




'''simple docstring'''







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	lowerCamelCase_				(			UpperCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					PreTrainedTokenizerFast
					lowercase__       =					False
					lowercase__       =					True
					lowercase__       =					"""tokenizer_file"""






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			False  # because we don't have pretrained_vocab_files_map
							super().setUp()
							_snake_case      			=			True

							_snake_case      			=			["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
							_snake_case      			=			"SaulLu/dummy-tokenizer-bytelevel-bpe"

							# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
							_snake_case      			=			[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained(model_paths[0]					)
							tokenizer.save_pretrained(self.tmpdirname					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer_with_special_tokens_change()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name					)

							_snake_case      			=			("a" for _ in range(1_000					))
							_snake_case      			=			tokenizer.train_new_from_iterator(text_iterator=__UpperCamelCase,    length=1_000,    vocab_size=50					)

							_snake_case      			=			new_tokenizer.encode("a🤗"					)
							self.assertEqual(__UpperCamelCase,    [64, 172, 253, 97, 245]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			AutoTokenizer.from_pretrained("bert-base-cased"					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							_snake_case      			=			len(__UpperCamelCase					)

							with tempfile.TemporaryDirectory() as tmp_dir:
									# Hack to save this in the tokenizer_config.json
									_snake_case      			=			["tokenizer.4.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									json.dump(__UpperCamelCase,    open(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    "w"					)					)

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					) + 1					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

									# Will need to be adjusted if we reach v42 and this test is still here.
									# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
									shutil.move(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    os.path.join(__UpperCamelCase,    "tokenizer.42.0.0.json"					)					)
									_snake_case      			=			["tokenizer.42.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					)					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
							_snake_case      			=			"hf-internal-testing/test-two-tokenizers"

							# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
							_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_997					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

							# Testing an older version by monkey-patching the version in the module it's used.
							import transformers as old_transformers

							_snake_case      			=			"3.0.0"
							_snake_case      			=			old_transformers.models.auto.AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_996					)
							_snake_case      			=			json.loads(old_tokenizer._tokenizer.to_str()					)
							self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# See https://github.com/huggingface/transformers/pull/12550
							# and https://github.com/huggingface/tokenizers/issues/537
							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"					)
							_snake_case      			=			"The Matrix is a 1999 science fiction action film."

							with concurrent.futures.ThreadPoolExecutor() as executor:
									_snake_case      			=			[executor.submit(self.fetch,    __UpperCamelCase,    __UpperCamelCase					) for i in range(10					)]
									_snake_case      			=			[future.result() for future in futures]
									self.assertEqual(__UpperCamelCase,    [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10					)]					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							return tokenizer.encode(__UpperCamelCase,    truncation="longest_first",    padding="longest"					)











'''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,
)


_lowerCamelCase				:		str				      =    '\\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				:		Union[str, Any]				      =    '\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				:		List[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 	lowerCamelCase_				(			datasets.Metric				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       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 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1.0					):
							_snake_case      			=			{p["id"]: p["no_answer_probability"] for p in predictions}
							_snake_case      			=			[{"paragraphs": [{"qas": references}]}]
							_snake_case      			=			{p["id"]: p["prediction_text"] for p in predictions}

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

							_snake_case			,       _snake_case      			=			get_raw_scores(__UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase					)

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





