



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_	       =   logging.get_logger(__name__)

UpperCamelCase_	       =   {
    "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
    "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
    "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
    "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}







class 							a_  (		_snake_case				):
						UpperCamelCase__      :						Any				       ="flaubert"
						UpperCamelCase__      :						List[Any]				       ={
						    "hidden_size": "emb_dim",
						    "num_attention_heads": "n_heads",
						    "num_hidden_layers": "n_layers",
						    "n_words": "vocab_size",  # For backward compatibility
						}

						def __init__(    self	:List[Any]   ,						_lowercase	:str=False   ,						_lowercase	:Optional[Any]=0.0   ,						_lowercase	:int=30145   ,						_lowercase	:Tuple=2048   ,						_lowercase	:List[str]=12   ,						_lowercase	:Optional[int]=16   ,						_lowercase	:Tuple=0.1   ,						_lowercase	:Any=0.1   ,						_lowercase	:List[str]=True   ,						_lowercase	:List[Any]=False   ,						_lowercase	:Union[str, Any]=False   ,						_lowercase	:Tuple=False   ,						_lowercase	:Tuple=1   ,						_lowercase	:List[Any]=True   ,						_lowercase	:List[Any]=512   ,						_lowercase	:List[str]=2048**-0.5   ,						_lowercase	:List[Any]=1E-1_2   ,						_lowercase	:Union[str, Any]=0.02   ,						_lowercase	:Optional[Any]=0   ,						_lowercase	:List[Any]=1   ,						_lowercase	:Any=2   ,						_lowercase	:int=3   ,						_lowercase	:Tuple=5   ,						_lowercase	:Optional[int]=True   ,						_lowercase	:int="first"   ,						_lowercase	:Optional[int]=True   ,						_lowercase	:List[Any]=None   ,						_lowercase	:Any=True   ,						_lowercase	:Optional[int]=0.1   ,						_lowercase	:Optional[int]=5   ,						_lowercase	:List[str]=5   ,						_lowercase	:List[Any]=0   ,						_lowercase	:List[str]=0   ,						_lowercase	:Dict=2   ,						_lowercase	:List[str]=0   ,						**_lowercase	:Union[str, Any]   ,						)		->					List[str]:
												UpperCAmelCase_     							=     pre_norm
												UpperCAmelCase_     							=     layerdrop
												UpperCAmelCase_     							=     vocab_size
												UpperCAmelCase_     							=     emb_dim
												UpperCAmelCase_     							=     n_layers
												UpperCAmelCase_     							=     n_heads
												UpperCAmelCase_     							=     dropout
												UpperCAmelCase_     							=     attention_dropout
												UpperCAmelCase_     							=     gelu_activation
												UpperCAmelCase_     							=     sinusoidal_embeddings
												UpperCAmelCase_     							=     causal
												UpperCAmelCase_     							=     asm
												UpperCAmelCase_     							=     n_langs
												UpperCAmelCase_     							=     use_lang_emb
												UpperCAmelCase_     							=     layer_norm_eps
												UpperCAmelCase_     							=     bos_index
												UpperCAmelCase_     							=     eos_index
												UpperCAmelCase_     							=     pad_index
												UpperCAmelCase_     							=     unk_index
												UpperCAmelCase_     							=     mask_index
												UpperCAmelCase_     							=     is_encoder
												UpperCAmelCase_     							=     max_position_embeddings
												UpperCAmelCase_     							=     embed_init_std
												UpperCAmelCase_     							=     init_std
												UpperCAmelCase_     							=     summary_type
												UpperCAmelCase_     							=     summary_use_proj
												UpperCAmelCase_     							=     summary_activation
												UpperCAmelCase_     							=     summary_proj_to_labels
												UpperCAmelCase_     							=     summary_first_dropout
												UpperCAmelCase_     							=     start_n_top
												UpperCAmelCase_     							=     end_n_top
												UpperCAmelCase_     							=     mask_token_id
												UpperCAmelCase_     							=     lang_id

												if "n_words" in kwargs:
																		UpperCAmelCase_     							=     kwargs['''n_words''']

												super().__init__(pad_token_id=_lowercase   ,						bos_token_id=_lowercase   ,						**_lowercase)







class 							a_  (		_snake_case				):

						@property
						def 	__a       (    self	:List[Any])		->					Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																		UpperCAmelCase_     							=     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		UpperCAmelCase_     							=     {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ])










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_	       =   "\\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"

UpperCamelCase_	       =   "\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"

UpperCamelCase_	       =   "\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 							a_  (		datasets.Metric				):

						def 	__a       (    self	:List[Any])		->					List[str]:
												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 	__a       (    self	:int   ,						_lowercase	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[Any]=1.0)		->					str:
												UpperCAmelCase_     							=     {p['''id''']: p['''no_answer_probability'''] for p in predictions}
												UpperCAmelCase_     							=     [{'''paragraphs''': [{'''qas''': references}]}]
												UpperCAmelCase_     							=     {p['''id''']: p['''prediction_text'''] for p in predictions}

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

												UpperCAmelCase_							,      UpperCAmelCase_     							=     get_raw_scores(_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase)

												if has_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''HasAns''')
												if no_ans_qids:
																		UpperCAmelCase_     							=     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)





