


'''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_		:       List[Any]							       =		{
    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_		:       Optional[Any]							       =		0
UpperCAmelCase_		:       Optional[Any]							       =		0
UpperCAmelCase_		:       Any							       =		0
UpperCAmelCase_		:       List[str]							       =		0







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if spm_diff == list(reversed(SCREAMING_SNAKE_CASE__					)					):
								# AAA -> AA+A vs A+AA case.
								return True
				elif len(SCREAMING_SNAKE_CASE__					) == len(SCREAMING_SNAKE_CASE__					) and fast.decode(SCREAMING_SNAKE_CASE__					) == fast.decode(SCREAMING_SNAKE_CASE__					):
								# Second order OK
								# Barrich -> Barr + ich vs Bar + rich
								return True
				_SCREAMING_SNAKE_CASE			:	str							=				slow.encode(slow.decode(SCREAMING_SNAKE_CASE__					)					)
				_SCREAMING_SNAKE_CASE			:	Any							=				fast.encode(fast.decode(SCREAMING_SNAKE_CASE__					)					)
				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       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[str]							=				fast.encode_plus(SCREAMING_SNAKE_CASE__					)[0]
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				enc.offsets
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				for i, (spm_id, tok_id) in enumerate(zip(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)					):
								if spm_id != tok_id:
												break
				_SCREAMING_SNAKE_CASE			:	int							=				i
				for i, (spm_id, tok_id) in enumerate(zip(reversed(SCREAMING_SNAKE_CASE__					)							,				reversed(SCREAMING_SNAKE_CASE__					)					)					):
								if spm_id != tok_id:
												break
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				len(SCREAMING_SNAKE_CASE__					) - i

				_SCREAMING_SNAKE_CASE			:	Tuple							=				spm_ids[first:last]
				_SCREAMING_SNAKE_CASE			:	Tuple							=				tok_ids[first:last]

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

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

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

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

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

				_SCREAMING_SNAKE_CASE			:	int							=				fast.decode(spm_ids[:first]					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				fast.decode(spm_ids[last:]					)
				_SCREAMING_SNAKE_CASE			:	int							=				fast.decode(spm_ids[first:last]					)
				print()
				print(SCREAMING_SNAKE_CASE__					)
				return False







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				global perfect
				global imperfect
				global wrong
				global total

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				slow.encode(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				fast.encode(SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	Any							=				False
				total += 1

				if slow_ids != fast_ids:
								if check_details(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):
												_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				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(SCREAMING_SNAKE_CASE__					)}\n{fast.tokenize(SCREAMING_SNAKE_CASE__					)}"""







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

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


if __name__ == "__main__":
				for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
								UpperCAmelCase_		:       Optional[Any]							       =		list(slow_class.max_model_input_sizes.keys())
								for checkpoint in checkpoint_names:
												UpperCAmelCase_		:       int							       =		0
												UpperCAmelCase_		:       int							       =		0
												UpperCAmelCase_		:       Dict							       =		0
												UpperCAmelCase_		:       Union[str, Any]							       =		0

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








'''simple docstring'''


import sys







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if not numbers:
								return 0
				if target == 0 and target in numbers:
								return 0
				if not isinstance(SCREAMING_SNAKE_CASE__							,				(list, tuple)					) or not all(
				    isinstance(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					) for number in numbers					):
								raise ValueError("""numbers must be an iterable of integers"""					)

				_SCREAMING_SNAKE_CASE			:	str							=				0
				_SCREAMING_SNAKE_CASE			:	int							=				sys.maxsize

				while right < len(SCREAMING_SNAKE_CASE__					):
								curr_sum += numbers[right]
								while curr_sum >= target and left <= right:
												_SCREAMING_SNAKE_CASE			:	Any							=				min(SCREAMING_SNAKE_CASE__							,				right - left + 1					)
												curr_sum -= numbers[left]
												left += 1
								right += 1

				return 0 if min_len == sys.maxsize else min_len




