import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       (UnCLIPScheduler,)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  **_UpperCamelCase     :				List[str])				->int:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						{
              """num_train_timesteps""": 1000,
              """variance_type""": """fixed_small_log""",
              """clip_sample""": True,
              """clip_sample_range""": 1.0,
              """prediction_type""": """epsilon""",
          }

          config.update(**_UpperCamelCase)
          return config



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


          """simple docstring"""


          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_UpperCamelCase)



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


          """simple docstring"""


          for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=_UpperCamelCase)



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


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->str:


          """simple docstring"""


          for clip_sample_range in [1, 5, 10, 20]:
              self.check_over_configs(clip_sample_range=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=_UpperCamelCase)



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


          """simple docstring"""


          for time_step in [0, 500, 999]:
              for prev_timestep in [None, 5, 100, 250, 500, 750]:
                  if prev_timestep is not None and prev_timestep >= time_step:
                      continue

                  self.check_over_forward(time_step=_UpperCamelCase    ,  prev_timestep=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(variance_type="""fixed_small_log""")
          _lowerCamelCase     :		Any 						=						scheduler_class(**_UpperCamelCase)

          assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_0_0_0E-1_0)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_5_4_9_6_2_5)) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9_9_9_4_9_8_7)) < 1E-5



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


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(variance_type="""learned_range""")
          _lowerCamelCase     :		Optional[Any] 						=						scheduler_class(**_UpperCamelCase)

          _lowerCamelCase     :		int 						=						0.5

          assert scheduler._get_variance(1    ,  predicted_variance=_UpperCamelCase) - -1_0.1_7_1_2_7_9_0 < 1E-5
          assert scheduler._get_variance(487    ,  predicted_variance=_UpperCamelCase) - -5.7_9_9_8_0_5_2 < 1E-5
          assert scheduler._get_variance(999    ,  predicted_variance=_UpperCamelCase) - -0.0_0_1_0_0_1_1 < 1E-5



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		List[str] 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Union[str, Any] 						=						scheduler_class(**_UpperCamelCase)

          _lowerCamelCase     :		Tuple 						=						scheduler.timesteps

          _lowerCamelCase     :		int 						=						self.dummy_model()
          _lowerCamelCase     :		List[str] 						=						self.dummy_sample_deter
          _lowerCamelCase     :		Optional[int] 						=						torch.manual_seed(0)

          for i, t in enumerate(_UpperCamelCase):
              # 1. predict noise residual
              _lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              # 2. predict previous mean of sample x_t-1
              _lowerCamelCase     :		List[str] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase).prev_sample

              _lowerCamelCase     :		Union[str, Any] 						=						pred_prev_sample

          _lowerCamelCase     :		int 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		int 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5) < 1E-2
          assert abs(result_mean.item() - 0.3_2_8_4_7_4_3) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Dict 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Optional[int] 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(25)

          _lowerCamelCase     :		Dict 						=						scheduler.timesteps

          _lowerCamelCase     :		Any 						=						self.dummy_model()
          _lowerCamelCase     :		Optional[Any] 						=						self.dummy_sample_deter
          _lowerCamelCase     :		Dict 						=						torch.manual_seed(0)

          for i, t in enumerate(_UpperCamelCase):
              # 1. predict noise residual
              _lowerCamelCase     :		Any 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              if i + 1 == timesteps.shape[0]:
                  _lowerCamelCase     :		Optional[int] 						=						None
              else:
                  _lowerCamelCase     :		Any 						=						timesteps[i + 1]

              # 2. predict previous mean of sample x_t-1
              _lowerCamelCase     :		int 						=						scheduler.step(
                  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  prev_timestep=_UpperCamelCase    ,  generator=_UpperCamelCase).prev_sample

              _lowerCamelCase     :		Union[str, Any] 						=						pred_prev_sample

          _lowerCamelCase     :		Optional[int] 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3) < 1E-2
          assert abs(result_mean.item() - 0.3_3_6_2_0_3_8) < 1E-3



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


          """simple docstring"""


          pass




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


          """simple docstring"""


          pass





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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-base""")
          _lowerCamelCase     :		Optional[int] 						=						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

          _lowerCamelCase     :		Optional[Any] 						=						torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		str 						=						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():
              _lowerCamelCase     :		List[str] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						XLMRobertaModel.from_pretrained("""xlm-roberta-large""")
          _lowerCamelCase     :		Optional[Any] 						=						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

          _lowerCamelCase     :		str 						=						torch.Size((1, 12, 1024))  # batch_size, sequence_length, embedding_vector_dim
          _lowerCamelCase     :		Union[str, Any] 						=						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():
              _lowerCamelCase     :		int 						=						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))




