code: 
  '''simple docstring'''
  
  
  
  
  
  import gc
  import random
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      ControlNetModel,
      DDIMScheduler,
      StableDiffusionControlNetInpaintPipeline,
      UNetaDConditionModel,
  )
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
  from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import (
      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
      TEXT_TO_IMAGE_IMAGE_PARAMS,
  )
  from ..test_pipelines_common import (
      PipelineKarrasSchedulerTesterMixin,
      PipelineLatentTesterMixin,
      PipelineTesterMixin,
  )
  
  
  enable_full_determinism()
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= StableDiffusionControlNetInpaintPipeline
    _UpperCamelCase						= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _UpperCamelCase						= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _UpperCamelCase						= frozenset({'control_image'}       )  # skip `image` and `mask` for now, only test for control_image
    _UpperCamelCase						= TEXT_TO_IMAGE_IMAGE_PARAMS
  
    def 		_lowerCamelCase	(self  : Tuple    )   ->       Tuple:
          torch.manual_seed(0    )
          snake_case_						       =							UNetaDConditionModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, sample_size=32	, in_channels=9	, out_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	, cross_attention_dim=32	, )
          torch.manual_seed(0    )
          snake_case_						       =							ControlNetModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, in_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, cross_attention_dim=32	, conditioning_embedding_out_channels=(16, 32)	, )
          torch.manual_seed(0    )
          snake_case_						       =							DDIMScheduler(
              beta_start=0.00_085	, beta_end=0.012	, beta_schedule="scaled_linear"	, clip_sample=lowerCAmelCase_	, set_alpha_to_one=lowerCAmelCase_	, )
          torch.manual_seed(0    )
          snake_case_						       =							AutoencoderKL(
              block_out_channels=[32, 64]	, in_channels=3	, out_channels=3	, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	, latent_channels=4	, )
          torch.manual_seed(0    )
          snake_case_						       =							CLIPTextConfig(
              bos_token_id=0	, eos_token_id=2	, hidden_size=32	, intermediate_size=37	, layer_norm_eps=1E-05	, num_attention_heads=4	, num_hidden_layers=5	, pad_token_id=1	, vocab_size=10_00	, )
          snake_case_						       =							CLIPTextModel(lowerCAmelCase_    )
          snake_case_						       =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
  
          snake_case_						       =							{
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components
  
    def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Tuple=0    )   ->       List[Any]:
          if str(lowerCAmelCase_    ).startswith("mps"    ):
                snake_case_						       =							torch.manual_seed(lowerCAmelCase_    )
          else:
                snake_case_						       =							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
  
          snake_case_						       =							2
          snake_case_						       =							randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	, generator=lowerCAmelCase_	, device=torch.device(lowerCAmelCase_    )	, )
          snake_case_						       =							floats_tensor((1, 3, 32, 32)	, rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
          snake_case_						       =							init_image.cpu().permute(0	, 2	, 3	, 1    )[0]
  
          snake_case_						       =							Image.fromarray(np.uinta(lowerCAmelCase_    )    ).convert("RGB"    ).resize((64, 64)    )
          snake_case_						       =							Image.fromarray(np.uinta(init_image + 4    )    ).convert("RGB"    ).resize((64, 64)    )
  
          snake_case_						       =							{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
              "image": image,
              "mask_image": mask_image,
              "control_image": control_image,
          }
  
          return inputs
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       int:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3    )
  
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()	, reason="XFormers attention is only available with CUDA and `xformers` installed"	, )
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       List[Any]:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
  
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
          self._test_inference_batch_single_identical(expected_max_diff=2E-3    )
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= StableDiffusionControlNetInpaintPipeline
    _UpperCamelCase						= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _UpperCamelCase						= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _UpperCamelCase						= frozenset([]       )
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
          torch.manual_seed(0    )
          snake_case_						       =							UNetaDConditionModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, sample_size=32	, in_channels=4	, out_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	, cross_attention_dim=32	, )
          torch.manual_seed(0    )
          snake_case_						       =							ControlNetModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, in_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, cross_attention_dim=32	, conditioning_embedding_out_channels=(16, 32)	, )
          torch.manual_seed(0    )
          snake_case_						       =							DDIMScheduler(
              beta_start=0.00_085	, beta_end=0.012	, beta_schedule="scaled_linear"	, clip_sample=lowerCAmelCase_	, set_alpha_to_one=lowerCAmelCase_	, )
          torch.manual_seed(0    )
          snake_case_						       =							AutoencoderKL(
              block_out_channels=[32, 64]	, in_channels=3	, out_channels=3	, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	, latent_channels=4	, )
          torch.manual_seed(0    )
          snake_case_						       =							CLIPTextConfig(
              bos_token_id=0	, eos_token_id=2	, hidden_size=32	, intermediate_size=37	, layer_norm_eps=1E-05	, num_attention_heads=4	, num_hidden_layers=5	, pad_token_id=1	, vocab_size=10_00	, )
          snake_case_						       =							CLIPTextModel(lowerCAmelCase_    )
          snake_case_						       =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
  
