






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_			:Union[str, Any]	    =		{
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

A_			:Optional[int]	    =		datasets.load_dataset('''xnli''', split='''test+validation''')

A_			:Any	    =		0
A_			:List[str]	    =		0
A_			:Union[str, Any]	    =		0
A_			:Optional[int]	    =		0






def 				A	(			a_				,a_				,a_				,a_   )	->		Dict:
					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 				A	(			a_				,a_				,a_   )	->		List[str]:
					__UpperCamelCase :   Union[str, Any]									=fast.encode_plus(a_   )[0]
					__UpperCamelCase :   Any									=enc.offsets
					__UpperCamelCase						,							__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 				A	(			a_				,a_				,a_				,a_				,a_   )	->		Optional[int]:
					# 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(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 				A	(			a_				,a_				,a_   )	->		int:
					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 % 10_000 == 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 				A	(			a_				,a_   )	->		Optional[int]:
					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():
														A_			:Optional[int]	    =		list(slow_class.max_model_input_sizes.keys())
														for checkpoint in checkpoint_names:
																					A_			:Optional[int]	    =		0
																					A_			:Optional[int]	    =		0
																					A_			:str	    =		0
																					A_			:Union[str, Any]	    =		0

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_			:Union[str, Any]	    =		{'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_			:Optional[Any]	    =		[
							    '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NatForImageClassification''',
							    '''NatModel''',
							    '''NatPreTrainedModel''',
							    '''NatBackbone''',
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

							A_			:Tuple	    =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

