"""simple docstring"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a							="\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

# TODO: Add description of the dataset here
# You can copy an official description
a							="\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n"

# TODO: Add a link to an official homepage for the dataset here
a							=""

# TODO: Add the licence for the dataset here if you can find it
a							=""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a							={
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}






class        __UpperCAmelCase						(     datasets.GeneratorBasedBuilder     ):
				A__				:       str  							=	datasets.Version('''1.1.0'''     )

				# This is an example of a dataset with multiple configurations.
				# If you don't want/need to define several sub-sets in your dataset,
				# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

				# If you need to make complex sub-parts in the datasets with configurable options
				# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
				# BUILDER_CONFIG_CLASS = MyBuilderConfig

				# You will be able to load one or the other configurations in the following list with
				# data = datasets.load_dataset('my_dataset', 'first_domain')
				# data = datasets.load_dataset('my_dataset', 'second_domain')
				A__				:       List[str]  							=	[
				    datasets.BuilderConfig(name='''first_domain'''				,				version=SCREAMING_SNAKE_CASE__				,				description='''This part of my dataset covers a first domain'''     ),
				    datasets.BuilderConfig(name='''second_domain'''				,				version=SCREAMING_SNAKE_CASE__				,				description='''This part of my dataset covers a second domain'''     ),
				]

				A__				:       List[str]  							=	'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.

				def 		_a     (				self				):

										if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
																lowerCamelCase__ =datasets.Features(
																    {
																        "sentence": datasets.Value("string"				),
																        "option1": datasets.Value("string"				),
																        "answer": datasets.Value("string"				)
																        # These are the features of your dataset like images, labels ...
																    }				)
										else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
																lowerCamelCase__ =datasets.Features(
																    {
																        "sentence": datasets.Value("string"				),
																        "option2": datasets.Value("string"				),
																        "second_domain_answer": datasets.Value("string"				)
																        # These are the features of your dataset like images, labels ...
																    }				)
										return datasets.DatasetInfo(
										    # This is the description that will appear on the datasets page.
										    description=_DESCRIPTION     ,			features=_lowerCamelCase     ,			homepage=_HOMEPAGE     ,			license=_LICENSE     ,			citation=_CITATION     ,			)

				def 		_a     (				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =_URLS[self.config.name]
										lowerCamelCase__ =dl_manager.download_and_extract(_lowerCamelCase				)
										return [
										    datasets.SplitGenerator(
										        name=datasets.Split.TRAIN     ,			gen_kwargs={
										            "filepath": os.path.join(_lowerCamelCase     ,			"train.jsonl"				),
										            "split": "train",
										        }     ,			),
										    datasets.SplitGenerator(
										        name=datasets.Split.VALIDATION     ,			gen_kwargs={
										            "filepath": os.path.join(_lowerCamelCase     ,			"dev.jsonl"				),
										            "split": "dev",
										        }     ,			),
										    datasets.SplitGenerator(
										        name=datasets.Split.TEST     ,			gen_kwargs={
										            "filepath": os.path.join(_lowerCamelCase     ,			"test.jsonl"				),
										            "split": "test"
										        }     ,			),
										]






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):

										with open(_lowerCamelCase     ,			encoding="utf-8"				) as f:
																for key, row in enumerate(_lowerCamelCase				):
																						lowerCamelCase__ =json.loads(_lowerCamelCase				)
																						if self.config.name == "first_domain":
																												# Yields examples as (key, example) tuples
																												yield key, {
																												    "sentence": data["sentence"],
																												    "option1": data["option1"],
																												    "answer": "" if split == "test" else data["answer"],
																												}
																						else:
																												yield key, {
																												    "sentence": data["sentence"],
																												    "option2": data["option2"],
																												    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
																												}



"""simple docstring"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


