



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

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

_UpperCamelCase									=      0
_UpperCamelCase									=      0
_UpperCamelCase									=      0
_UpperCamelCase									=      0


def 	lowercase_						( lowerCAmelCase__						:	List[str]      ,				lowerCAmelCase__						:	Tuple      ,				lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	str			):

			"""simple docstring"""





			if spm_diff == list(reversed(a_			)			):
						# AAA -> AA+A vs A+AA case.
						return True
			elif len(a_			) == len(a_			) and fast.decode(a_			) == fast.decode(a_			):
						# Second order OK
						# Barrich -> Barr + ich vs Bar + rich
						return True
			__UpperCAmelCase :   str    					=  slow.encode(slow.decode(a_			)			)
			__UpperCAmelCase :   List[Any]    					=  fast.encode(fast.decode(a_			)			)
			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 	lowercase_						( lowerCAmelCase__						:	Optional[Any]      ,				lowerCAmelCase__						:	Union[str, Any]      ,				lowerCAmelCase__						:	Optional[int]			):

			"""simple docstring"""





			__UpperCAmelCase :   Union[str, Any]    					=  fast.encode_plus(a_			)[0]
			__UpperCAmelCase :   Any    					=  enc.offsets
			__UpperCAmelCase :   Dict    					=  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 	lowercase_						( lowerCAmelCase__						:	List[str]      ,				lowerCAmelCase__						:	Optional[int]      ,				lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	List[str]      ,				lowerCAmelCase__						:	Tuple			):

			"""simple docstring"""





			for i, (spm_id, tok_id) in enumerate(zip(a_      ,				a_			)			):
						if spm_id != tok_id:
									break
			__UpperCAmelCase :   Dict    					=  i
			for i, (spm_id, tok_id) in enumerate(zip(reversed(a_			)      ,				reversed(a_			)			)			):
						if spm_id != tok_id:
									break
			__UpperCAmelCase :   Dict    					=  len(a_			) - i

			__UpperCAmelCase :   List[str]    					=  spm_ids[first:last]
			__UpperCAmelCase :   List[str]    					=  tok_ids[first:last]

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

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

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

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

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

			__UpperCAmelCase :   List[str]    					=  fast.decode(spm_ids[:first]			)
			__UpperCAmelCase :   List[str]    					=  fast.decode(spm_ids[last:]			)
			__UpperCAmelCase :   List[Any]    					=  fast.decode(spm_ids[first:last]			)
			print()
			print(a_			)
			return False


def 	lowercase_						( lowerCAmelCase__						:	Dict      ,				lowerCAmelCase__						:	List[Any]      ,				lowerCAmelCase__						:	Optional[int]			):

			"""simple docstring"""





			global perfect
			global imperfect
			global wrong
			global total

			__UpperCAmelCase :   str    					=  slow.encode(a_			)
			__UpperCAmelCase :   Dict    					=  fast.encode(a_			)

			__UpperCAmelCase :   Any    					=  False
			total += 1

			if slow_ids != fast_ids:
						if check_details(a_      ,				a_      ,				a_      ,				a_      ,				a_			):
									__UpperCAmelCase :   Optional[Any]    					=  True
									imperfect += 1
						else:
									wrong += 1
			else:
						perfect += 1

			if total % 10000 == 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(a_			)}\n{fast.tokenize(a_			)}'


def 	lowercase_						( lowerCAmelCase__						:	int      ,				lowerCAmelCase__						:	Union[str, Any]			):

			"""simple docstring"""





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

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


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

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












from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_			:Union[str, Any]	    =		load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
A_    ,A_    ,A_    ,A_			:Tuple	    =		train_test_split(
    iris['''data'''], iris['''target'''], random_state=4
)

# KNN

A_			:Any	    =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_			:Optional[Any]	    =		[[1, 2, 1, 4], [2, 3, 4, 5]]

A_			:Union[str, Any]	    =		knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)

