code: 
  '''simple docstring'''
  
  
  
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
  from diffusers.utils.testing_utils import (
      enable_full_determinism,
      load_image,
      load_numpy,
      nightly,
      require_torch_gpu,
      skip_mps,
      torch_device,
  )
  
  from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= RePaintPipeline
    _UpperCamelCase						= IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    _UpperCamelCase						= PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    _UpperCamelCase						= IMAGE_INPAINTING_BATCH_PARAMS
    _UpperCamelCase						= False
  
    def 		_lowerCamelCase	(self  : List[Any]    )   ->       List[str]:
          torch.manual_seed(0    )
          torch.manual_seed(0    )
          snake_case_						       =							UNetaDModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, sample_size=32	, in_channels=3	, out_channels=3	, down_block_types=("DownBlock2D", "AttnDownBlock2D")	, up_block_types=("AttnUpBlock2D", "UpBlock2D")	, )
          snake_case_						       =							RePaintScheduler()
          snake_case_						       =							{"unet": unet, "scheduler": scheduler}
          return components
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Tuple=0    )   ->       Dict:
          if str(lowerCAmelCase_    ).startswith("mps"    ):
                snake_case_						       =							torch.manual_seed(lowerCAmelCase_    )
          else:
                snake_case_						       =							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
          snake_case_						       =							np.random.RandomState(lowerCAmelCase_    ).standard_normal((1, 3, 32, 32)    )
          snake_case_						       =							torch.from_numpy(lowerCAmelCase_    ).to(device=lowerCAmelCase_	, dtype=torch.floataa    )
          snake_case_						       =							(image > 0).to(device=lowerCAmelCase_	, dtype=torch.floataa    )
          snake_case_						       =							{
              "image": image,
              "mask_image": mask,
              "generator": generator,
              "num_inference_steps": 5,
              "eta": 0.0,
              "jump_length": 2,
              "jump_n_sample": 2,
              "output_type": "numpy",
          }
          return inputs
  
    def 		_lowerCamelCase	(self  : List[Any]    )   ->       Union[str, Any]:
          snake_case_						       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case_						       =							self.get_dummy_components()
          snake_case_						       =							RePaintPipeline(**lowerCAmelCase_    )
          snake_case_						       =							sd_pipe.to(lowerCAmelCase_    )
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_    )
  
          snake_case_						       =							self.get_dummy_inputs(lowerCAmelCase_    )
          snake_case_						       =							sd_pipe(**lowerCAmelCase_    ).images
          snake_case_						       =							image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 32, 32, 3)
          snake_case_						       =							np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )
  
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3
  
    @skip_mps
    def 		_lowerCamelCase	(self  : Dict    )   ->       List[Any]:
          return super().test_save_load_local()
  
    @unittest.skip("non-deterministic pipeline"    )
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Optional[int]:
          return super().test_inference_batch_single_identical()
  
    @skip_mps
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Dict:
          return super().test_dict_tuple_outputs_equivalent()
  
    @skip_mps
    def 		_lowerCamelCase	(self  : int    )   ->       Union[str, Any]:
          return super().test_save_load_optional_components()
  
  
    @skip_mps
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Any:
          return super().test_attention_slicing_forward_pass()
  
  
  
  @nightly
  @require_torch_gpu
  class  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       List[Any]:
          snake_case_						       =							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"    )
          snake_case_						       =							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"    )
          snake_case_						       =							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"    )
  
          snake_case_						       =							"google/ddpm-ema-celebahq-256"
          snake_case_						       =							UNetaDModel.from_pretrained(lowerCAmelCase_    )
          snake_case_						       =							RePaintScheduler.from_pretrained(lowerCAmelCase_    )
  
          snake_case_						       =							RePaintPipeline(unet=lowerCAmelCase_	, scheduler=lowerCAmelCase_    ).to(lowerCAmelCase_    )
          repaint.set_progress_bar_config(disable=lowerCAmelCase_    )
          repaint.enable_attention_slicing()
  
          snake_case_						       =							torch.manual_seed(0    )
          snake_case_						       =							repaint(
              lowerCAmelCase_	, lowerCAmelCase_	, num_inference_steps=2_50	, eta=0.0	, jump_length=10	, jump_n_sample=10	, generator=lowerCAmelCase_	, output_type="np"	, )
          snake_case_						       =							output.images[0]
  
          assert image.shape == (2_56, 2_56, 3)
          assert np.abs(expected_image - image    ).mean() < 1E-2
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  import jax
  import jax.numpy as jnp
  from bigbird_flax import FlaxBigBirdForNaturalQuestions
  from datasets import load_from_disk
  
  from transformers import BigBirdTokenizerFast
  
  
  A           =   {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"}
  A           =   set("".join(["‘", "’", "´", "`", ".", ",", "-", "\""]))
  def   snake_case_       (							_a     :							Any ,					_a     :							Union[str, Any]=0 ,					_a     :							Optional[int]=None			):
        return [" ".join(x.split(" "			)[begin:end]			) for x in answers if len(x.split(" "			)			) > 1]
  def   snake_case_       (							_a     :							Optional[int] ,					_a     :							str=False			):
        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"
              snake_case_						       =							(
                  given_answers + get_sub_answers(_a ,					begin=1			) + get_sub_answers(_a ,					end=-1			)
              )
        snake_case_						       =							[]
        for answer in given_answers:
              snake_case_						       =							answer.replace("_" ,					" "			).lower()
              snake_case_						       =							"".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias			)
              answers.append(" ".join(alias.split()			).strip()			)
        return set(_a			)
  def   snake_case_       (							_a     :							Optional[Any] ,					_a     :							int ,					_a     :							Optional[Any]=1 ,					_a     :							List[Any]=100			):
        snake_case_       ,	snake_case_						       =							jax.lax.top_k(_a ,					_a			)
        snake_case_       ,	snake_case_						       =							jax.lax.top_k(_a ,					_a			)
  
