
'''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



'''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()

