


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class   snake_case__ (unittest.TestCase	):
       _lowercase       							=    (ScoreSdeVeScheduler,)
       _lowercase       							=    ()







       @property
       def 		snake_case_  ( self:   Union[str, Any]				):






            '''simple docstring'''




            __UpperCamelCase   = 4
            __UpperCamelCase   = 3
            __UpperCamelCase   = 8
            __UpperCamelCase   = 8

            __UpperCamelCase   = torch.rand((batch_size, num_channels, height, width)				)

            return sample







       @property
       def 		snake_case_  ( self:   Dict				):






            '''simple docstring'''




            __UpperCamelCase   = 4
            __UpperCamelCase   = 3
            __UpperCamelCase   = 8
            __UpperCamelCase   = 8

            __UpperCamelCase   = batch_size * num_channels * height * width
            __UpperCamelCase   = torch.arange(__A				)
            __UpperCamelCase   = sample.reshape(__A,__A,__A,__A				)
            __UpperCamelCase   = sample / num_elems
            __UpperCamelCase   = sample.permute(3,0,1,2				)

            return sample







       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            def model(A_:   Any,A_:   Optional[int],*A_:   List[str]				):
                 return sample * t / (t + 1)

            return model







       def 		snake_case_  ( self:   int,**A_:   Optional[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = {
                "num_train_timesteps": 2000,
                "snr": 0.1_5,
                "sigma_min": 0.0_1,
                "sigma_max": 1348,
                "sampling_eps": 1E-5,
            }

            config.update(**__A				)
            return config







       def 		snake_case_  ( self:   Optional[int],A_:   List[Any]=0,**A_:   str				):






            '''simple docstring'''




            __UpperCamelCase   = dict(self.forward_default_kwargs				)

            for scheduler_class in self.scheduler_classes:
                 __UpperCamelCase   = self.dummy_sample
                 __UpperCamelCase   = 0.1 * sample

                 __UpperCamelCase   = self.get_scheduler_config(**__A				)
                 __UpperCamelCase   = scheduler_class(**__A				)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__A				)
                      __UpperCamelCase   = scheduler_class.from_pretrained(__A				)

                 __UpperCamelCase   = scheduler.step_pred(
                     __A,__A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample
                 __UpperCamelCase   = new_scheduler.step_pred(
                     __A,__A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample

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

                 __UpperCamelCase   = scheduler.step_correct(__A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample
                 __UpperCamelCase   = new_scheduler.step_correct(
                     __A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample

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







       def 		snake_case_  ( self:   Tuple,A_:   Union[str, Any]=0,**A_:   Optional[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = dict(self.forward_default_kwargs				)
            kwargs.update(__A				)

            for scheduler_class in self.scheduler_classes:
                 __UpperCamelCase   = self.dummy_sample
                 __UpperCamelCase   = 0.1 * sample

                 __UpperCamelCase   = self.get_scheduler_config()
                 __UpperCamelCase   = scheduler_class(**__A				)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__A				)
                      __UpperCamelCase   = scheduler_class.from_pretrained(__A				)

                 __UpperCamelCase   = scheduler.step_pred(
                     __A,__A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample
                 __UpperCamelCase   = new_scheduler.step_pred(
                     __A,__A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample

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

                 __UpperCamelCase   = scheduler.step_correct(__A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample
                 __UpperCamelCase   = new_scheduler.step_correct(
                     __A,__A,generator=torch.manual_seed(0				),**__A				).prev_sample

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







       def 		snake_case_  ( self:   Any				):






            '''simple docstring'''




            for timesteps in [10, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=__A				)







       def 		snake_case_  ( self:   Optional[Any]				):






            '''simple docstring'''




            for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1],[1, 100, 1000]				):
                 self.check_over_configs(sigma_min=__A,sigma_max=__A				)







       def 		snake_case_  ( self:   Optional[int]				):






            '''simple docstring'''




            for t in [0.1, 0.5, 0.7_5]:
                 self.check_over_forward(time_step=__A				)







       def 		snake_case_  ( self:   Tuple				):






            '''simple docstring'''




            __UpperCamelCase   = dict(self.forward_default_kwargs				)

            __UpperCamelCase   = self.scheduler_classes[0]
            __UpperCamelCase   = self.get_scheduler_config()
            __UpperCamelCase   = scheduler_class(**__A				)

