

import os

import numpy
import onnx
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				List[str]:
    A__   				=       a.name
    A__   				=       b.name

    A__   				=       ''
    A__   				=       ''

    A__   				=       a == b

    A__   				=       name_a
    A__   				=       name_b

    return res
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[str]				)							->				Optional[Any]:






    for i, input_name in enumerate(node_proto.input				):
        if input_name == name:
            node_proto.input.insert(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
            node_proto.input.pop(i + 1				)

    if node_proto.op_type == "If":
        _graph_replace_input_with(node_proto.attribute[0].g   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
        _graph_replace_input_with(node_proto.attribute[1].g   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    if node_proto.op_type == "Loop":
        _graph_replace_input_with(node_proto.attribute[0].g   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				Any:






    for n in graph_proto.node:
        _node_replace_input_with(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Dict				)							->				Any:
    A__   				=       list(model.graph.initializer				)
    A__   				=       list(model_without_ext.graph.initializer				)
    for i, ref_i in ind_to_replace:
        assert inits_with_data[i].name == inits[i].name
        assert inits_with_data[ref_i].name == inits[ref_i].name
        assert i > ref_i

        A__   				=       inits[i].name
        A__   				=       inits[ref_i].name

        model_without_ext.graph.initializer.remove(inits[i]				)

        # for n in model.graph.node:
        _graph_replace_input_with(model_without_ext.graph   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:
    A__   				=       os.path.dirname(SCREAMING_SNAKE_CASE__				)
    A__   				=       os.path.basename(SCREAMING_SNAKE_CASE__				)

    A__   				=       onnx.load(os.path.join(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)

    A__   				=       list(model.graph.initializer				)

    A__   				=       set()
    A__   				=       {}
    A__   				=       []

    A__   				=       0

    for i in range(len(SCREAMING_SNAKE_CASE__				)				):
        if i in dup_set:
            continue

        for j in range(i + 1   ,      len(SCREAMING_SNAKE_CASE__				)				):
            if j in dup_set:
                continue
            if _is_equal_tensor_proto(inits[i]   ,      inits[j]				):
                dup_set.add(SCREAMING_SNAKE_CASE__				)
                dup_set.add(SCREAMING_SNAKE_CASE__				)

                A__   				=       inits[j].data_type
                A__   				=       numpy.prod(inits[j].dims				)
                if dtype == 1:
                    mem_size *= 4
                elif dtype == 6:
                    mem_size *= 4
                elif dtype == 7 or dtype == 11:
                    mem_size *= 8
                else:
                    print('unexpected data type: '   ,      SCREAMING_SNAKE_CASE__				)
                total_reduced_size += mem_size

                A__   				=       inits[i].name
                A__   				=       inits[j].name

                if name_i in dup_map:
                    dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                else:
                    A__   				=       [name_j]
                ind_to_replace.append((j, i)				)

    print('total reduced size: '   ,      total_reduced_size / 1024 / 1024 / 1024   ,      'GB'				)

    A__   				=       sorted(SCREAMING_SNAKE_CASE__				)
    _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    A__   				=       'optimized_' + model_file_name
    A__   				=       os.path.join(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    onnx.save(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    return new_model




import datasets

from .evaluate import evaluate


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

lowercase_			=  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

lowercase_			=  "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"


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







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': {'id': datasets.Value('string'		), 'prediction_text': datasets.Value('string'		)},
                    '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			:	str,lowercase_			:	List[Any],lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       {prediction['id']: prediction['prediction_text'] for prediction in predictions}
        A__   				=       [
            {
                'paragraphs': [
                    {
                        'qas': [
                            {
                                'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                'id': ref['id'],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        A__   				=       evaluate(dataset=lowercase_,predictions=lowercase_		)
        return score

