"""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()

lowercase__      :    Optional[Any]          =     {
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

lowercase__      :    List[str]          =     datasets.load_dataset('''xnli''', split='''test+validation''')

lowercase__      :    Union[str, Any]          =     0
lowercase__      :    Dict          =     0
lowercase__      :    str          =     0
lowercase__      :    Tuple          =     0

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     Optional[int]:
							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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Tuple:
							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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__							)					->     List[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(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        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__							)					->     Dict:
							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_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(lowerCamelCase_							)}\n{fast.tokenize(lowerCamelCase_							)}"

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     Dict:
							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():
		lowercase__      :    Optional[Any]          =     list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			lowercase__      :    Dict          =     0
			lowercase__      :    Dict          =     0
			lowercase__      :    List[str]          =     0
			lowercase__      :    List[str]          =     0

			print(f'========================== Checking {name}: {checkpoint} ==========================')
			lowercase__      :    Tuple          =     slow_class.from_pretrained(checkpoint, force_download=True)
			lowercase__      :    Dict          =     fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(f'Accuracy {perfect * 1_0_0 / total:.2f}')




import warnings

from .generation import FlaxGenerationMixin




class        lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	warnings.warn(
	    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
	    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""				,					UpperCamelCase_				,					)



