

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest



@require_tokenizers
class  __snake_case					(      lowerCAmelCase__    ):
		__lowerCAmelCase       :		str  	=					DPRContextEncoderTokenizer
		__lowerCAmelCase       :		Any  	=					DPRContextEncoderTokenizerFast
		__lowerCAmelCase       :		str  	=					True






@require_tokenizers
class  __snake_case					(      lowerCAmelCase__    ):
		__lowerCAmelCase       :		Union[str, Any]  	=					DPRQuestionEncoderTokenizer
		__lowerCAmelCase       :		List[Any]  	=					DPRQuestionEncoderTokenizerFast
		__lowerCAmelCase       :		Union[str, Any]  	=					True






@require_tokenizers
class  __snake_case					(      lowerCAmelCase__    ):
		__lowerCAmelCase       :		Tuple  	=					DPRReaderTokenizer
		__lowerCAmelCase       :		Dict  	=					DPRReaderTokenizerFast
		__lowerCAmelCase       :		str  	=					True

		@slow
		def   lowerCAmelCase__       (   self):
						SCREAMING_SNAKE_CASE_													=  self.tokenizer_class.from_pretrained('bert-base-uncased')

						SCREAMING_SNAKE_CASE_													=  tokenizer.encode('question sequence'  ,						add_special_tokens=_A)
						SCREAMING_SNAKE_CASE_													=  tokenizer.encode('title sequence'  ,						add_special_tokens=_A)
						SCREAMING_SNAKE_CASE_													=  tokenizer.encode('text sequence ' * 4  ,						add_special_tokens=_A)
						SCREAMING_SNAKE_CASE_													=  [[101] + text_a + [102] + text_a + [102] + text_a]
						SCREAMING_SNAKE_CASE_													=  BatchEncoding({'input_ids': input_ids})

						SCREAMING_SNAKE_CASE_													=  [[0] * len(input_ids[0])]
						SCREAMING_SNAKE_CASE_													=  [[0] * len(input_ids[0])]
						SCREAMING_SNAKE_CASE_													=  [0]
						SCREAMING_SNAKE_CASE_													=  DPRReaderOutput(_A  ,						_A  ,						_A)

						SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  8, 9
						SCREAMING_SNAKE_CASE_													=  10
						SCREAMING_SNAKE_CASE_													=  10
						SCREAMING_SNAKE_CASE_													=  tokenizer.decode_best_spans(_A  ,						_A)
						self.assertEqual(predicted_spans[0].start_index  ,						_A)
						self.assertEqual(predicted_spans[0].end_index  ,						_A)
						self.assertEqual(predicted_spans[0].doc_id  ,						0)


		@slow
		def   lowerCAmelCase__       (   self):
						SCREAMING_SNAKE_CASE_													=  self.tokenizer_class.from_pretrained('bert-base-uncased')

						SCREAMING_SNAKE_CASE_													=  tokenizer.encode('question sequence'  ,						add_special_tokens=_A)
						SCREAMING_SNAKE_CASE_													=  tokenizer.encode('title sequence'  ,						add_special_tokens=_A)
						SCREAMING_SNAKE_CASE_													=  tokenizer.encode('text sequence'  ,						add_special_tokens=_A)
						SCREAMING_SNAKE_CASE_													=  [101] + text_a + [102] + text_a + [102] + text_a
						SCREAMING_SNAKE_CASE_													=  tokenizer(questions=['question sequence']  ,						titles=['title sequence']  ,						texts=['text sequence'])
						self.assertIn('input_ids'  ,						_A)
						self.assertIn('attention_mask'  ,						_A)
						self.assertListEqual(encoded_input['input_ids'][0]  ,						_A)





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					(HeunDiscreteScheduler,)
  __lowerCAmelCase       :		Optional[int]  	=					10

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A  ,						beta_end=_A)

  def   lowerCAmelCase__       (   self):
      for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_A)

  def   lowerCAmelCase__       (   self):
      for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_A  ,						clip_sample=_A)

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if str(_A).startswith('cpu'):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      elif str(_A).startswith('mps'):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-2
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A  ,						use_karras_sigmas=_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      assert abs(result_sum.item() - 0.0_0_0_1_5) < 1E-2
      assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07) < 1E-2


