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 						snake_case_ (	datasets.Metric ):






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Any:
       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    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  Any			,		lowercase_       :  int			,		lowercase_       :  List[str]=1.0						)      ->		Optional[Any]:
       lowercase__				:     str					      =       {p["id"]: p["no_answer_probability"] for p in predictions}
       lowercase__				:     List[str]					      =       [{"paragraphs": [{"qas": references}]}]
       lowercase__				:     List[Any]					      =       {p["id"]: p["prediction_text"] for p in predictions}

       lowercase__				:     int					      =       make_qid_to_has_ans(lowercase_						)  # maps qid to True/False
       lowercase__				:     int					      =       [k for k, v in qid_to_has_ans.items() if v]
       lowercase__				:     str					      =       [k for k, v in qid_to_has_ans.items() if not v]

       lowercase__      ,     lowercase__				:     str					      =       get_raw_scores(lowercase_			,		lowercase_						)
       lowercase__				:     Dict					      =       apply_no_ans_threshold(lowercase_			,		lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       apply_no_ans_threshold(lowercase_			,		lowercase_			,		lowercase_			,		lowercase_						)
       lowercase__				:     Any					      =       make_eval_dict(lowercase_			,		lowercase_						)

       if has_ans_qids:
           lowercase__				:     Optional[Any]					      =       make_eval_dict(lowercase_			,		lowercase_			,		qid_list=lowercase_						)
           merge_eval(lowercase_			,		lowercase_			,		"HasAns"						)
       if no_ans_qids:
           lowercase__				:     Optional[int]					      =       make_eval_dict(lowercase_			,		lowercase_			,		qid_list=lowercase_						)
           merge_eval(lowercase_			,		lowercase_			,		"NoAns"						)
       find_all_best_thresh(lowercase_			,		lowercase_			,		lowercase_			,		lowercase_			,		lowercase_			,		lowercase_						)
       return dict(lowercase_						)




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


UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		{
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}



class 						snake_case_ (	__A ):
   __A			:	Any        =       "mobilevit"






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  List[Any]=3			,		lowercase_       :  List[Any]=2_56			,		lowercase_       :  Union[str, Any]=2			,		lowercase_       :  int=[1_44, 1_92, 2_40]			,		lowercase_       :  List[str]=[16, 32, 64, 96, 1_28, 1_60, 6_40]			,		lowercase_       :  Tuple=4			,		lowercase_       :  Optional[int]=2.0			,		lowercase_       :  int=4.0			,		lowercase_       :  str="silu"			,		lowercase_       :  Optional[Any]=3			,		lowercase_       :  Dict=32			,		lowercase_       :  Union[str, Any]=0.1			,		lowercase_       :  str=0.0			,		lowercase_       :  str=0.1			,		lowercase_       :  List[str]=0.02			,		lowercase_       :  Union[str, Any]=1E-5			,		lowercase_       :  Tuple=True			,		lowercase_       :  Optional[Any]=2_56			,		lowercase_       :  List[str]=[6, 12, 18]			,		lowercase_       :  Any=0.1			,		lowercase_       :  List[str]=2_55			,		**lowercase_       :  Tuple			,		)      ->		Optional[Any]:
       super().__init__(**lowercase_						)

       lowercase__				:     List[str]					      =       num_channels
       lowercase__				:     Optional[int]					      =       image_size
       lowercase__				:     Any					      =       patch_size
       lowercase__				:     int					      =       hidden_sizes
       lowercase__				:     str					      =       neck_hidden_sizes
       lowercase__				:     Optional[int]					      =       num_attention_heads
       lowercase__				:     Tuple					      =       mlp_ratio
       lowercase__				:     Tuple					      =       expand_ratio
       lowercase__				:     List[str]					      =       hidden_act
       lowercase__				:     Optional[int]					      =       conv_kernel_size
       lowercase__				:     Union[str, Any]					      =       output_stride
       lowercase__				:     Optional[int]					      =       hidden_dropout_prob
       lowercase__				:     List[str]					      =       attention_probs_dropout_prob
       lowercase__				:     Union[str, Any]					      =       classifier_dropout_prob
       lowercase__				:     Union[str, Any]					      =       initializer_range
       lowercase__				:     List[str]					      =       layer_norm_eps
       lowercase__				:     Dict					      =       qkv_bias

       # decode head attributes for semantic segmentation
       lowercase__				:     List[Any]					      =       aspp_out_channels
       lowercase__				:     List[Any]					      =       atrous_rates
       lowercase__				:     str					      =       aspp_dropout_prob
       lowercase__				:     int					      =       semantic_loss_ignore_index



class 						snake_case_ (	__A ):
   __A			:	int        =       version.parse("1.11" )






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






   @property
   def    __UpperCamelCase  (   self       :  str						)      ->		Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([("logits", {0: "batch"})]						)
       else:
           return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)







   @property
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		float:
       return 1E-4



