






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

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

a    =       0
a    =       0
a    =       0
a    =       0
def       a__						(  A		,				A		,				A		,				A					):
			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
			lowercase__						:	Optional[Any]         = slow.encode(slow.decode(A					)					)
			lowercase__						:	Optional[int]         = 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					):
			lowercase__						:	List[Any]         = fast.encode_plus(A					)[0]
			lowercase__						:	str         = enc.offsets
			lowercase__						:	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       a__						(  A		,				A		,				A		,				A		,				A					):
			# 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
			lowercase__						:	Union[str, Any]         = i
			for i, (spm_id, tok_id) in enumerate(zip(reversed(A					)		,				reversed(A					)					)					):
						if spm_id != tok_id:
									break
			lowercase__						:	List[Any]         = len(A					) - i

			lowercase__						:	List[str]         = spm_ids[first:last]
			lowercase__						:	Union[str, Any]         = 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
						lowercase__						:	List[str]         = Counter(spm_ids[first:last]					)
						lowercase__						:	str         = Counter(tok_ids[first:last]					)

						lowercase__						:	Dict         = {spm_ for (spm_, si) in spms.items() if toks.get(spm_		,				0					) == si}
						lowercase__						:	List[Any]         = 3
						for i in range(last - first - min_width					):
									if all(spm_ids[first + i + j] in removable_tokens for j in range(A					)					):
												lowercase__						:	int         = [
												    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

			lowercase__						:	Optional[int]         = fast.decode(spm_ids[:first]					)
			lowercase__						:	str         = fast.decode(spm_ids[last:]					)
			lowercase__						:	Optional[int]         = fast.decode(spm_ids[first:last]					)
			print()
			print(A					)
			return False
def       a__						(  A		,				A		,				A					):
			global perfect
			global imperfect
			global wrong
			global total

			lowercase__						:	Any         = slow.encode(A					)
			lowercase__						:	int         = fast.encode(A					)

			lowercase__						:	Tuple         = False
			total += 1

			if slow_ids != fast_ids:
						if check_details(A		,				A		,				A		,				A		,				A					):
									lowercase__						:	int         = 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					):
			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    =       list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a    =       0
			a    =       0
			a    =       0
			a    =       0

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










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a    =       logging.get_logger(__name__)

a    =       {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}
class        __snake_case(					__UpperCamelCase      ):
 lowercase :       Dict		      =		'''ernie'''



 def __init__(	self   :   Tuple ,		UpperCAmelCase__   :   int=30_522 ,		UpperCAmelCase__   :   List[str]=768 ,		UpperCAmelCase__   :   Tuple=12 ,		UpperCAmelCase__   :   Optional[int]=12 ,		UpperCAmelCase__   :   Any=3_072 ,		UpperCAmelCase__   :   Tuple="gelu" ,		UpperCAmelCase__   :   Union[str, Any]=0.1 ,		UpperCAmelCase__   :   str=0.1 ,		UpperCAmelCase__   :   List[str]=512 ,		UpperCAmelCase__   :   str=2 ,		UpperCAmelCase__   :   Optional[int]=3 ,		UpperCAmelCase__   :   Dict=False ,		UpperCAmelCase__   :   Any=0.02 ,		UpperCAmelCase__   :   Any=1e-12 ,		UpperCAmelCase__   :   str=0 ,		UpperCAmelCase__   :   Any="absolute" ,		UpperCAmelCase__   :   List[str]=True ,		UpperCAmelCase__   :   Tuple=None ,		**UpperCAmelCase__   :   Dict ,		):

    """simple docstring"""






    super().__init__(pad_token_id=UpperCAmelCase__ ,		**UpperCAmelCase__	)

    lowercase__						:	List[str]         = vocab_size
    lowercase__						:	Optional[int]         = hidden_size
    lowercase__						:	Union[str, Any]         = num_hidden_layers
    lowercase__						:	Any         = num_attention_heads
    lowercase__						:	Optional[int]         = hidden_act
    lowercase__						:	str         = intermediate_size
    lowercase__						:	Tuple         = hidden_dropout_prob
    lowercase__						:	Tuple         = attention_probs_dropout_prob
    lowercase__						:	str         = max_position_embeddings
    lowercase__						:	List[Any]         = type_vocab_size
    lowercase__						:	Any         = task_type_vocab_size
    lowercase__						:	Optional[Any]         = use_task_id
    lowercase__						:	Optional[Any]         = initializer_range
    lowercase__						:	Union[str, Any]         = layer_norm_eps
    lowercase__						:	Dict         = position_embedding_type
    lowercase__						:	Union[str, Any]         = use_cache
    lowercase__						:	int         = classifier_dropout
class        __snake_case(					__UpperCamelCase      ):



 @property
 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    if self.task == "multiple-choice":
       lowercase__						:	Dict         = {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
       lowercase__						:	Dict         = {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
            ("""token_type_ids""", dynamic_axis),
            ("""task_type_ids""", dynamic_axis),
        ]	)


