"""simple docstring"""
import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     lowercase = False       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	List[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	Tuple     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	str     				=      [Image.fromarray(lowercase) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        if not isinstance(lowercase       ,     lowercase):
          a__:	int     				=      [images]
        a__:	Optional[Any]     				=      [np.array(lowercase).astype(np.floataa) / 255.0 for image in images]
        a__:	int     				=      np.stack(lowercase       ,     axis=0)

        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     torch.FloatTensor:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Any     				=      images[..., None]

        a__:	Any     				=      torch.from_numpy(images.transpose(0       ,     3       ,     1       ,     2))
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        a__:	Tuple     				=      images.cpu().permute(0       ,     2       ,     3       ,     1).float().numpy()
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Any:





        '''simple docstring'''





        return 2.0 * images - 1.0



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return (images / 2 + 0.5).clamp(0       ,     1)



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        a__:	List[Any]     				=      image.convert('RGB')
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     PIL.Image.Image:





        '''simple docstring'''





        if height is None:
          a__:	Union[str, Any]     				=      image.height
        if width is None:
          a__:	Optional[Any]     				=      image.width

        a__				,       a__:	int     				=      (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        a__:	Dict     				=      image.resize((width, height)       ,     resample=PIL_INTERPOLATION[self.config.resample])
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     torch.Tensor:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(lowercase       ,     lowercase):
          a__:	str     				=      [image]
        elif not (isinstance(lowercase       ,     lowercase) and all(isinstance(lowercase       ,     lowercase) for i in image)):
          raise ValueError(
              f'Input is in incorrect format: {[type(lowercase) for i in image]}. Currently, we only support {", ".join(lowercase)}')

        if isinstance(image[0]       ,     PIL.Image.Image):
          if self.config.do_convert_rgb:
            a__:	Any     				=      [self.convert_to_rgb(lowercase) for i in image]
          if self.config.do_resize:
            a__:	Any     				=      [self.resize(lowercase       ,     lowercase       ,     lowercase) for i in image]
          a__:	Any     				=      self.pil_to_numpy(lowercase)  # to np
          a__:	Tuple     				=      self.numpy_to_pt(lowercase)  # to pt

        elif isinstance(image[0]       ,     np.ndarray):
          a__:	Any     				=      np.concatenate(lowercase       ,     axis=0) if image[0].ndim == 4 else np.stack(lowercase       ,     axis=0)
          a__:	List[Any]     				=      self.numpy_to_pt(lowercase)
          a__				,       a__				,       a__				,       a__:	Dict     				=      image.shape
          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
                f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor')

        elif isinstance(image[0]       ,     torch.Tensor):
          a__:	int     				=      torch.cat(lowercase       ,     axis=0) if image[0].ndim == 4 else torch.stack(lowercase       ,     axis=0)
          a__				,       a__				,       a__				,       a__:	Tuple     				=      image.shape

          # don't need any preprocess if the image is latents
          if channel == 4:
            return image

          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
                f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor')

        # expected range [0,1], normalize to [-1,1]
        a__:	Optional[Any]     				=      self.config.do_normalize
        if image.min() < 0:
          warnings.warn(
              'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
              f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'       ,     lowercase       ,     )
          a__:	Union[str, Any]     				=      False

        if do_normalize:
          a__:	Optional[Any]     				=      self.normalize(lowercase)

        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     Dict:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	str     				=      (
              f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'       ,     '1.0.0'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	int     				=      'np'

        if output_type == "latent":
          return image

        if do_denormalize is None:
          a__:	Dict     				=      [self.config.do_normalize] * image.shape[0]

        a__:	int     				=      torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

        if output_type == "pt":
          return image

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          return image

        if output_type == "pil":
          return self.numpy_to_pil(lowercase)







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Tuple     				=      images[None, ...]
        a__:	Optional[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	int     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	Optional[Any]     				=      [Image.fromarray(image[:, :, :3]) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return image[:, :, 1] * 2**8 + image[:, :, 2]



