






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
  import PIL

SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

def      _lowerCamelCase	(						__A    :							List[Any] )      ->					List[List[ImageInput]]:
    if isinstance(_A		,		(list, tuple) ) and isinstance(videos[0]		,		(list, tuple) ) and is_valid_image(videos[0][0] ):
        return videos

    elif isinstance(_A		,		(list, tuple) ) and is_valid_image(videos[0] ):
        return [videos]

    elif is_valid_image(_A ):
        return [[videos]]

    raise ValueError(f'''Could not make batched video from {videos}''' )


class 							A_    (						lowerCAmelCase__			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					["pixel_values"]


 def __init__(	self							,  _A = True							,  _A = None							,  _A = PILImageResampling.BILINEAR							,  _A = True							,  _A = None							,  _A = True							,  _A = 1 / 255							,  _A = True							,  _A = True							,  _A = None							,  _A = None							,  **_A							,  ) ->		None:

     """simple docstring"""



     super().__init__(**_SCREAMING_SNAKE_CASE)
     _UpperCAmelCase				:      Dict			  =				size if size is not None else {'''shortest_edge''': 256}
     _UpperCAmelCase				:      Optional[Any]			  =				get_size_dict(_SCREAMING_SNAKE_CASE							,  default_to_square=_SCREAMING_SNAKE_CASE)
     _UpperCAmelCase				:      str			  =				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     _UpperCAmelCase				:      int			  =				get_size_dict(_SCREAMING_SNAKE_CASE							,  param_name='''crop_size''')

     _UpperCAmelCase				:      Optional[int]			  =				do_resize
     _UpperCAmelCase				:      Tuple			  =				size
     _UpperCAmelCase				:      int			  =				do_center_crop
     _UpperCAmelCase				:      Any			  =				crop_size
     _UpperCAmelCase				:      Optional[int]			  =				resample
     _UpperCAmelCase				:      Optional[Any]			  =				do_rescale
     _UpperCAmelCase				:      Optional[Any]			  =				rescale_factor
     _UpperCAmelCase				:      List[Any]			  =				offset
     _UpperCAmelCase				:      List[Any]			  =				do_normalize
     _UpperCAmelCase				:      Dict			  =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _UpperCAmelCase				:      str			  =				image_std if image_std is not None else IMAGENET_STANDARD_STD


 def  snake_case__				(	self							,  _A							,  _A							,  _A = PILImageResampling.BILINEAR							,  _A = None							,  **_A							,  ) ->		np.ndarray:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				get_size_dict(_SCREAMING_SNAKE_CASE							,  default_to_square=_SCREAMING_SNAKE_CASE)
     if "shortest_edge" in size:
         _UpperCAmelCase				:      Tuple			  =				get_resize_output_image_size(_SCREAMING_SNAKE_CASE							,  size['''shortest_edge''']							,  default_to_square=_SCREAMING_SNAKE_CASE)
     elif "height" in size and "width" in size:
         _UpperCAmelCase				:      Tuple			  =				(size['''height'''], size['''width'''])
     else:
         raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
     return resize(_SCREAMING_SNAKE_CASE							,  size=_SCREAMING_SNAKE_CASE							,  resample=_SCREAMING_SNAKE_CASE							,  data_format=_SCREAMING_SNAKE_CASE							,  **_SCREAMING_SNAKE_CASE)


 def  snake_case__				(	self							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		np.ndarray:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				get_size_dict(_SCREAMING_SNAKE_CASE)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
     return center_crop(_SCREAMING_SNAKE_CASE							,  size=(size['''height'''], size['''width'''])							,  data_format=_SCREAMING_SNAKE_CASE							,  **_SCREAMING_SNAKE_CASE)


 def  snake_case__				(	self							,  _A							,  _A							,  _A = True							,  _A = None							,  **_A							,  ) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				image.astype(np.floataa)
     if offset:
         _UpperCAmelCase				:      List[str]			  =				image - (scale / 2)
     return rescale(_SCREAMING_SNAKE_CASE							,  scale=_SCREAMING_SNAKE_CASE							,  data_format=_SCREAMING_SNAKE_CASE							,  **_SCREAMING_SNAKE_CASE)


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		np.ndarray:

     """simple docstring"""



     return normalize(_SCREAMING_SNAKE_CASE							,  mean=_SCREAMING_SNAKE_CASE							,  std=_SCREAMING_SNAKE_CASE							,  data_format=_SCREAMING_SNAKE_CASE							,  **_SCREAMING_SNAKE_CASE)


 def  snake_case__				(	self							,  _A							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = ChannelDimension.FIRST							,  ) ->		np.ndarray:

     """simple docstring"""



     if do_resize and size is None or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.''')

     if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop is True.''')

     if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.''')

     if offset and not do_rescale:
         raise ValueError('''For offset, do_rescale must also be set to True.''')

