


'''simple docstring'''







import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :str												=(DDPMParallelScheduler,)

  def __a						(					self     :							List[Any]				,							**SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       __a       =   {
           """num_train_timesteps""": 1_0_0_0,
           """beta_start""": 0.0_0_0_1,
           """beta_end""": 0.0_2,
           """beta_schedule""": """linear""",
           """variance_type""": """fixed_small""",
           """clip_sample""": True,
       }

       config.update(**SCREAMING_SNAKE_CASE__						)
       return config

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,							[0.0_0_2, 0.0_2, 0.2, 2]						):
            self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__				,							beta_end=SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str						):





       '''simple docstring'''

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

  def __a						(					self     :							int						):





       '''simple docstring'''

       for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

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

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__						)
       for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=SCREAMING_SNAKE_CASE__				,							prediction_type=SCREAMING_SNAKE_CASE__				,							sample_max_value=SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

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

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       for t in [0, 5_0_0, 9_9_9]:
            self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						) - 0.0_0_9_7_9						)						) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.0_2						)						) < 1E-5

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   len(SCREAMING_SNAKE_CASE__						)

       __a       =   self.dummy_model()
       __a       =   self.dummy_sample_deter
       __a       =   self.dummy_sample_deter + 0.1
       __a       =   self.dummy_sample_deter - 0.1

       __a       =   samplea.shape[0]
       __a       =   torch.stack([samplea, samplea, samplea]				,							dim=0						)
       __a       =   torch.arange(SCREAMING_SNAKE_CASE__						)[0:3, None].repeat(1				,							SCREAMING_SNAKE_CASE__						)

       __a       =   model(samples.flatten(0				,							1						)				,							timesteps.flatten(0				,							1						)						)
       __a       =   scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__				,							timesteps.flatten(0				,							1						)				,							samples.flatten(0				,							1						)						)

       __a       =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE__						)						)
       __a       =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE__						)						)

       assert abs(result_sum.item() - 1_1_5_3.1_8_3_3						) < 1E-2
       assert abs(result_mean.item() - 0.5_0_0_5						) < 1E-3

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   len(SCREAMING_SNAKE_CASE__						)

       __a       =   self.dummy_model()
       __a       =   self.dummy_sample_deter
       __a       =   torch.manual_seed(0						)

       for t in reversed(range(SCREAMING_SNAKE_CASE__						)						):
            # 1. predict noise residual
            __a       =   model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

            # 2. predict previous mean of sample x_t-1
            __a       =   scheduler.step(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__						).prev_sample

            __a       =   pred_prev_sample

       __a       =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE__						)						)
       __a       =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE__						)						)

       assert abs(result_sum.item() - 2_5_8.9_6_0_6						) < 1E-2
       assert abs(result_mean.item() - 0.3_3_7_2						) < 1E-3

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config(prediction_type="""v_prediction"""						)
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   len(SCREAMING_SNAKE_CASE__						)

       __a       =   self.dummy_model()
       __a       =   self.dummy_sample_deter
       __a       =   torch.manual_seed(0						)

       for t in reversed(range(SCREAMING_SNAKE_CASE__						)						):
            # 1. predict noise residual
            __a       =   model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

            # 2. predict previous mean of sample x_t-1
            __a       =   scheduler.step(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__						).prev_sample

            __a       =   pred_prev_sample

       __a       =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE__						)						)
       __a       =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE__						)						)

       assert abs(result_sum.item() - 2_0_2.0_2_9_6						) < 1E-2
       assert abs(result_mean.item() - 0.2_6_3_1						) < 1E-3

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   [1_0_0, 8_7, 5_0, 1, 0]

       scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__						)

       __a       =   scheduler.timesteps

       for i, timestep in enumerate(SCREAMING_SNAKE_CASE__						):
            if i == len(SCREAMING_SNAKE_CASE__						) - 1:
                 __a       =   -1
            else:
                 __a       =   timesteps[i + 1]

            __a       =   scheduler.previous_timestep(SCREAMING_SNAKE_CASE__						)
            __a       =   prev_t.item()

            self.assertEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   [1_0_0, 8_7, 5_0, 5_1, 0]

       with self.assertRaises(SCREAMING_SNAKE_CASE__				,							msg="""`custom_timesteps` must be in descending order."""						):
            scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   [1_0_0, 8_7, 5_0, 1, 0]
       __a       =   len(SCREAMING_SNAKE_CASE__						)

       with self.assertRaises(SCREAMING_SNAKE_CASE__				,							msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""						):
            scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE__				,							timesteps=SCREAMING_SNAKE_CASE__						)


  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   self.scheduler_classes[0]
       __a       =   self.get_scheduler_config()
       __a       =   scheduler_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           SCREAMING_SNAKE_CASE__				,							msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""				,							):
            scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__						)










'''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  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   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():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   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():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






