






"""simple docstring"""



from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


_snake_case       :      Any       =					logging.get_logger(__name__)

_snake_case       :      List[str]       =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_snake_case       :      str       =					{
    "vocab_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json",
    },
    "merges_file": {
        "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "openai/clip-vit-base-patch32": (
            "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case       :      Optional[int]       =					{
    "openai/clip-vit-base-patch32": 77,
}




class        __lowercase(       _UpperCamelCase  ):
						lowerCAmelCase_		:							Any=       VOCAB_FILES_NAMES
						lowerCAmelCase_		:							str=       PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_		:							List[str]=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_		:							int=       ["""input_ids""", """attention_mask"""]
						lowerCAmelCase_		:							str=       CLIPTokenizer






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE=None    ,						_SCREAMING_SNAKE_CASE=None    ,						_SCREAMING_SNAKE_CASE=None    ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"    ,						_SCREAMING_SNAKE_CASE="<|startoftext|>"    ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"    ,						_SCREAMING_SNAKE_CASE="<|endoftext|>"    ,						**_SCREAMING_SNAKE_CASE    ,						)			->  Optional[Any]:




											"""simple docstring"""





											super().__init__(
											    _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						tokenizer_file=_SCREAMING_SNAKE_CASE    ,						unk_token=_SCREAMING_SNAKE_CASE    ,						bos_token=_SCREAMING_SNAKE_CASE    ,						eos_token=_SCREAMING_SNAKE_CASE    ,						pad_token=_SCREAMING_SNAKE_CASE    ,						**_SCREAMING_SNAKE_CASE    ,						)

											if not isinstance(self.backend_tokenizer.pre_tokenizer    ,						pre_tokenizers.Sequence):
																raise ValueError(
																    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																    ' to be compatible with this version.The easiest way to do so is'
																    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																    ' transformers.')

											self._wrap_decode_method_backend_tokenizer()






						def 		lowerCAmelCase   (					self)			->  Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__				=self.backend_tokenizer.decode

											def new_decode_method(*_SCREAMING_SNAKE_CASE    ,						**_SCREAMING_SNAKE_CASE):
																lowerCamelCase__				=orig_decode_method(*_SCREAMING_SNAKE_CASE    ,						**_SCREAMING_SNAKE_CASE)
																lowerCamelCase__				=text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,						' ').strip()
																return text

											lowerCamelCase__				=new_decode_method






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None)			->  List[int]:




											"""simple docstring"""





											lowerCamelCase__				=[self.bos_token_id]
											lowerCamelCase__				=[self.eos_token_id]

											if token_ids_a is None:
																return bos_token + token_ids_a + eos_token
											return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None)			->  List[int]:




											"""simple docstring"""





											lowerCamelCase__				=[self.bos_token_id]
											lowerCamelCase__				=[self.eos_token_id]

											if token_ids_a is None:
																return len(bos_token + token_ids_a + eos_token) * [0]
											return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE = None)			->  Tuple[str]:




											"""simple docstring"""





											lowerCamelCase__				=self._tokenizer.model.save(_SCREAMING_SNAKE_CASE    ,						name=_SCREAMING_SNAKE_CASE)
											return tuple(_SCREAMING_SNAKE_CASE)







"""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)