






"""simple docstring"""



import subprocess
import sys
import warnings
from argparse import ArgumentParser
from pathlib import Path

from packaging import version

from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
from ..utils import logging
from ..utils.import_utils import is_optimum_available
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import get_preprocessor


_snake_case       :      Any       =					"1.5.0"

_snake_case       :      Tuple       =					["vision-encoder-decoder"]




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			):
					if is_optimum_available():
										from optimum.version import __version__ as optimum_version

										lowerCamelCase__				=version.parse(__A			)
										if parsed_optimum_version < version.parse(__A			):
															raise RuntimeError(
															    f"transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You "
															    'can upgrade optimum by running: pip install -U optimum[exporters]'			)
					else:
										raise RuntimeError(
										    'transformers.onnx requires optimum to run, you can install the library by running: pip install '
										    'optimum[exporters]'			)
					lowerCamelCase__				=[
					    sys.executable,
					    '-m',
					    'optimum.exporters.onnx',
					    f"--model {args.model}",
					    f"--task {args.feature}",
					    f"--framework {args.framework}" if args.framework is not None else '',
					    f"{args.output}",
					]
					lowerCamelCase__				=subprocess.Popen(' '.join(__A			)			,	stdout=subprocess.PIPE			,	shell=__A			)
					proc.wait()

					logger.info(
					    'The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as '
					    'transformers.onnx is deprecated, and will be removed in v5. You can find more information here: '
					    'https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'			)




def 						lowerCamelCase_				(  __A  :		Optional[Any]			):
					lowerCamelCase__				=args.output if args.output.is_file() else args.output.joinpath('model.onnx'			)
					if not args.output.parent.exists():
										args.output.parent.mkdir(parents=__A			)

					# Allocate the model
					lowerCamelCase__				=FeaturesManager.get_model_from_feature(
					    args.feature			,	args.model			,	framework=args.framework			,	cache_dir=args.cache_dir			)

					lowerCamelCase__				,     lowerCamelCase__				=FeaturesManager.check_supported_model_or_raise(__A			,	feature=args.feature			)
					lowerCamelCase__				=model_onnx_config(model.config			)

					if model_kind in ENCODER_DECODER_MODELS:
										lowerCamelCase__				=model.get_encoder()
										lowerCamelCase__				=model.get_decoder()

										lowerCamelCase__				=onnx_config.get_encoder_config(encoder_model.config			)
										lowerCamelCase__				=onnx_config.get_decoder_config(
										    encoder_model.config			,	decoder_model.config			,	feature=args.feature			)

										if args.opset is None:
															lowerCamelCase__				=max(encoder_onnx_config.default_onnx_opset			,	decoder_onnx_config.default_onnx_opset			)

										if args.opset < min(encoder_onnx_config.default_onnx_opset			,	decoder_onnx_config.default_onnx_opset			):
															raise ValueError(
															    f"Opset {args.opset} is not sufficient to export {model_kind}. At least "
															    f" {min(encoder_onnx_config.default_onnx_opset			,	decoder_onnx_config.default_onnx_opset			)} is required."			)

										lowerCamelCase__				=AutoFeatureExtractor.from_pretrained(args.model			)

										lowerCamelCase__				,     lowerCamelCase__				=export(
										    __A			,	__A			,	__A			,	args.opset			,	args.output.parent.joinpath('encoder_model.onnx'			)			,	)

										validate_model_outputs(
										    __A			,	__A			,	__A			,	args.output.parent.joinpath('encoder_model.onnx'			)			,	__A			,	args.atol if args.atol else encoder_onnx_config.atol_for_validation			,	)

										lowerCamelCase__				=AutoTokenizer.from_pretrained(args.model			)

										lowerCamelCase__				,     lowerCamelCase__				=export(
										    __A			,	__A			,	__A			,	args.opset			,	args.output.parent.joinpath('decoder_model.onnx'			)			,	)

										validate_model_outputs(
										    __A			,	__A			,	__A			,	args.output.parent.joinpath('decoder_model.onnx'			)			,	__A			,	args.atol if args.atol else decoder_onnx_config.atol_for_validation			,	)
										logger.info(
										    f"All good, model saved at: {args.output.parent.joinpath('encoder_model.onnx'			).as_posix()},"
										    f" {args.output.parent.joinpath('decoder_model.onnx'			).as_posix()}"			)

					else:
										# Instantiate the appropriate preprocessor
										if args.preprocessor == "auto":
															lowerCamelCase__				=get_preprocessor(args.model			)
										elif args.preprocessor == "tokenizer":
															lowerCamelCase__				=AutoTokenizer.from_pretrained(args.model			)
										elif args.preprocessor == "image_processor":
															lowerCamelCase__				=AutoImageProcessor.from_pretrained(args.model			)
										elif args.preprocessor == "feature_extractor":
															lowerCamelCase__				=AutoFeatureExtractor.from_pretrained(args.model			)
										elif args.preprocessor == "processor":
															lowerCamelCase__				=AutoProcessor.from_pretrained(args.model			)
										else:
															raise ValueError(f"Unknown preprocessor type '{args.preprocessor}'"			)