            __UpperCamelCase   = 3

            __UpperCamelCase   = self.dummy_model()
            __UpperCamelCase   = self.dummy_sample_deter

            scheduler.set_sigmas(__A				)
            scheduler.set_timesteps(__A				)
            __UpperCamelCase   = torch.manual_seed(0				)

            for i, t in enumerate(scheduler.timesteps				):
                 __UpperCamelCase   = scheduler.sigmas[i]

                 for _ in range(scheduler.config.correct_steps				):
                      with torch.no_grad():
                           __UpperCamelCase   = model(__A,__A				)
                      __UpperCamelCase   = scheduler.step_correct(__A,__A,generator=__A,**__A				).prev_sample

                 with torch.no_grad():
                      __UpperCamelCase   = model(__A,__A				)

                 __UpperCamelCase   = scheduler.step_pred(__A,__A,__A,generator=__A,**__A				)
                 __UpperCamelCase   = output.prev_sample, output.prev_sample_mean

            __UpperCamelCase   = torch.sum(torch.abs(__A				)				)
            __UpperCamelCase   = torch.mean(torch.abs(__A				)				)

            assert np.isclose(result_sum.item(),143_7275_8528.0				)
            assert np.isclose(result_mean.item(),1871_4530.0				)







       def 		snake_case_  ( self:   List[Any]				):






            '''simple docstring'''




            __UpperCamelCase   = dict(self.forward_default_kwargs				)

            __UpperCamelCase   = kwargs.pop('num_inference_steps',__A				)

            for scheduler_class in self.scheduler_classes:
                 __UpperCamelCase   = self.get_scheduler_config()
                 __UpperCamelCase   = scheduler_class(**__A				)

                 __UpperCamelCase   = self.dummy_sample
                 __UpperCamelCase   = 0.1 * sample

                 if num_inference_steps is not None and hasattr(__A,'set_timesteps'				):
                      scheduler.set_timesteps(__A				)
                 elif num_inference_steps is not None and not hasattr(__A,'set_timesteps'				):
                      __UpperCamelCase   = num_inference_steps

                 __UpperCamelCase   = scheduler.step_pred(__A,0,__A,generator=torch.manual_seed(0				),**__A				).prev_sample
                 __UpperCamelCase   = scheduler.step_pred(__A,1,__A,generator=torch.manual_seed(0				),**__A				).prev_sample

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






import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__snake_case             =	{0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
__snake_case             =	set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))






def 		_A		(		_lowercase				,     _lowercase=0				,     _lowercase=None   )				->   Union[str, Any]:


     """simple docstring"""


     return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]






def 		_A		(		_lowercase				,     _lowercase=False   )				->   Tuple:


     """simple docstring"""


     if make_sub_answers:
          # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
          # *e.g.* if the correct answer contains a prefix such as "the", or "a"
          __UpperCamelCase   = (
              given_answers + get_sub_answers(_lowercase				,     begin=1   ) + get_sub_answers(_lowercase				,     end=-1   )
          )
     __UpperCamelCase   = []
     for answer in given_answers:
          __UpperCamelCase   = answer.replace('_'				,     ' '   ).lower()
          __UpperCamelCase   = ''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
          answers.append(' '.join(alias.split()   ).strip()   )
     return set(_lowercase   )






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase=1				,     _lowercase=1_00   )				->   Any:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )
     __UpperCamelCase,						__UpperCamelCase   = jax.lax.top_k(_lowercase				,     _lowercase   )

     __UpperCamelCase   = best_end_idx[:, None] - best_start_idx[None, :]
     __UpperCamelCase   = jnp.logical_or(widths < 0				,     widths > max_size   )
     __UpperCamelCase   = (best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
     __UpperCamelCase   = jnp.argmax(_lowercase   ).item()

     return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]






def 		_A		(		_lowercase   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = sample['question']['text']
     __UpperCamelCase   = sample['document']['tokens']['token']
     __UpperCamelCase   = sample['document']['tokens']['is_html']
     __UpperCamelCase   = sample['annotations']['long_answer']
     __UpperCamelCase   = sample['annotations']['short_answers']

     __UpperCamelCase   = ' '.join([context[i] for i in range(len(_lowercase   )   ) if not is_html[i]]   )

     # 0 - No ; 1 - Yes
     for answer in sample["annotations"]["yes_no_answer"]:
          if answer == 0 or answer == 1:
               return {
                   "question": question,
                   "context": context_string,
                   "short": [],
                   "long": [],
                   "category": "no" if answer == 0 else "yes",
               }

