


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

import datasets


_A		       =						'\\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'

_A		       =						'\\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'

_A		       =						'\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__			:					int			,     SCREAMING_SNAKE_CASE__			:					int				):
     return float((preds == labels).mean()				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     __UpperCamelCase    							=simple_accuracy(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=float(fa_score(y_true=SCREAMING_SNAKE_CASE__			,     y_pred=SCREAMING_SNAKE_CASE__				)				)
     return {
         "accuracy": acc,
         "f1": fa,
     }

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     __UpperCamelCase    							=float(pearsonr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)[0]				)
     __UpperCamelCase    							=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     UpperCAmelCase__       (					datasets.Metric		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[int]:
         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 _a					(			self					,   A_					,   A_    )  ->       Dict:
         if self.config_name == "cola":
              return {"matthews_correlation": matthews_corrcoef(A_					,   A_    )}
         elif self.config_name == "stsb":
              return pearson_and_spearman(A_					,   A_    )
         elif self.config_name in ["mrpc", "qqp"]:
              return acc_and_fa(A_					,   A_    )
         elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
              return {"accuracy": simple_accuracy(A_					,   A_    )}
         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"]'    )









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