     # All transformations expect numpy arrays.
     _UpperCAmelCase				:      int			  =				to_numpy_array(_SCREAMING_SNAKE_CASE)

     if do_resize:
         _UpperCAmelCase				:      List[str]			  =				self.resize(image=_SCREAMING_SNAKE_CASE							,  size=_SCREAMING_SNAKE_CASE							,  resample=_SCREAMING_SNAKE_CASE)

     if do_center_crop:
         _UpperCAmelCase				:      int			  =				self.center_crop(_SCREAMING_SNAKE_CASE							,  size=_SCREAMING_SNAKE_CASE)

     if do_rescale:
         _UpperCAmelCase				:      Optional[Any]			  =				self.rescale(image=_SCREAMING_SNAKE_CASE							,  scale=_SCREAMING_SNAKE_CASE							,  offset=_SCREAMING_SNAKE_CASE)

     if do_normalize:
         _UpperCAmelCase				:      List[Any]			  =				self.normalize(image=_SCREAMING_SNAKE_CASE							,  mean=_SCREAMING_SNAKE_CASE							,  std=_SCREAMING_SNAKE_CASE)

     _UpperCAmelCase				:      Tuple			  =				to_channel_dimension_format(_SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE)
     return image


 def  snake_case__				(	self							,  _A							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = None							,  _A = ChannelDimension.FIRST							,  **_A							,  ) ->		PIL.Image.Image:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				do_resize if do_resize is not None else self.do_resize
     _UpperCAmelCase				:      Dict			  =				resample if resample is not None else self.resample
     _UpperCAmelCase				:      str			  =				do_center_crop if do_center_crop is not None else self.do_center_crop
     _UpperCAmelCase				:      Tuple			  =				do_rescale if do_rescale is not None else self.do_rescale
     _UpperCAmelCase				:      List[Any]			  =				rescale_factor if rescale_factor is not None else self.rescale_factor
     _UpperCAmelCase				:      Optional[int]			  =				offset if offset is not None else self.offset
     _UpperCAmelCase				:      Any			  =				do_normalize if do_normalize is not None else self.do_normalize
     _UpperCAmelCase				:      Dict			  =				image_mean if image_mean is not None else self.image_mean
     _UpperCAmelCase				:      str			  =				image_std if image_std is not None else self.image_std

     _UpperCAmelCase				:      Optional[int]			  =				size if size is not None else self.size
     _UpperCAmelCase				:      Optional[int]			  =				get_size_dict(_SCREAMING_SNAKE_CASE							,  default_to_square=_SCREAMING_SNAKE_CASE)
     _UpperCAmelCase				:      Any			  =				crop_size if crop_size is not None else self.crop_size
     _UpperCAmelCase				:      List[str]			  =				get_size_dict(_SCREAMING_SNAKE_CASE							,  param_name='''crop_size''')

     if not valid_images(_SCREAMING_SNAKE_CASE):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.''')

     _UpperCAmelCase				:      List[str]			  =				make_batched(_SCREAMING_SNAKE_CASE)

     _UpperCAmelCase				:      Dict			  =				[
         [
             self._preprocess_image(
                 image=_SCREAMING_SNAKE_CASE							,  do_resize=_SCREAMING_SNAKE_CASE							,  size=_SCREAMING_SNAKE_CASE							,  resample=_SCREAMING_SNAKE_CASE							,  do_center_crop=_SCREAMING_SNAKE_CASE							,  crop_size=_SCREAMING_SNAKE_CASE							,  do_rescale=_SCREAMING_SNAKE_CASE							,  rescale_factor=_SCREAMING_SNAKE_CASE							,  offset=_SCREAMING_SNAKE_CASE							,  do_normalize=_SCREAMING_SNAKE_CASE							,  image_mean=_SCREAMING_SNAKE_CASE							,  image_std=_SCREAMING_SNAKE_CASE							,  data_format=_SCREAMING_SNAKE_CASE							,  )
             for img in video
         ]
         for video in videos
     ]

     _UpperCAmelCase				:      List[Any]			  =				{'''pixel_values''': videos}
     return BatchFeature(data=_SCREAMING_SNAKE_CASE							,  tensor_type=_SCREAMING_SNAKE_CASE)












import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


SCREAMING_SNAKE_CASE 							=						'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


SCREAMING_SNAKE_CASE 							=						'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


SCREAMING_SNAKE_CASE 							=						R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION			)
class 							A_    (						datasets.Metric			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     return datasets.MetricInfo(
         description=_DESCRIPTION							,  citation=_CITATION							,  inputs_description=_KWARGS_DESCRIPTION							,  features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string'''),
                 '''references''': datasets.Value('''string'''),
             })							,  homepage='''https://github.com/hendrycks/math'''							,  codebase_urls=['''https://github.com/hendrycks/math''']							,  )


 def  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				0.0
     for i, j in zip(_A							,  _A):
         n_correct += 1.0 if math_equivalence.is_equiv(_A							,  _A) else 0.0
     _UpperCAmelCase				:      Tuple			  =				n_correct / len(_A)
     return {
         "accuracy": accuracy,
     }