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Optional[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
          a__:	List[str]     				=      (images_depth * 2_55).round().astype('uint8')
          a__:	Any     				=      [
              Image.fromarray(self.rgblike_to_depthmap(lowercase)       ,     mode='I;16') for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          a__:	Dict     				=      (images_depth * 65535.0).astype(np.uintaa)
          a__:	Optional[Any]     				=      [Image.fromarray(lowercase       ,     mode='I;16') for image_depth in images_depth]
        else:
          raise Exception('Not supported')

        return pil_images



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     List[str]:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	Optional[Any]     				=      (
              f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'       ,     '1.0.0'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	List[str]     				=      'np'

        if do_denormalize is None:
          a__:	int     				=      [self.config.do_normalize] * image.shape[0]

        a__:	Optional[Any]     				=      torch.stack(
            [self.denormalize(image[i]) if do_denormalize[i] else image[i] for i in range(image.shape[0])])

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          if image.shape[-1] == 6:
            a__:	Any     				=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:]) for im in image]       ,     axis=0)
          else:
            a__:	str     				=      image[:, :, :, 3:]
          return image[:, :, :, :3], image_depth

        if output_type == "pil":
          return self.numpy_to_pil(lowercase), self.numpy_to_depth(lowercase)
        else:
          raise Exception(f'This type {output_type} is not supported')









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




def  A_(      A ):

	def remove_articles(A ):
		return re.sub(R"""\b(a|an|the)\b"""					,   """ """					,   A )

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(A ) ) ) )




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  normalize_answer(A ).split()
	UpperCAmelCase_		    =  normalize_answer(A ).split()
	UpperCAmelCase_		    =  Counter(A ) & Counter(A )
	UpperCAmelCase_		    =  sum(common.values() )
	if num_same == 0:
		return 0
	UpperCAmelCase_		    =  1.0 * num_same / len(A )
	UpperCAmelCase_		    =  1.0 * num_same / len(A )
	UpperCAmelCase_		    =  (2 * precision * recall) / (precision + recall)
	return fa




def  A_(      A					,   A ):
	return normalize_answer(A ) == normalize_answer(A )




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  []
	for ground_truth in ground_truths:
		UpperCAmelCase_		    =  metric_fn(A					,   A )
		scores_for_ground_truths.append(A )
	return max(A )




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  UpperCAmelCase_		    =  UpperCAmelCase_		    =  0
	UpperCAmelCase_		    =  []
	for passage in dataset:
		for qa in passage["qas"]:
			total += 1
			if qa["id"] not in predictions:
				UpperCAmelCase_		    =  f"""Unanswered question {qa['id']} will receive score 0."""
				print(A					,   file=sys.stderr )
				continue

			UpperCAmelCase_		    =  [x["""text"""] for x in qa["""answers"""]]
			UpperCAmelCase_		    =  predictions[qa["""id"""]]

			UpperCAmelCase_		    =  metric_max_over_ground_truths(A					,   A					,   A )
			if int(_exact_match ) == 1:
				correct_ids.append(qa["""id"""] )
			exact_match += _exact_match

			fa += metric_max_over_ground_truths(A					,   A					,   A )

	UpperCAmelCase_		    =  exact_match / total
	UpperCAmelCase_		    =  fa / total

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


if __name__ == "__main__":
				UpperCamelCase__ :			Any       	=			"""1.0"""
				UpperCamelCase__ :			Optional[Any]       	=			argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
				parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
				parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
				parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
				UpperCamelCase__ :			Any       	=			parser.parse_args()

				with open(args.data_file) as data_file:
								UpperCamelCase__ :			int       	=			json.load(data_file)
								if dataset_json["version"] != expected_version:
												print(
												    f"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
												    file=sys.stderr,
												)
								UpperCamelCase__ :			str       	=			dataset_json["""data"""]

				with open(args.pred_file) as pred_file:
								UpperCamelCase__ :			List[Any]       	=			json.load(pred_file)

				UpperCamelCase__	,      UpperCamelCase__ :			Optional[int]       	=			evaluate(dataset, predictions)

				if args.output_correct_ids:
								print(f"Output {len(correct_ids)} correctly answered question IDs.")
								with open("""correct_ids.json""", """w""") as f:
												json.dump(correct_ids, f)


