

import datasets

from .evaluate import evaluate


_lowerCAmelCase					:			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"

_lowerCAmelCase					:			List[Any]  				=			"\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"

_lowerCAmelCase					:			Any  				=			"\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 	__magic_name__    (			datasets.Metric      ):





			def 		__magic_name__    (		self					)   -> List[str]:




						'''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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					)   -> Tuple:




						'''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=__snake_case					,  predictions=__snake_case					)
						return score









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






