"""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()

UpperCamelCase_    :    int											=						{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

UpperCamelCase_    :    List[Any]											=						datasets.load_dataset("""xnli""", split="""test+validation""")

UpperCamelCase_    :    List[Any]											=						0
UpperCamelCase_    :    List[str]											=						0
UpperCamelCase_    :    Any											=						0
UpperCamelCase_    :    str											=						0


def 					a__ (						UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     List[Any]     )->		List[str]:
						if spm_diff == list(reversed(UpperCAmelCase__     )     ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(UpperCAmelCase__     ) == len(UpperCAmelCase__     ) and fast.decode(UpperCAmelCase__     ) == fast.decode(UpperCAmelCase__     ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						UpperCAmelCase__   		=						slow.encode(slow.decode(UpperCAmelCase__     )     )
						UpperCAmelCase__   		=						fast.encode(fast.decode(UpperCAmelCase__     )     )
						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 					a__ (						UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     str     )->		List[str]:
						UpperCAmelCase__   		=						fast.encode_plus(UpperCAmelCase__     )[0]
						UpperCAmelCase__   		=						enc.offsets
						UpperCAmelCase__		, UpperCAmelCase__   		=						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 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     str     )->		str:
						# Encoding can be the same with same result AAA -> A + AA vs AA + A
						# We can check that we use at least exactly the same number of tokens.
						for i, (spm_id, tok_id) in enumerate(zip(UpperCAmelCase__						,     UpperCAmelCase__     )     ):
												if spm_id != tok_id:
																		break
						UpperCAmelCase__   		=						i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(UpperCAmelCase__     )						,     reversed(UpperCAmelCase__     )     )     ):
												if spm_id != tok_id:
																		break
						UpperCAmelCase__   		=						len(UpperCAmelCase__     ) - i

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

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

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

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

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

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

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


def 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     str     )->		Optional[Any]:
						global perfect
						global imperfect
						global wrong
						global total

						UpperCAmelCase__   		=						slow.encode(UpperCAmelCase__     )
						UpperCAmelCase__   		=						fast.encode(UpperCAmelCase__     )

						UpperCAmelCase__   		=						False
						total += 1

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

						if total % 1_0_0_0_0 == 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(UpperCAmelCase__     )}\n{fast.tokenize(UpperCAmelCase__     )}'


def 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Tuple     )->		str:
						global batch_total
						for i in range(len(UpperCAmelCase__     )     ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(UpperCAmelCase__						,     UpperCAmelCase__						,     UpperCAmelCase__     )

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


if __name__ == "__main__":
						for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
												UpperCamelCase_    :    List[Any]											=						list(slow_class.max_model_input_sizes.keys())
												for checkpoint in checkpoint_names:
																		UpperCamelCase_    :    Optional[Any]											=						0
																		UpperCamelCase_    :    List[Any]											=						0
																		UpperCamelCase_    :    Any											=						0
																		UpperCamelCase_    :    Optional[Any]											=						0

																		print(F"========================== Checking {name}: {checkpoint} ==========================")
																		UpperCamelCase_    :    List[str]											=						slow_class.from_pretrained(checkpoint, force_download=True)
																		UpperCamelCase_    :    Dict											=						fast_class.from_pretrained(checkpoint, force_download=True)
																		test_tokenizer(slow, fast)
																		print(F"Accuracy {perfect * 100 / total:.2f}")


"""simple docstring"""







from __future__ import annotations


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						assert (
						    isinstance(UpperCAmelCase__						,     UpperCAmelCase__     ) and mask > 0
						), f'mask needs to be positive integer, your input {mask}'
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						mask

						while submask:
												all_submasks.append(UpperCAmelCase__     )
												UpperCAmelCase__   		=						(submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()