										# Ensure the requested opset is sufficient
										if args.opset is None:
															lowerCamelCase__				=onnx_config.default_onnx_opset

										if args.opset < onnx_config.default_onnx_opset:
															raise ValueError(
															    f"Opset {args.opset} is not sufficient to export {model_kind}. "
															    f"At least  {onnx_config.default_onnx_opset} is required."			)

										lowerCamelCase__				,     lowerCamelCase__				=export(
										    __A			,	__A			,	__A			,	args.opset			,	args.output			,	)

										if args.atol is None:
															lowerCamelCase__				=onnx_config.atol_for_validation

										validate_model_outputs(__A			,	__A			,	__A			,	args.output			,	__A			,	args.atol			)
										logger.info(f"All good, model saved at: {args.output.as_posix()}"			)
										warnings.warn(
										    'The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend'
										    ' using optimum.exporters.onnx in future. You can find more information here:'
										    ' https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model.'			,	__A			,	)




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=ArgumentParser('Hugging Face Transformers ONNX exporter'			)
					parser.add_argument(
					    '-m'			,	'--model'			,	type=__A			,	required=__A			,	help='Model ID on huggingface.co or path on disk to load model from.'			)
					parser.add_argument(
					    '--feature'			,	default='default'			,	help='The type of features to export the model with.'			,	)
					parser.add_argument('--opset'			,	type=__A			,	default=__A			,	help='ONNX opset version to export the model with.'			)
					parser.add_argument(
					    '--atol'			,	type=__A			,	default=__A			,	help='Absolute difference tolerance when validating the model.'			)
					parser.add_argument(
					    '--framework'			,	type=__A			,	choices=['pt', 'tf']			,	default=__A			,	help=(
					        'The framework to use for the ONNX export.'
					        ' If not provided, will attempt to use the local checkpoint\'s original framework'
					        ' or what is available in the environment.'
					    )			,	)
					parser.add_argument('output'			,	type=__A			,	help='Path indicating where to store generated ONNX model.'			)
					parser.add_argument('--cache_dir'			,	type=__A			,	default=__A			,	help='Path indicating where to store cache.'			)
					parser.add_argument(
					    '--preprocessor'			,	type=__A			,	choices=['auto', 'tokenizer', 'feature_extractor', 'image_processor', 'processor']			,	default='auto'			,	help='Which type of preprocessor to use. \'auto\' tries to automatically detect it.'			,	)
					parser.add_argument(
					    '--export_with_transformers'			,	action='store_true'			,	help=(
					        'Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be '
					        'useful when exporting a model supported in transformers but not in optimum, otherwise it is not '
					        'recommended.'
					    )			,	)

					lowerCamelCase__				=parser.parse_args()
					if args.export_with_transformers or not is_optimum_available():
										export_with_transformers(__A			)
					else:
										export_with_optimum(__A			)


if __name__ == "__main__":
						_snake_case       :      Optional[Any]       =					logging.get_logger("transformers.onnx")  # pylint: disable=invalid-name
						logger.setLevel(logging.INFO)
						main()







"""simple docstring"""



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,
)


_snake_case       :      Dict       =					"\\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"

_snake_case       :      List[str]       =					"\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering 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\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

_snake_case       :      Dict       =					"\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no 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 list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'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}\n"




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






						def 		lowerCAmelCase   (					self)			->  int:




											"""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'),
											                '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 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE=1.0)			->  Optional[int]:




											"""simple docstring"""





											lowerCamelCase__				={p['id']: p['no_answer_probability'] for p in predictions}
											lowerCamelCase__				=[{'paragraphs': [{'qas': references}]}]
											lowerCamelCase__				={p['id']: p['prediction_text'] for p in predictions}

											lowerCamelCase__				=make_qid_to_has_ans(_SCREAMING_SNAKE_CASE)  # maps qid to True/False
											lowerCamelCase__				=[k for k, v in qid_to_has_ans.items() if v]
											lowerCamelCase__				=[k for k, v in qid_to_has_ans.items() if not v]

											lowerCamelCase__				,     lowerCamelCase__				=get_raw_scores(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=apply_no_ans_threshold(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=apply_no_ans_threshold(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)

											if has_ans_qids:
																lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						qid_list=_SCREAMING_SNAKE_CASE)
																merge_eval(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						'HasAns')
											if no_ans_qids:
																lowerCamelCase__				=make_eval_dict(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						qid_list=_SCREAMING_SNAKE_CASE)
																merge_eval(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						'NoAns')
											find_all_best_thresh(_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)
											return dict(_SCREAMING_SNAKE_CASE)