




'''simple docstring'''





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class     lowercase  ( unittest.TestCase):
      # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
      __lowercase					=	(ScoreSdeVeScheduler,)
      __lowercase					=	()






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Any							    =			4
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			3
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			8
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			8

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.rand((batch_size, num_channels, height, width)       )

         return sample






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Any							    =			4
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			3
         SCREAMING_SNAKE_CASE__      :					Dict							    =			8
         SCREAMING_SNAKE_CASE__      :					Dict							    =			8

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			batch_size * num_channels * height * width
         SCREAMING_SNAKE_CASE__      :					Any							    =			torch.arange(a__       )
         SCREAMING_SNAKE_CASE__      :					int							    =			sample.reshape(a__			,	a__			,	a__			,	a__       )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			sample / num_elems
         SCREAMING_SNAKE_CASE__      :					str							    =			sample.permute(3			,	0			,	1			,	2       )

         return sample






      def  A__(self       )							-> Tuple:
         def model(a__			,	a__			,	*a__       ):
            return sample * t / (t + 1)

         return model






      def  A__(self			,	**a__       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			{
             '''num_train_timesteps''': 2_0_0_0,
             '''snr''': 0.15,
             '''sigma_min''': 0.01,
             '''sigma_max''': 1_3_4_8,
             '''sampling_eps''': 1E-5,
         }

         config.update(**a__       )
         return config






      def  A__(self			,	a__=0			,	**a__       )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			dict(self.forward_default_kwargs       )

         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.dummy_sample
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			0.1 * sample

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.get_scheduler_config(**a__       )
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			scheduler_class(**a__       )

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__       )
               SCREAMING_SNAKE_CASE__      :					List[str]							    =			scheduler_class.from_pretrained(a__       )

            SCREAMING_SNAKE_CASE__      :					Tuple							    =			scheduler.step_pred(
                a__			,	a__			,	a__			,	generator=torch.manual_seed(0       )			,	**a__       ).prev_sample
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			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"

            SCREAMING_SNAKE_CASE__      :					Tuple							    =			scheduler.step_correct(a__			,	a__			,	generator=torch.manual_seed(0       )			,	**a__       ).prev_sample
            SCREAMING_SNAKE_CASE__      :					str							    =			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  A__(self			,	a__=0			,	**a__       )							-> int:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			dict(self.forward_default_kwargs       )
         kwargs.update(a__       )

         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__      :					Dict							    =			self.dummy_sample
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			0.1 * sample

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__      :					Any							    =			scheduler_class(**a__       )

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__       )
               SCREAMING_SNAKE_CASE__      :					Dict							    =			scheduler_class.from_pretrained(a__       )

            SCREAMING_SNAKE_CASE__      :					Tuple							    =			scheduler.step_pred(
                a__			,	a__			,	a__			,	generator=torch.manual_seed(0       )			,	**a__       ).prev_sample
            SCREAMING_SNAKE_CASE__      :					int							    =			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"

            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			scheduler.step_correct(a__			,	a__			,	generator=torch.manual_seed(0       )			,	**a__       ).prev_sample
            SCREAMING_SNAKE_CASE__      :					int							    =			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  A__(self       )							-> Optional[Any]:
         for timesteps in [1_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=a__       )






      def  A__(self       )							-> Optional[int]:
         for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]			,	[1, 1_0_0, 1_0_0_0]       ):
            self.check_over_configs(sigma_min=a__			,	sigma_max=a__       )






      def  A__(self       )							-> str:
         for t in [0.1, 0.5, 0.75]:
            self.check_over_forward(time_step=a__       )






      def  A__(self       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dict(self.forward_default_kwargs       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE__      :					str							    =			self.get_scheduler_config()
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			scheduler_class(**a__       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			3

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.dummy_model()
         SCREAMING_SNAKE_CASE__      :					str							    =			self.dummy_sample_deter

         scheduler.set_sigmas(a__       )
         scheduler.set_timesteps(a__       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.manual_seed(0       )

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

            for _ in range(scheduler.config.correct_steps       ):
               with torch.no_grad():
                  SCREAMING_SNAKE_CASE__      :					List[str]							    =			model(a__			,	a__       )
               SCREAMING_SNAKE_CASE__      :					str							    =			scheduler.step_correct(a__			,	a__			,	generator=a__			,	**a__       ).prev_sample

            with torch.no_grad():
               SCREAMING_SNAKE_CASE__      :					Dict							    =			model(a__			,	a__       )

            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			scheduler.step_pred(a__			,	a__			,	a__			,	generator=a__			,	**a__       )
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			output.prev_sample, output.prev_sample_mean

