
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 		__magic_name__				( datasets.Metric					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    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     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__=1.0		):
    _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(a__		)  # 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(a__	,		a__		)
    _lowerCamelCase      =						apply_no_ans_threshold(a__	,		a__	,		a__	,		a__		)
    _lowerCamelCase      =						apply_no_ans_threshold(a__	,		a__	,		a__	,		a__		)
    _lowerCamelCase      =						make_eval_dict(a__	,		a__		)

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






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase    			=					logging.get_logger(__name__)

_UpperCAmelCase    			=					{
    "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
    "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
    "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
    "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
    "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
}

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = "gpt2"
  _UpperCamelCase						 = ["past_key_values"]
  _UpperCamelCase						 = {
      "hidden_size": "n_embd",
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }


  def __init__(					self	,		a__=5_02_57	,		a__=10_24	,		a__=7_68	,		a__=12	,		a__=12	,		a__=None	,		a__="gelu_new"	,		a__=0.1	,		a__=0.1	,		a__=0.1	,		a__=1E-5	,		a__=0.02	,		a__="cls_index"	,		a__=True	,		a__=None	,		a__=True	,		a__=0.1	,		a__=True	,		a__=True	,		a__=5_02_56	,		a__=5_02_56	,		a__=False	,		a__=False	,		**a__	,		):
    _lowerCamelCase      =						vocab_size
    _lowerCamelCase      =						n_positions
    _lowerCamelCase      =						n_embd
    _lowerCamelCase      =						n_layer
    _lowerCamelCase      =						n_head
    _lowerCamelCase      =						n_inner
    _lowerCamelCase      =						activation_function
    _lowerCamelCase      =						resid_pdrop
    _lowerCamelCase      =						embd_pdrop
    _lowerCamelCase      =						attn_pdrop
    _lowerCamelCase      =						layer_norm_epsilon
    _lowerCamelCase      =						initializer_range
    _lowerCamelCase      =						summary_type
    _lowerCamelCase      =						summary_use_proj
    _lowerCamelCase      =						summary_activation
    _lowerCamelCase      =						summary_first_dropout
    _lowerCamelCase      =						summary_proj_to_labels
    _lowerCamelCase      =						scale_attn_weights
    _lowerCamelCase      =						use_cache
    _lowerCamelCase      =						scale_attn_by_inverse_layer_idx
    _lowerCamelCase      =						reorder_and_upcast_attn

    _lowerCamelCase      =						bos_token_id
    _lowerCamelCase      =						eos_token_id

    super().__init__(bos_token_id=a__	,		eos_token_id=a__	,		**a__		)







class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		a__	,		a__ = "default"	,		a__ = None	,		a__ = False	,		):
    super().__init__(a__	,		task=a__	,		patching_specs=a__	,		use_past=a__		)
    if not getattr(self._config	,		'''pad_token_id'''	,		a__		):
      # TODO: how to do that better?
      _lowerCamelCase      =						0


  @property
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
    if self.use_past:
      self.fill_with_past_key_values_(a__	,		direction='''inputs'''		)
      _lowerCamelCase      =						{0: '''batch''', 1: '''past_sequence + sequence'''}
    else:
      _lowerCamelCase      =						{0: '''batch''', 1: '''sequence'''}

    return common_inputs


  @property
  def     _UpperCAmelCase  (					self		):
    return self._config.n_layer


  @property
  def     _UpperCAmelCase  (					self		):
    return self._config.n_head


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = -1	,		a__ = -1	,		a__ = False	,		a__ = None	,		):
    _lowerCamelCase      =						super(a__	,		self		).generate_dummy_inputs(
        a__	,		batch_size=a__	,		seq_length=a__	,		is_pair=a__	,		framework=a__		)

    # We need to order the input in the way they appears in the forward()
    _lowerCamelCase      =						OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

    # Need to add the past_keys
    if self.use_past:
      if not is_torch_available():
        raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
      else:
        import torch

        _lowerCamelCase    ,    _lowerCamelCase      =						common_inputs['''input_ids'''].shape
        # Not using the same length for past_key_values
        _lowerCamelCase      =						seqlen + 2
        _lowerCamelCase      =						(
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        _lowerCamelCase      =						[
            (torch.zeros(a__		), torch.zeros(a__		)) for _ in range(self.num_layers		)
        ]

    _lowerCamelCase      =						common_inputs['''attention_mask''']
    if self.use_past:
      _lowerCamelCase      =						ordered_inputs['''attention_mask'''].dtype
      _lowerCamelCase      =						torch.cat(
          [ordered_inputs['''attention_mask'''], torch.ones(a__	,		a__	,		dtype=a__		)]	,		dim=1		)

    return ordered_inputs


  @property
  def     _UpperCAmelCase  (					self		):
    return 13