          snake_case_						       =							{
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= StableDiffusionControlNetInpaintPipeline
    _UpperCamelCase						= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _UpperCamelCase						= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
    def 		_lowerCamelCase	(self  : str    )   ->       Dict:
          torch.manual_seed(0    )
          snake_case_						       =							UNetaDConditionModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, sample_size=32	, in_channels=9	, out_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	, cross_attention_dim=32	, )
          torch.manual_seed(0    )
  
          def init_weights(lowerCAmelCase_  : int    ):
                if isinstance(lowerCAmelCase_	, torch.nn.Convad    ):
                      torch.nn.init.normal(m.weight    )
                      m.bias.data.fill_(1.0    )
  
          snake_case_						       =							ControlNetModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, in_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, cross_attention_dim=32	, conditioning_embedding_out_channels=(16, 32)	, )
          controlneta.controlnet_down_blocks.apply(lowerCAmelCase_    )
  
          torch.manual_seed(0    )
          snake_case_						       =							ControlNetModel(
              block_out_channels=(32, 64)	, layers_per_block=2	, in_channels=4	, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	, cross_attention_dim=32	, conditioning_embedding_out_channels=(16, 32)	, )
          controlneta.controlnet_down_blocks.apply(lowerCAmelCase_    )
  
          torch.manual_seed(0    )
          snake_case_						       =							DDIMScheduler(
              beta_start=0.00_085	, beta_end=0.012	, beta_schedule="scaled_linear"	, clip_sample=lowerCAmelCase_	, set_alpha_to_one=lowerCAmelCase_	, )
          torch.manual_seed(0    )
          snake_case_						       =							AutoencoderKL(
              block_out_channels=[32, 64]	, in_channels=3	, out_channels=3	, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	, up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	, latent_channels=4	, )
          torch.manual_seed(0    )
          snake_case_						       =							CLIPTextConfig(
              bos_token_id=0	, eos_token_id=2	, hidden_size=32	, intermediate_size=37	, layer_norm_eps=1E-05	, num_attention_heads=4	, num_hidden_layers=5	, pad_token_id=1	, vocab_size=10_00	, )
          snake_case_						       =							CLIPTextModel(lowerCAmelCase_    )
          snake_case_						       =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
  
          snake_case_						       =							MultiControlNetModel([controlneta, controlneta]    )
  
          snake_case_						       =							{
              "unet": unet,
              "controlnet": controlnet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components
  
    def 		_lowerCamelCase	(self  : List[Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Any=0    )   ->       int:
          if str(lowerCAmelCase_    ).startswith("mps"    ):
                snake_case_						       =							torch.manual_seed(lowerCAmelCase_    )
          else:
                snake_case_						       =							torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
  
          snake_case_						       =							2
  
          snake_case_						       =							[
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	, generator=lowerCAmelCase_	, device=torch.device(lowerCAmelCase_    )	, ),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)	, generator=lowerCAmelCase_	, device=torch.device(lowerCAmelCase_    )	, ),
          ]
          snake_case_						       =							floats_tensor((1, 3, 32, 32)	, rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
          snake_case_						       =							init_image.cpu().permute(0	, 2	, 3	, 1    )[0]
  
          snake_case_						       =							Image.fromarray(np.uinta(lowerCAmelCase_    )    ).convert("RGB"    ).resize((64, 64)    )
          snake_case_						       =							Image.fromarray(np.uinta(init_image + 4    )    ).convert("RGB"    ).resize((64, 64)    )
  
          snake_case_						       =							{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
              "image": image,
              "mask_image": mask_image,
              "control_image": control_image,
          }
  
          return inputs
  
    def 		_lowerCamelCase	(self  : List[str]    )   ->       Optional[int]:
          snake_case_						       =							self.get_dummy_components()
          snake_case_						       =							self.pipeline_class(**lowerCAmelCase_    )
          pipe.to(lowerCAmelCase_    )
  
          snake_case_						       =							10.0
          snake_case_						       =							4
  
          snake_case_						       =							self.get_dummy_inputs(lowerCAmelCase_    )
          snake_case_						       =							steps
          snake_case_						       =							scale
          snake_case_						       =							pipe(**lowerCAmelCase_    )[0]
  
          snake_case_						       =							self.get_dummy_inputs(lowerCAmelCase_    )
          snake_case_						       =							steps
          snake_case_						       =							scale
          snake_case_						       =							pipe(**lowerCAmelCase_	, control_guidance_start=0.1	, control_guidance_end=0.2    )[0]
  
          snake_case_						       =							self.get_dummy_inputs(lowerCAmelCase_    )
          snake_case_						       =							steps
          snake_case_						       =							scale
          snake_case_						       =							pipe(**lowerCAmelCase_	, control_guidance_start=[0.1, 0.3]	, control_guidance_end=[0.2, 0.7]    )[0]
  
          snake_case_						       =							self.get_dummy_inputs(lowerCAmelCase_    )
          snake_case_						       =							steps
          snake_case_						       =							scale
          snake_case_						       =							pipe(**lowerCAmelCase_	, control_guidance_start=0.4	, control_guidance_end=[0.5, 0.8]    )[0]
  