        snake_case_						       =							best_end_idx[:, None] - best_start_idx[None, :]
        snake_case_						       =							jnp.logical_or(widths < 0 ,					widths > max_size			)
        snake_case_						       =							(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
        snake_case_						       =							jnp.argmax(_a			).item()
  
        return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
  def   snake_case_       (							_a     :							Any			):
        snake_case_						       =							sample["question"]["text"]
        snake_case_						       =							sample["document"]["tokens"]["token"]
        snake_case_						       =							sample["document"]["tokens"]["is_html"]
        snake_case_						       =							sample["annotations"]["long_answer"]
        snake_case_						       =							sample["annotations"]["short_answers"]
  
        snake_case_						       =							" ".join([context[i] for i in range(len(_a			)			) 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",
                    }
  
        snake_case_						       =							[]
        for s in short_answers:
              short_targets.extend(s["text"]			)
        snake_case_						       =							list(set(_a			)			)
  
        snake_case_						       =							[]
        for s in long_answers:
              if s["start_token"] == -1:
                    continue
              snake_case_						       =							context[s["start_token"] : s["end_token"]]
              snake_case_						       =							is_html[s["start_token"] : s["end_token"]]
              snake_case_						       =							" ".join([answer[i] for i in range(len(_a			)			) if not html[i]]			)
              if new_answer not in long_targets:
                    long_targets.append(_a			)
  
        snake_case_						       =							"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   snake_case_       (							):
        snake_case_						       =							load_from_disk("natural-questions-validation"			)
        snake_case_						       =							dataset.map(_a			).remove_columns(["annotations", "document", "id"]			)
        print(_a			)
  
        snake_case_						       =							dataset.filter(lambda _a			: (len(x["question"]			) + len(x["context"]			)) < 4 * 4_096			)
        snake_case_						       =							short_validation_dataset.filter(lambda _a			: x["category"] != "null"			)
        short_validation_dataset
  
        snake_case_						       =							"vasudevgupta/flax-bigbird-natural-questions"
        snake_case_						       =							FlaxBigBirdForNaturalQuestions.from_pretrained(_a			)
        snake_case_						       =							BigBirdTokenizerFast.from_pretrained(_a			)
  
        @jax.jit
        def forward(*_a     :							List[Any] ,					**_a     :							int			):
              snake_case_       ,	snake_case_       ,	snake_case_						       =							model(*_a ,					**_a			)
              return start_logits, end_logits, jnp.argmax(_a ,					axis=-1			)
  
        def evaluate(_a     :							List[Any]			):
              # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
              snake_case_						       =							tokenizer(
                  example["question"] ,					example["context"] ,					return_tensors="np" ,					max_length=4_096 ,					padding="max_length" ,					truncation=_a ,					)
  
              snake_case_       ,	snake_case_       ,	snake_case_						       =							forward(**_a			)
  
              snake_case_						       =							CATEGORY_MAPPING[category.item()]
  
              snake_case_						       =							example["long"] + example["short"]
              if example["category"] in ["yes", "no", "null"]:
                    snake_case_						       =							[example["category"]]
              snake_case_						       =							example["category"] != "null"
              # Now target can be: "yes", "no", "null", "list of long & short answers"
  
              if predicted_category in ["yes", "no", "null"]:
                    snake_case_						       =							[predicted_category]
                    snake_case_						       =							example["output"] == example["targets"]
                    snake_case_						       =							predicted_category != "null"
                    return example
  
              snake_case_						       =							38 if predicted_category == "short" else 1_024
              snake_case_       ,	snake_case_						       =							get_best_valid_start_end_idx(
                  start_scores[0] ,					end_scores[0] ,					top_k=8 ,					max_size=_a			)
  
              snake_case_						       =							inputs["input_ids"][0].tolist()
              snake_case_						       =							[tokenizer.decode(input_ids[start_score : end_score + 1]			)]
  
              snake_case_						       =							expand_to_aliases(example["targets"] ,					make_sub_answers=_a			)
              snake_case_						       =							expand_to_aliases(example["output"]			)
  
              # some preprocessing to both prediction and answer
              snake_case_						       =							{"".join(a.split()			) for a in answers}
              snake_case_						       =							{"".join(p.split()			) for p in predictions}
              snake_case_						       =							{s for s in predictions if s not in ["``", "''", "`", "'"]}
  
              # if there is a common element, it's a exact match
              snake_case_						       =							len(list(answers & predictions			)			) > 0
              snake_case_						       =							predicted_category != "null" and len(_a			) > 0
  
              return example
  
        snake_case_						       =							short_validation_dataset.map(_a			)
  
        snake_case_						       =							len(_a			)
        snake_case_						       =							len(short_validation_dataset.filter(lambda _a			: x["match"] == 1			)			)
        print("EM score:" ,					(matched / total) * 100 ,					"%"			)
  
  
  if __name__ == "__main__":
       main()
  
  