"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
	a							=None


a							=logging.get_logger(__name__)

a							={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a							={
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

a							={
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}






class        __UpperCAmelCase						(     A__     ):
				A__				:       List[str]  							=	VOCAB_FILES_NAMES
				A__				:       Tuple  							=	PRETRAINED_VOCAB_FILES_MAP
				A__				:       Optional[Any]  							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				A__				:       Any  							=	['''input_ids''', '''attention_mask''']
				A__				:       Any  							=	XLMRobertaTokenizer

				def __init__(				self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<mask>"     ,			**_lowerCamelCase     ,			):
										# Mask token behave like a normal word, i.e. include the space before it
										lowerCamelCase__ =AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case				) if isinstance(__snake_case     ,			__snake_case				) else mask_token

										super().__init__(
										    __snake_case     ,			tokenizer_file=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			sep_token=__snake_case     ,			cls_token=__snake_case     ,			unk_token=__snake_case     ,			pad_token=__snake_case     ,			mask_token=__snake_case     ,			**__snake_case     ,			)

										lowerCamelCase__ =vocab_file
										lowerCamelCase__ =False if not self.vocab_file else True

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowerCamelCase__ =[self.cls_token_id]
										lowerCamelCase__ =[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):
										lowerCamelCase__ =[self.sep_token_id]
										lowerCamelCase__ =[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep				) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):
										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																    "tokenizer."				)

										if not os.path.isdir(__snake_case				):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
																return
										lowerCamelCase__ =os.path.join(
										    __snake_case     ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(__snake_case				):
																copyfile(self.vocab_file     ,			__snake_case				)

										return (out_vocab_file,)



"""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}''')


