamelCase__   )
            UpperCAmelCase_      			=							torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_mean.item() - 0.0649   ) < 1e-3







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


            '''simple docstring'''




            UpperCAmelCase_      			=							self.scheduler_classes[0]
            UpperCAmelCase_      			=							self.get_scheduler_config(thresholding=UpperCamelCase__		,    dynamic_thresholding_ratio=0   )
            UpperCAmelCase_      			=							scheduler_class(**UpperCamelCase__   )

            UpperCAmelCase_      			=							10
            UpperCAmelCase_      			=							self.dummy_model()
            UpperCAmelCase_      			=							self.dummy_sample_deter.half()
            scheduler.set_timesteps(UpperCamelCase__   )

            for i, t in enumerate(scheduler.timesteps   ):
                 UpperCAmelCase_      			=							model(UpperCamelCase__		,    UpperCamelCase__   )
                 UpperCAmelCase_      			=							scheduler.step(UpperCamelCase__		,    UpperCamelCase__		,    UpperCamelCase__   ).prev_sample

            assert sample.dtype == torch.floataa









"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
  import torch

  from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @slow
       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )







       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]   )
             # The dog is cute and lives in the garden house

             UpperCAmelCase_      			=							torch.Size((1, 12, 1024)   )  # batch_size, sequence_length, embedding_vector_dim
             UpperCAmelCase_      			=							torch.tensor(
                 [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
             #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
             #  xlmr.eval()
             #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
             with torch.no_grad():
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )["last_hidden_state"].detach()
             self.assertEqual(output.shape		,    _UpperCAmelCase   )
             # compare the actual values for a slice of last dim
             self.assertTrue(torch.allclose(output[:, :, -1]		,    _UpperCAmelCase		,    atol=1e-3   )   )




