






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=(KDPMaAncestralDiscreteScheduler,)
      UpperCamelCase_    : List[Any]									=10
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:
          UpperCamelCase	:List[Any]     							=       {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**SCREAMING_SNAKE_CASE_			)
          return config
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Any:
          for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]   ,    [0.0002, 0.002, 0.02]			):
              self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_   ,    beta_end=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				int:
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          if torch_device == "mps":
              return
          UpperCamelCase	:List[str]     							=       self.scheduler_classes[0]
          UpperCamelCase	:Optional[Any]     							=       self.get_scheduler_config()
          UpperCamelCase	:Dict     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps			)

          UpperCamelCase	:Any     							=       torch.manual_seed(0			)

          UpperCamelCase	:Optional[Any]     							=       self.dummy_model()
          UpperCamelCase	:str     							=       self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCamelCase	:Any     							=       sample.to(SCREAMING_SNAKE_CASE_			)

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase	:Optional[int]     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:List[str]     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Union[str, Any]     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:Optional[int]     							=       output.prev_sample

          UpperCamelCase	:Optional[Any]     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Any     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 1_3849.3877			) < 1e-2
          assert abs(result_mean.item() - 18.0331			) < 5e-3
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          if torch_device == "mps":
              return
          UpperCamelCase	:Union[str, Any]     							=       self.scheduler_classes[0]
          UpperCamelCase	:Union[str, Any]     							=       self.get_scheduler_config(prediction_type='''v_prediction'''			)
          UpperCamelCase	:Optional[Any]     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps			)

          UpperCamelCase	:int     							=       self.dummy_model()
          UpperCamelCase	:int     							=       self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCamelCase	:Union[str, Any]     							=       sample.to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:int     							=       torch.manual_seed(0			)

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase	:Optional[int]     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Any     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:int     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:List[str]     							=       output.prev_sample

          UpperCamelCase	:Any     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Union[str, Any]     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 328.9970			) < 1e-2
          assert abs(result_mean.item() - 0.4284			) < 1e-3




      def    UpperCAmelCase						(    self			)    ->				Any:
          if torch_device == "mps":
              return
          UpperCamelCase	:Any     							=       self.scheduler_classes[0]
          UpperCamelCase	:List[Any]     							=       self.get_scheduler_config()
          UpperCamelCase	:int     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps   ,    device=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       torch.manual_seed(0			)

          UpperCamelCase	:int     							=       self.dummy_model()
          UpperCamelCase	:List[str]     							=       self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

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

              UpperCamelCase	:List[str]     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Any     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:int     							=       output.prev_sample

          UpperCamelCase	:int     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Optional[int]     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 1_3849.3818			) < 1e-1
          assert abs(result_mean.item() - 18.0331			) < 1e-3













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       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

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