         SCREAMING_SNAKE_CASE__      :					Any							    =			torch.sum(torch.abs(a__       )       )
         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.mean(torch.abs(a__       )       )

         assert np.isclose(result_sum.item()			,	1_43_72_75_85_28.0       )
         assert np.isclose(result_mean.item()			,	18_71_45_30.0       )






      def  A__(self       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					str							    =			dict(self.forward_default_kwargs       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			kwargs.pop('''num_inference_steps'''			,	a__       )

         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__      :					Dict							    =			self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__      :					str							    =			scheduler_class(**a__       )

            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.dummy_sample
            SCREAMING_SNAKE_CASE__      :					Any							    =			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'''       ):
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			num_inference_steps

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			scheduler.step_pred(a__			,	0			,	a__			,	generator=torch.manual_seed(0       )			,	**a__       ).prev_sample
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			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       )







'''simple docstring'''





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

from transformers import BigBirdTokenizerFast


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


def 					UpperCamelCase						(	snake_case						,  snake_case=0						,  snake_case=None ):




   '''simple docstring'''

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


def 					UpperCamelCase						(	snake_case						,  snake_case=False ):




   '''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"
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			(
          given_answers + get_sub_answers(snake_case						,  begin=1 ) + get_sub_answers(snake_case						,  end=-1 )
      )
   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for answer in given_answers:
      SCREAMING_SNAKE_CASE__      :					int							    =			answer.replace('''_'''						,  ''' ''' ).lower()
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias )
      answers.append(''' '''.join(alias.split() ).strip() )
   return set(snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case=1						,  snake_case=1_00 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			jax.lax.top_k(snake_case						,  snake_case )
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			jax.lax.top_k(snake_case						,  snake_case )

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			best_end_idx[:, None] - best_start_idx[None, :]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			jnp.logical_or(widths < 0						,  widths > max_size )
   SCREAMING_SNAKE_CASE__      :					Any							    =			(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
   SCREAMING_SNAKE_CASE__      :					str							    =			jnp.argmax(snake_case ).item()

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


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			sample['''question''']['''text''']
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			sample['''document''']['''tokens''']['''token''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''document''']['''tokens''']['''is_html''']
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample['''annotations''']['''long_answer''']
   SCREAMING_SNAKE_CASE__      :					str							    =			sample['''annotations''']['''short_answers''']

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([context[i] for i in range(len(snake_case ) ) 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",
         }

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[]
   for s in short_answers:
      short_targets.extend(s['''text'''] )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			list(set(snake_case ) )

   SCREAMING_SNAKE_CASE__      :					Any							    =			[]
   for s in long_answers:
      if s["start_token"] == -1:
         continue
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			context[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					Dict							    =			is_html[s['''start_token'''] : s['''end_token''']]
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			''' '''.join([answer[i] for i in range(len(snake_case ) ) if not html[i]] )
      if new_answer not in long_targets:
         long_targets.append(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			'''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 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			load_from_disk('''natural-questions-validation''' )
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			dataset.map(snake_case ).remove_columns(['''annotations''', '''document''', '''id'''] )
   print(snake_case )

   SCREAMING_SNAKE_CASE__      :					Dict							    =			dataset.filter(lambda snake_case : (len(x['''question'''] ) + len(x['''context'''] )) < 4 * 40_96 )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			short_validation_dataset.filter(lambda snake_case : x["category"] != "null" )
   short_validation_dataset

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			'''vasudevgupta/flax-bigbird-natural-questions'''
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			FlaxBigBirdForNaturalQuestions.from_pretrained(snake_case )
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			BigBirdTokenizerFast.from_pretrained(snake_case )

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

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

      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			forward(**snake_case )

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			CATEGORY_MAPPING[category.item()]

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

      if predicted_category in ["yes", "no", "null"]:
         SCREAMING_SNAKE_CASE__      :					str							    =			[predicted_category]
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			example['''output'''] == example['''targets''']
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			predicted_category != '''null'''
         return example

      SCREAMING_SNAKE_CASE__      :					Any							    =			38 if predicted_category == '''short''' else 10_24
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			get_best_valid_start_end_idx(
          start_scores[0]						,  end_scores[0]						,  top_k=8						,  max_size=snake_case )

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

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			expand_to_aliases(example['''targets''']						,  make_sub_answers=snake_case )
      SCREAMING_SNAKE_CASE__      :					Any							    =			expand_to_aliases(example['''output'''] )

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

      # if there is a common element, it's a exact match
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			len(list(answers & predictions ) ) > 0
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			predicted_category != '''null''' and len(snake_case ) > 0

      return example

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			short_validation_dataset.map(snake_case )

   SCREAMING_SNAKE_CASE__      :					int							    =			len(snake_case )
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(short_validation_dataset.filter(lambda snake_case : x["match"] == 1 ) )
   print('''EM score:'''						,  (matched / total) * 1_00						,  '''%''' )


if __name__ == "__main__":
       main()

