






import argparse
import json
import re
import string
import sys
from collections import Counter





def 						A_					(						lowercase_			)				->Optional[Any]:







 """simple docstring"""

 def remove_articles(lowercase_			):
  return re.sub(r'\b(a|an|the)\b'	,  ' '	,  lowercase_			)

 def white_space_fix(lowercase_			):
  return " ".join(text.split()			)

 def remove_punc(lowercase_			):
  SCREAMING_SNAKE_CASE				    =		set(string.punctuation			)
  return "".join(ch for ch in text if ch not in exclude			)

 def lower(lowercase_			):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(lowercase_			)			)			)			)





def 						A_					(						lowercase_	,  lowercase_			)				->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		normalize_answer(lowercase_			).split()
 SCREAMING_SNAKE_CASE				    =		normalize_answer(lowercase_			).split()
 SCREAMING_SNAKE_CASE				    =		Counter(lowercase_			) & Counter(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		sum(common.values()			)
 if num_same == 0:
  return 0
 SCREAMING_SNAKE_CASE				    =		1.0 * num_same / len(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		1.0 * num_same / len(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		(2 * precision * recall) / (precision + recall)
 return fa





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

 return normalize_answer(lowercase_			) == normalize_answer(lowercase_			)





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		[]
 for ground_truth in ground_truths:
  SCREAMING_SNAKE_CASE				    =		metric_fn(lowercase_	,  lowercase_			)
  scores_for_ground_truths.append(lowercase_			)
 return max(lowercase_			)





def 						A_					(						lowercase_	,  lowercase_			)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		SCREAMING_SNAKE_CASE				    =		SCREAMING_SNAKE_CASE				    =		0
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     SCREAMING_SNAKE_CASE				    =		'Unanswered question ' + qa['id'] + ' will receive score 0.'
     print(lowercase_	,  file=sys.stderr			)
     continue
    SCREAMING_SNAKE_CASE				    =		[x['text'] for x in qa['answers']]
    SCREAMING_SNAKE_CASE				    =		predictions[qa['id']]
    exact_match += metric_max_over_ground_truths(lowercase_	,  lowercase_	,  lowercase_			)
    fa += metric_max_over_ground_truths(lowercase_	,  lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * exact_match / total
 SCREAMING_SNAKE_CASE				    =		1_0_0.0 * fa / total

 return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
      __UpperCAmelCase      =				"1.1"
      __UpperCAmelCase      =				argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
      parser.add_argument("dataset_file", help="Dataset file")
      parser.add_argument("prediction_file", help="Prediction File")
      __UpperCAmelCase      =				parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            __UpperCAmelCase      =				json.load(dataset_file)
            if dataset_json["version"] != expected_version:
                  print(
                      "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
                      file=sys.stderr,
                  )
            __UpperCAmelCase      =				dataset_json["data"]
      with open(args.prediction_file) as prediction_file:
            __UpperCAmelCase      =				json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))






'''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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      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')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           '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 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      sd_pipe(**_A      ).images
       __A													=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __A													=      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 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image      ).mean() < 1E-2






