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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List




def      __magic_name__							(					A						:   List[str],    A						:   str  ):






			'''simple docstring'''







			if name != "training_function" and name != "main":
						raise ValueError(F"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""  )
			a       ,						a       =     [], False
			for line in lines:
						if not found_start and F"""def {name}""" in line:
									a       =     True
									good_lines.append(A  )
									continue
						if found_start:
									if name == "training_function" and "def main" in line:
												return good_lines
									if name == "main" and "if __name__" in line:
												return good_lines
									good_lines.append(A  )




def      __magic_name__							(					A						:   List[str]  ):






			'''simple docstring'''







			return [line for line in lines if not line.lstrip().startswith("#"  ) and line != "\n"]






def      __magic_name__							(					A						:   str,    A						:   str,    A						:   bool,    A						:   str = None  ):






			'''simple docstring'''







			with open(A,    "r"  ) as f:
						a       =     f.readlines()
			with open(os.path.abspath(os.path.join("examples",    "nlp_example.py"  )  ),    "r"  ) as f:
						a       =     f.readlines()
			with open(A,    "r"  ) as f:
						a       =     f.readlines()
			if secondary_filename is not None:
						with open(A,    "r"  ) as f:
									a       =     f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
			if parser_only:
						a       =     clean_lines(get_function_contents_by_name(A,    "main"  )  )
						a       =     clean_lines(get_function_contents_by_name(A,    "main"  )  )
						a       =     clean_lines(get_function_contents_by_name(A,    "main"  )  )
						if secondary_filename is not None:
									a       =     clean_lines(get_function_contents_by_name(A,    "main"  )  )
			else:
						a       =     clean_lines(get_function_contents_by_name(A,    "training_function"  )  )
						a       =     clean_lines(get_function_contents_by_name(A,    "training_function"  )  )
						a       =     clean_lines(get_function_contents_by_name(A,    "training_function"  )  )
						if secondary_filename is not None:
									a       =     clean_lines(
									    get_function_contents_by_name(A,    "training_function"  )  )

			a       =     "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

			# Specific code in our script that differs from the full version, aka what is new
			a       =     []
			a       =     []  # We keep track of the idxs just in case it's a repeated statement
			a       =     iter(A  )
			for i in range(len(A  ) - 1  ):
						if i not in passed_idxs:
									a       =     next(A  )
									if (line not in full_file_func) and (line.lstrip() != _dl_line):
												if "TESTING_MOCKED_DATALOADERS" not in line:
															new_feature_code.append(A  )
															passed_idxs.append(A  )
												else:
															# Skip over the `config['num_epochs'] = 2` statement
															a       =     next(A  )

    # Extract out just the new parts from the full_file_training_func
			a       =     []
			a       =     []  # We keep track of the idxs just in case it's a repeated statement
			for i, line in enumerate(A  ):
						if i not in passed_idxs:
									if (line not in full_file_func) and (line.lstrip() != _dl_line):
												if "TESTING_MOCKED_DATALOADERS" not in line:
															new_full_example_parts.append(A  )
															passed_idxs.append(A  )

    # Finally, get the overall diff
			a       =     [line for line in new_feature_code if line not in new_full_example_parts]
			if secondary_filename is not None:
						a       =     [line for line in full_file_contents if line not in secondary_file_func]
						a       =     [line for line in diff_from_example if line not in diff_from_two]

			return diff_from_example












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,
)


__lowerCAmelCase						:							Optional[int]			     =						'\\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						:							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'

__lowerCAmelCase						:							Optional[Any]			     =						'\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        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							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							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

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

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

							if has_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "HasAns"				)
							if no_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "NoAns"				)
							find_all_best_thresh(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							return dict(__lowerCamelCase				)






