"""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 				_A		( unittest.TestCase			):

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			XLMRobertaModel.from_pretrained("""xlm-roberta-base"""		)
            lowercase														=			torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]		)
            # The dog is cute and lives in the garden house

            lowercase														=			torch.Size((1, 12, 768)		)  # batch_size, sequence_length, embedding_vector_dim
            lowercase														=			torch.tensor(
                [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]		)
            #  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():
                  lowercase														=			model(__lowerCAmelCase		)["""last_hidden_state"""].detach()
            self.assertEqual(output.shape	, __lowerCAmelCase		)
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]	, __lowerCAmelCase	, atol=1E-3		)		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			XLMRobertaModel.from_pretrained("""xlm-roberta-large"""		)
            lowercase														=			torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]		)
            # The dog is cute and lives in the garden house

            lowercase														=			torch.Size((1, 12, 1024)		)  # batch_size, sequence_length, embedding_vector_dim
            lowercase														=			torch.tensor(
                [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]		)
            #  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():
                  lowercase														=			model(__lowerCAmelCase		)["""last_hidden_state"""].detach()
            self.assertEqual(output.shape	, __lowerCAmelCase		)
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]	, __lowerCAmelCase	, atol=1E-3		)		)




"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

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

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

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

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

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			0.1 * sample

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

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                  lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.timesteps[5]
                  lowercase														=			scheduler.timesteps[6]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

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

      def        A__ (		self		):







            """simple docstring"""



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

      def        A__ (		self		):







            """simple docstring"""



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







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10



