


import os

import numpy
import onnx


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   Dict				)    ->	List[str]:





			"""simple docstring"""
			a__ :List[Any]						  =    a.name
			a__ :Optional[int]						  =    b.name

			a__ :int						  =    ''''''
			a__ :Union[str, Any]						  =    ''''''

			a__ :Any						  =    a == b

			a__ :Dict						  =    name_a
			a__ :List[Any]						  =    name_b

			return res


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   List[Any]	,  a	:   Dict				)    ->	Dict:





			"""simple docstring"""






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

			if node_proto.op_type == "If":
						_graph_replace_input_with(node_proto.attribute[0].g	,  a	,  a				)
						_graph_replace_input_with(node_proto.attribute[1].g	,  a	,  a				)
			if node_proto.op_type == "Loop":
						_graph_replace_input_with(node_proto.attribute[0].g	,  a	,  a				)


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   Optional[int]	,  a	:   Optional[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""






			for n in graph_proto.node:
						_node_replace_input_with(a	,  a	,  a				)


def 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   int	,  a	:   Any				)    ->	Tuple:





			"""simple docstring"""
			a__ :Any						  =    list(model.graph.initializer				)
			a__ :Tuple						  =    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__ :str						  =    inits[i].name
						a__ :Optional[int]						  =    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	,  a	,  a				)







def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	Optional[Any]:





			"""simple docstring"""
			a__ :str						  =    os.path.dirname(a				)
			a__ :Dict						  =    os.path.basename(a				)

			a__ :Optional[Any]						  =    onnx.load(os.path.join(a	,  a				)				)

			a__ :Any						  =    list(model.graph.initializer				)

			a__ :Dict						  =    set()
			a__ :Union[str, Any]						  =    {}
			a__ :Optional[Any]						  =    []

			a__ :Union[str, Any]						  =    0

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

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

												a__ :Dict						  =    inits[j].data_type
												a__ :Union[str, Any]						  =    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: "	,  a				)
												total_reduced_size += mem_size

												a__ :Optional[int]						  =    inits[i].name
												a__ :Optional[Any]						  =    inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(a				)
												else:
															a__ :str						  =    [name_j]
												ind_to_replace.append((j, i)				)

			print("total reduced size: "	,  total_reduced_size / 1_024 / 1_024 / 1_024	,  "GB"				)

			a__ :Any						  =    sorted(a				)
			_remove_dup_initializers_from_model(a	,  a	,  a				)

			a__ :List[Any]						  =    '''optimized_''' + model_file_name
			a__ :Optional[int]						  =    os.path.join(a	,  a				)
			onnx.save(a	,  a				)

			return new_model






import datasets

from .evaluate import evaluate


snake_case__			 =						'''\
@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}
}
'''

snake_case__			 =						'''
This metric wrap the official scoring script for version 1 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.
'''

snake_case__			 =						'''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''


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




						def  _snake_case					(	self						:   List[str]						)      ->Union[str, Any]:






									"""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						:   Dict   ,     __A						:   List[Any]   ,     __A						:   Optional[int]						)      ->str:






									"""simple docstring"""






									a__ :Optional[int]						  =    {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
									a__ :Optional[Any]						  =    [
									    {
									        "paragraphs": [
									            {
									                "qas": [
									                    {
									                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
									                        "id": ref["id"],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									a__ :Union[str, Any]						  =    evaluate(dataset=__A   ,     predictions=__A						)
									return score


