import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCAmelCase      		=					"""\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

UpperCAmelCase      		=					"""
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
"""

UpperCAmelCase      		=					"""
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer 'id' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - 'id': id of the question-answer pair (see above),
            - 'answers': a list of Dict {'text': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    'exact': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'total': Number of score considered
    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'HasAns_f1': The F-score of predicted tokens versus the gold answer
    'HasAns_total': Number of score considered
    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'NoAns_f1': The F-score of predicted tokens versus the gold answer
    'NoAns_total': Number of score considered
    'best_exact': Best exact match (with varying threshold)
    'best_exact_thresh': No-answer probability threshold associated to the best exact match
    'best_f1': Best F1 (with varying threshold)
    'best_f1_thresh': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,						_KWARGS_DESCRIPTION    )
class  __lowerCAmelCase	(							datasets.Metric    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Any	)			->				Dict:
        return datasets.MetricInfo(
            description=_DESCRIPTION							,      citation=_CITATION							,      inputs_description=_KWARGS_DESCRIPTION							,      features=datasets.Features(
                {
                    'predictions': {
                        'id': datasets.Value('string'	),
                        'prediction_text': datasets.Value('string'	),
                        'no_answer_probability': datasets.Value('float32'	),
                    },
                    'references': {
                        'id': datasets.Value('string'	),
                        'answers': datasets.features.Sequence(
                            {'text': datasets.Value('string'	), 'answer_start': datasets.Value('int32'	)}	),
                    },
                }	)							,      codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/']							,      reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/']							,      )

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Tuple=1.0	)			->				Optional[Any]:
        _UpperCamelCase  :							int	    =	{p['id']: p['no_answer_probability'] for p in predictions}
        _UpperCamelCase  :							Optional[Any]	    =	[{'paragraphs': [{'qas': references}]}]
        _UpperCamelCase  :							Union[str, Any]	    =	{p['id']: p['prediction_text'] for p in predictions}

        _UpperCamelCase  :							Optional[Any]	    =	make_qid_to_has_ans(__lowerCamelCase	)  # maps qid to True/False
        _UpperCamelCase  :							Union[str, Any]	    =	[k for k, v in qid_to_has_ans.items() if v]
        _UpperCamelCase  :							Any	    =	[k for k, v in qid_to_has_ans.items() if not v]

        _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	get_raw_scores(__lowerCamelCase							,      __lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	apply_no_ans_threshold(__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	apply_no_ans_threshold(__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase	)
        _UpperCamelCase  :							List[Any]	    =	make_eval_dict(__lowerCamelCase							,      __lowerCamelCase	)

        if has_ans_qids:
               _UpperCamelCase  :							Union[str, Any]	    =	make_eval_dict(__lowerCamelCase							,      __lowerCamelCase							,      qid_list=__lowerCamelCase	)
               merge_eval(__lowerCamelCase							,      __lowerCamelCase							,      'HasAns'	)
        if no_ans_qids:
               _UpperCamelCase  :							str	    =	make_eval_dict(__lowerCamelCase							,      __lowerCamelCase							,      qid_list=__lowerCamelCase	)
               merge_eval(__lowerCamelCase							,      __lowerCamelCase							,      'NoAns'	)
        find_all_best_thresh(__lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase							,      __lowerCamelCase	)
        return dict(__lowerCamelCase	)











'''simple docstring'''





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A				:				Optional[int]					=		logging.get_logger(__name__)

__A				:				Tuple					=		{
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				"convnext"

    def __init__(       self  :  Any					,  __lowerCamelCase  :  Dict=3					,  __lowerCamelCase  :  int=4					,  __lowerCamelCase  :  Any=4					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  str=None					,  __lowerCamelCase  :  Optional[int]="gelu"					,  __lowerCamelCase  :  List[str]=0.0_2					,  __lowerCamelCase  :  int=1E-12					,  __lowerCamelCase  :  Optional[Any]=1E-6					,  __lowerCamelCase  :  Any=0.0					,  __lowerCamelCase  :  Union[str, Any]=224					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  List[str]=None					,  **__lowerCamelCase  :  Tuple					,  )				->				Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     lowerCamelCase__	=  num_channels
     lowerCamelCase__	=  patch_size
     lowerCamelCase__	=  num_stages
     lowerCamelCase__	=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     lowerCamelCase__	=  [3, 3, 9, 3] if depths is None else depths
     lowerCamelCase__	=  hidden_act
     lowerCamelCase__	=  initializer_range
     lowerCamelCase__	=  layer_norm_eps
     lowerCamelCase__	=  layer_scale_init_value
     lowerCamelCase__	=  drop_path_rate
     lowerCamelCase__	=  image_size
     lowerCamelCase__	=  ["stem"] + [f'''stage{idx}''' for idx in range(1					,  len(self.depths  ) + 1  )]
     lowerCamelCase__	,						lowerCamelCase__	=  get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,  out_indices=__lowerCamelCase					,  stage_names=self.stage_names  )
class 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				version.parse("1.11"   )

    @property
    def  a__					(       self  :  List[Any]  )				->				Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]  )




    @property
    def  a__					(       self  :  Tuple  )				->				float:

     '''simple docstring'''




     return 1E-5

