





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


if is_vision_available():
       import PIL

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ['pixel_values']





      def __init__(       self      ,						lowercase = True      ,						lowercase = None      ,						lowercase = PILImageResampling.BICUBIC      ,						lowercase = True      ,						lowercase = None      ,						lowercase = True      ,						lowercase = 1 / 255      ,						lowercase = True      ,						lowercase = None      ,						lowercase = None      ,						lowercase = True      ,						**lowercase      ,						)		->     None:
            super().__init__(**lowercase )
            lowerCAmelCase  						=       size if size is not None else {"""shortest_edge""": 224}
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase      ,						param_name="""crop_size""" )

            lowerCAmelCase  						=       do_resize
            lowerCAmelCase  						=       size
            lowerCAmelCase  						=       resample
            lowerCAmelCase  						=       do_center_crop
            lowerCAmelCase  						=       crop_size
            lowerCAmelCase  						=       do_rescale
            lowerCAmelCase  						=       rescale_factor
            lowerCAmelCase  						=       do_normalize
            lowerCAmelCase  						=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
            lowerCAmelCase  						=       image_std if image_std is not None else OPENAI_CLIP_STD
            lowerCAmelCase  						=       do_convert_rgb





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = PILImageResampling.BICUBIC      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						default_to_square=lowercase )
            if "shortest_edge" not in size:
                  raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
            lowerCAmelCase  						=       get_resize_output_image_size(lowercase      ,						size=size["""shortest_edge"""]      ,						default_to_square=lowercase )
            return resize(lowercase      ,						size=lowercase      ,						resample=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            lowerCAmelCase  						=       get_size_dict(lowercase )
            if "height" not in size or "width" not in size:
                  raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
            return center_crop(lowercase      ,						size=(size["""height"""], size["""width"""])      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     Any:
            return rescale(lowercase      ,						scale=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     np.ndarray:
            return normalize(lowercase      ,						mean=lowercase      ,						std=lowercase      ,						data_format=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = None      ,						lowercase = ChannelDimension.FIRST      ,						**lowercase      ,						)		->     PIL.Image.Image:
            lowerCAmelCase  						=       do_resize if do_resize is not None else self.do_resize
            lowerCAmelCase  						=       size if size is not None else self.size
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						param_name="""size"""      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       resample if resample is not None else self.resample
            lowerCAmelCase  						=       do_center_crop if do_center_crop is not None else self.do_center_crop
            lowerCAmelCase  						=       crop_size if crop_size is not None else self.crop_size
            lowerCAmelCase  						=       get_size_dict(lowercase      ,						param_name="""crop_size"""      ,						default_to_square=lowercase )
            lowerCAmelCase  						=       do_rescale if do_rescale is not None else self.do_rescale
            lowerCAmelCase  						=       rescale_factor if rescale_factor is not None else self.rescale_factor
            lowerCAmelCase  						=       do_normalize if do_normalize is not None else self.do_normalize
            lowerCAmelCase  						=       image_mean if image_mean is not None else self.image_mean
            lowerCAmelCase  						=       image_std if image_std is not None else self.image_std
            lowerCAmelCase  						=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

            lowerCAmelCase  						=       make_list_of_images(lowercase )

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

            if do_resize and size is None:
                  raise ValueError("""Size 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.""" )

            # PIL RGBA images are converted to RGB
            if do_convert_rgb:
                  lowerCAmelCase  						=       [convert_to_rgb(lowercase ) for image in images]

            # All transformations expect numpy arrays.
            lowerCAmelCase  						=       [to_numpy_array(lowercase ) for image in images]

            if do_resize:
                  lowerCAmelCase  						=       [self.resize(image=lowercase      ,						size=lowercase      ,						resample=lowercase ) for image in images]

            if do_center_crop:
                  lowerCAmelCase  						=       [self.center_crop(image=lowercase      ,						size=lowercase ) for image in images]

            if do_rescale:
                  lowerCAmelCase  						=       [self.rescale(image=lowercase      ,						scale=lowercase ) for image in images]

            if do_normalize:
                  lowerCAmelCase  						=       [self.normalize(image=lowercase      ,						mean=lowercase      ,						std=lowercase ) for image in images]

            lowerCAmelCase  						=       [to_channel_dimension_format(lowercase      ,						lowercase ) for image in images]

            lowerCAmelCase  						=       {"""pixel_values""": images}
            return BatchFeature(data=lowercase      ,						tensor_type=lowercase )













"""simple docstring"""





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

import datasets


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

SCREAMING_SNAKE_CASE__					=   "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

SCREAMING_SNAKE_CASE__					=   "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      return float((preds == labels).mean()			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




      lowerCAmelCase  						=       simple_accuracy(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       float(fa_score(y_true=SCREAMING_SNAKE_CASE							,						y_pred=SCREAMING_SNAKE_CASE			)			)
      return {
          "accuracy": acc,
          "f1": fa,
      }


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       float(pearsonr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      lowerCAmelCase  						=       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 		lowercase					(					datasets.Metric					):





      def     _snake_case			(       self )		->     List[str]:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Any:
            if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(lowercase      ,						lowercase )}
            elif self.config_name == "stsb":
                  return pearson_and_spearman(lowercase      ,						lowercase )
            elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(lowercase      ,						lowercase )
            elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(lowercase      ,						lowercase )}
            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\"]""" )






