




'''simple docstring'''





import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()






class     lowercase  ( unittest.TestCase):
      __lowercase					=	'''hf-internal-testing/tiny-stable-diffusion-pipe'''
      __lowercase					=	1
      __lowercase					=	77
      __lowercase					=	32
      __lowercase					=	4
      __lowercase					=	__lowercase					=	64
      __lowercase					=	torch.manual_seed(0)






      def  A__(self			,	a__=0.99_99       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			UNetaDConditionModel.from_pretrained(self.model_id			,	subfolder='''unet'''       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			unet.to(a__       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			EMAModel(unet.parameters()			,	decay=a__			,	model_cls=a__			,	model_config=unet.config       )
         return unet, ema_unet






      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.randn(
             self.batch_size			,	self.num_in_channels			,	self.latent_height			,	self.latent_width			,	generator=self.generator       ).to(a__       )
         SCREAMING_SNAKE_CASE__      :					int							    =			torch.randint(0			,	1_0_0_0			,	size=(self.batch_size,)			,	generator=self.generator       ).to(a__       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.randn(
             self.batch_size			,	self.prompt_length			,	self.text_encoder_hidden_dim			,	generator=self.generator       ).to(a__       )
         return noisy_latents, timesteps, encoder_hidden_states






      def  A__(self			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			{}
         for k, param in unet.state_dict().items():
            SCREAMING_SNAKE_CASE__      :					int							    =			torch.randn_like(a__       ) + (param * torch.randn_like(a__       ))
            updated_state_dict.update({k: updated_param}       )
         unet.load_state_dict(a__       )
         return unet






      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					int							    =			self.get_models()
         # Take the first (hypothetical) EMA step.
         ema_unet.step(unet.parameters()       )
         assert ema_unet.optimization_step == 1

         # Take two more.
         for _ in range(2       ):
            ema_unet.step(unet.parameters()       )
         assert ema_unet.optimization_step == 3






      def  A__(self       )							-> Any:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.get_models()
         # Since the `unet` is not being updated (i.e., backprop'd)
         # there won't be any difference between the `params` of `unet`
         # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
         ema_unet.step(unet.parameters()       )
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			list(unet.parameters()       )
         for s_param, param in zip(ema_unet.shadow_params			,	a__       ):
            assert torch.allclose(a__			,	a__       )

         # The above holds true even if we call `ema.step()` multiple times since
         # `unet` params are still not being updated.
         for _ in range(4       ):
            ema_unet.step(unet.parameters()       )
         for s_param, param in zip(ema_unet.shadow_params			,	a__       ):
            assert torch.allclose(a__			,	a__       )






      def  A__(self       )							-> Any:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			self.get_models()
         # Here we simulate the parameter updates for `unet`. Since there might
         # be some parameters which are initialized to zero we take extra care to
         # initialize their values to something non-zero before the multiplication.
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.simulate_backprop(a__       )

         # Take the EMA step.
         ema_unet.step(unet_pseudo_updated_step_one.parameters()       )

         # Now the EMA'd parameters won't be equal to the original model parameters.
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			list(unet_pseudo_updated_step_one.parameters()       )
         for s_param, param in zip(ema_unet.shadow_params			,	a__       ):
            assert ~torch.allclose(a__			,	a__       )

         # Ensure this is the case when we take multiple EMA steps.
         for _ in range(4       ):
            ema_unet.step(unet.parameters()       )
         for s_param, param in zip(ema_unet.shadow_params			,	a__       ):
            assert ~torch.allclose(a__			,	a__       )






      def  A__(self       )							-> Any:
         # If we call EMA step after a backpropagation consecutively for two times,
         # the shadow params from those two steps should be different.
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.get_models()

         # First backprop + EMA
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.simulate_backprop(a__       )
         ema_unet.step(unet_step_one.parameters()       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			ema_unet.shadow_params

         # Second backprop + EMA
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.simulate_backprop(a__       )
         ema_unet.step(unet_step_two.parameters()       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			ema_unet.shadow_params

         for step_one, step_two in zip(a__			,	a__       ):
            assert ~torch.allclose(a__			,	a__       )






      def  A__(self       )							-> Tuple:
         # If there's no decay even if there are backprops, EMA steps
         # won't take any effect i.e., the shadow params would remain the
         # same.
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.get_models(decay=0.0       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.simulate_backprop(a__       )
         ema_unet.step(unet_step_one.parameters()       )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			ema_unet.shadow_params

         SCREAMING_SNAKE_CASE__      :					Any							    =			self.simulate_backprop(a__       )
         ema_unet.step(unet_step_two.parameters()       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			ema_unet.shadow_params

         for step_one, step_two in zip(a__			,	a__       ):
            assert torch.allclose(a__			,	a__       )






      @skip_mps
      def  A__(self       )							-> str:
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.get_models()
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdir:
            ema_unet.save_pretrained(a__       )
            SCREAMING_SNAKE_CASE__      :					int							    =			UNetaDConditionModel.from_pretrained(a__			,	model_cls=a__       )
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			loaded_unet.to(unet.device       )

         # Since no EMA step has been performed the outputs should match.
         SCREAMING_SNAKE_CASE__      :					Dict							    =			unet(a__			,	a__			,	a__       ).sample
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			loaded_unet(a__			,	a__			,	a__       ).sample

         assert torch.allclose(a__			,	a__			,	atol=1E-4       )







'''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()