          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
          assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
          assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
  
    def 		_lowerCamelCase	(self  : List[Any]    )   ->       Dict:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3    )
  
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()	, reason="XFormers attention is only available with CUDA and `xformers` installed"	, )
    def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
  
    def 		_lowerCamelCase	(self  : List[str]    )   ->       List[Any]:
          self._test_inference_batch_single_identical(expected_max_diff=2E-3    )
  
  
    def 		_lowerCamelCase	(self  : int    )   ->       Tuple:
          snake_case_						       =							self.get_dummy_components()
          snake_case_						       =							self.pipeline_class(**lowerCAmelCase_    )
          pipe.to(lowerCAmelCase_    )
          pipe.set_progress_bar_config(disable=lowerCAmelCase_    )
          with tempfile.TemporaryDirectory() as tmpdir:
                try:
                      # save_pretrained is not implemented for Multi-ControlNet
                      pipe.save_pretrained(lowerCAmelCase_    )
                except NotImplementedError:
                      pass
  
  
  
  @slow
  @require_torch_gpu
  class  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def 		_lowerCamelCase	(self  : List[str]    )   ->       str:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
          snake_case_						       =							ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"    )
  
          snake_case_						       =							StableDiffusionControlNetInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting"	, safety_checker=lowerCAmelCase_	, controlnet=lowerCAmelCase_    )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=lowerCAmelCase_    )
  
          snake_case_						       =							torch.Generator(device="cpu"    ).manual_seed(0    )
          snake_case_						       =							load_image(
              "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"    ).resize((5_12, 5_12)    )
  
          snake_case_						       =							load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_inpaint/input_bench_mask.png"    ).resize((5_12, 5_12)    )
  
          snake_case_						       =							"pitch black hole"
  
          snake_case_						       =							load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    ).resize((5_12, 5_12)    )
  
          snake_case_						       =							pipe(
              lowerCAmelCase_	, image=lowerCAmelCase_	, mask_image=lowerCAmelCase_	, control_image=lowerCAmelCase_	, generator=lowerCAmelCase_	, output_type="np"	, num_inference_steps=3	, )
  
          snake_case_						       =							output.images[0]
  
          assert image.shape == (5_12, 5_12, 3)
  
          snake_case_						       =							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"    )
  
          assert np.abs(expected_image - image    ).max() < 9E-2
  
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Union[str, Any]:
          snake_case_						       =							ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"    )
  
          snake_case_						       =							StableDiffusionControlNetInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-v1-5"	, safety_checker=lowerCAmelCase_	, controlnet=lowerCAmelCase_    )
          snake_case_						       =							DDIMScheduler.from_config(pipe.scheduler.config    )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=lowerCAmelCase_    )
  
          snake_case_						       =							torch.Generator(device="cpu"    ).manual_seed(33    )
  
          snake_case_						       =							load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"    )
          snake_case_						       =							init_image.resize((5_12, 5_12)    )
  
          snake_case_						       =							load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"    )
          snake_case_						       =							mask_image.resize((5_12, 5_12)    )
  
          snake_case_						       =							"a handsome man with ray-ban sunglasses"
  
          def make_inpaint_condition(lowerCAmelCase_  : str	, lowerCAmelCase_  : List[Any]    ):
                snake_case_						       =							np.array(image.convert("RGB"    )    ).astype(np.floataa    ) / 255.0
                snake_case_						       =							np.array(image_mask.convert("L"    )    ).astype(np.floataa    ) / 255.0
  
                assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
                snake_case_						       =							-1.0  # set as masked pixel
                snake_case_						       =							np.expand_dims(lowerCAmelCase_	, 0    ).transpose(0	, 3	, 1	, 2    )
                snake_case_						       =							torch.from_numpy(lowerCAmelCase_    )
                return image
  
          snake_case_						       =							make_inpaint_condition(lowerCAmelCase_	, lowerCAmelCase_    )
  
          snake_case_						       =							pipe(
              lowerCAmelCase_	, image=lowerCAmelCase_	, mask_image=lowerCAmelCase_	, control_image=lowerCAmelCase_	, guidance_scale=9.0	, eta=1.0	, generator=lowerCAmelCase_	, num_inference_steps=20	, output_type="np"	, )
          snake_case_						       =							output.images[0]
  
          assert image.shape == (5_12, 5_12, 3)
  
          snake_case_						       =							load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"    )
  
          assert np.abs(expected_image - image    ).max() < 9E-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()
  
  