
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

lowercase  :					int						    =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[List[ImageInput]]:
       if isinstance(SCREAMING_SNAKE_CASE__					,	(list, tuple)				) and isinstance(videos[0]					,	(list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

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

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

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




class __snake_case		(      lowerCAmelCase     ):
    _a		:						Dict=				["pixel_values"]
    def __init__(  self		,snake_case = True		,snake_case = None		,snake_case = PILImageResampling.BILINEAR		,snake_case = True		,snake_case = None		,snake_case = True		,snake_case = 1 / 255		,snake_case = True		,snake_case = True		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case      )
           lowercase							:     int       	=      size if size is not None else {"""shortest_edge""": 256}
           lowercase							:     Union[str, Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           lowercase							:     str       	=      crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           lowercase							:     Tuple       	=      get_size_dict(snake_case		,param_name="""crop_size"""      )

           lowercase							:     Union[str, Any]       	=      do_resize
           lowercase							:     str       	=      size
           lowercase							:     Optional[int]       	=      do_center_crop
           lowercase							:     List[Any]       	=      crop_size
           lowercase							:     List[str]       	=      resample
           lowercase							:     Any       	=      do_rescale
           lowercase							:     Union[str, Any]       	=      rescale_factor
           lowercase							:     Any       	=      offset
           lowercase							:     Optional[int]       	=      do_normalize
           lowercase							:     Optional[int]       	=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowercase							:     Tuple       	=      image_std if image_std is not None else IMAGENET_STANDARD_STD
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = PILImageResampling.BILINEAR		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           if "shortest_edge" in size:
                  lowercase							:     str       	=      get_resize_output_image_size(snake_case		,size["""shortest_edge"""]		,default_to_square=snake_case      )
           elif "height" in size and "width" in size:
                  lowercase							:     Optional[int]       	=      (size["""height"""], size["""width"""])
           else:
                  raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"      )
           return resize(snake_case		,size=snake_case		,resample=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      get_size_dict(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(snake_case		,size=(size["""height"""], size["""width"""])		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case = True		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      image.astype(np.floataa      )
           if offset:
                  lowercase							:     Optional[Any]       	=      image - (scale / 2)
           return rescale(snake_case		,scale=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return normalize(snake_case		,mean=snake_case		,std=snake_case		,data_format=snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,):



           '''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.
           lowercase							:     Optional[int]       	=      to_numpy_array(snake_case      )

           if do_resize:
                  lowercase							:     Union[str, Any]       	=      self.resize(image=snake_case		,size=snake_case		,resample=snake_case      )

           if do_center_crop:
                  lowercase							:     Optional[Any]       	=      self.center_crop(snake_case		,size=snake_case      )

           if do_rescale:
                  lowercase							:     Optional[int]       	=      self.rescale(image=snake_case		,scale=snake_case		,offset=snake_case      )

           if do_normalize:
                  lowercase							:     Any       	=      self.normalize(image=snake_case		,mean=snake_case		,std=snake_case      )

           lowercase							:     Any       	=      to_channel_dimension_format(snake_case		,snake_case      )
           return image
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:     int       	=      size if size is not None else self.size
           lowercase							:     List[Any]       	=      get_size_dict(snake_case		,default_to_square=snake_case      )
           lowercase							:     str       	=      crop_size if crop_size is not None else self.crop_size
           lowercase							:     List[Any]       	=      get_size_dict(snake_case		,param_name="""crop_size"""      )

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

           lowercase							:     List[Any]       	=      make_batched(snake_case      )

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

           lowercase							:     Optional[int]       	=      {"""pixel_values""": videos}
           return BatchFeature(data=snake_case		,tensor_type=snake_case      )


from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase  :					str						    =							"""\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

lowercase  :					Dict						    =							"""\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

lowercase  :					int						    =							"""
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      float(spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.config_name not in [
               "sst2",
               "mnli",
               "mnli_mismatched",
               "mnli_matched",
               "cola",
               "stsb",
               "mrpc",
               "qqp",
               "qnli",
               "rte",
               "wnli",
               "hans",
           ]:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""      )
           return datasets.MetricInfo(
               description=_DESCRIPTION		,citation=_CITATION		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
                   {
                       """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""      ),
                       """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""      ),
                   }      )		,codebase_urls=[]		,reference_urls=[]		,format="""numpy"""		,)
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(snake_case		,snake_case      )}
           elif self.config_name == "stsb":
                  return pearson_and_spearman(snake_case		,snake_case      )
           elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(snake_case		,snake_case      )
           elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(snake_case		,snake_case      )}
           else:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""      )