     __UpperCamelCase   = []
     for s in short_answers:
          short_targets.extend(s['text']   )
     __UpperCamelCase   = list(set(_lowercase   )   )

     __UpperCamelCase   = []
     for s in long_answers:
          if s["start_token"] == -1:
               continue
          __UpperCamelCase   = context[s['start_token'] : s['end_token']]
          __UpperCamelCase   = is_html[s['start_token'] : s['end_token']]
          __UpperCamelCase   = ' '.join([answer[i] for i in range(len(_lowercase   )   ) if not html[i]]   )
          if new_answer not in long_targets:
               long_targets.append(_lowercase   )

     __UpperCamelCase   = 'long_short' if len(short_targets + long_targets   ) > 0 else 'null'

     return {
         "question": question,
         "context": context_string,
         "short": short_targets,
         "long": long_targets,
         "category": category,
     }






def 		_A		(		)				->   Optional[int]:


     """simple docstring"""


     __UpperCamelCase   = load_from_disk('natural-questions-validation'   )
     __UpperCamelCase   = dataset.map(_lowercase   ).remove_columns(['annotations', 'document', 'id']   )
     print(_lowercase   )

     __UpperCamelCase   = dataset.filter(lambda _lowercase   : (len(x['question']   ) + len(x['context']   )) < 4 * 40_96   )
     __UpperCamelCase   = short_validation_dataset.filter(lambda _lowercase   : x["category"] != "null"   )
     short_validation_dataset

     __UpperCamelCase   = 'vasudevgupta/flax-bigbird-natural-questions'
     __UpperCamelCase   = FlaxBigBirdForNaturalQuestions.from_pretrained(_lowercase   )
     __UpperCamelCase   = BigBirdTokenizerFast.from_pretrained(_lowercase   )

     @jax.jit
     def forward(*_lowercase				,     **_lowercase   ):
          __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = model(*_lowercase				,     **_lowercase   )
          return start_logits, end_logits, jnp.argmax(_lowercase				,     axis=-1   )

     def evaluate(_lowercase   ):
          # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
          __UpperCamelCase   = tokenizer(
              example['question']				,     example['context']				,     return_tensors='np'				,     max_length=40_96				,     padding='max_length'				,     truncation=_lowercase				,     )

          __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase   = forward(**_lowercase   )

          __UpperCamelCase   = CATEGORY_MAPPING[category.item()]

          __UpperCamelCase   = example['long'] + example['short']
          if example["category"] in ["yes", "no", "null"]:
               __UpperCamelCase   = [example['category']]
          __UpperCamelCase   = example['category'] != 'null'
          # Now target can be: "yes", "no", "null", "list of long & short answers"

          if predicted_category in ["yes", "no", "null"]:
               __UpperCamelCase   = [predicted_category]
               __UpperCamelCase   = example['output'] == example['targets']
               __UpperCamelCase   = predicted_category != 'null'
               return example

          __UpperCamelCase   = 38 if predicted_category == 'short' else 10_24
          __UpperCamelCase,						__UpperCamelCase   = get_best_valid_start_end_idx(
              start_scores[0]				,     end_scores[0]				,     top_k=8				,     max_size=_lowercase   )

          __UpperCamelCase   = inputs['input_ids'][0].tolist()
          __UpperCamelCase   = [tokenizer.decode(input_ids[start_score : end_score + 1]   )]

          __UpperCamelCase   = expand_to_aliases(example['targets']				,     make_sub_answers=_lowercase   )
          __UpperCamelCase   = expand_to_aliases(example['output']   )

          # some preprocessing to both prediction and answer
          __UpperCamelCase   = {''.join(a.split()   ) for a in answers}
          __UpperCamelCase   = {''.join(p.split()   ) for p in predictions}
          __UpperCamelCase   = {s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

          # if there is a common element, it's a exact match
          __UpperCamelCase   = len(list(answers & predictions   )   ) > 0
          __UpperCamelCase   = predicted_category != 'null' and len(_lowercase   ) > 0

          return example

     __UpperCamelCase   = short_validation_dataset.map(_lowercase   )

     __UpperCamelCase   = len(_lowercase   )
     __UpperCamelCase   = len(short_validation_dataset.filter(lambda _lowercase   : x["match"] == 1   )   )
     print('EM score:'				,     (matched / total) * 1_00				,     '%'   )


if __name__ == "__main__":
    main()


