


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()

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

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

snake_case_  :	Dict							  =							0
snake_case_  :	Union[str, Any]							  =							0
snake_case_  :	Dict							  =							0
snake_case_  :	int							  =							0







def   lowerCamelCase(      a__		,a__		,a__		,a__):
				if spm_diff == list(reversed(UpperCamelCase_)):
								# AAA -> AA+A vs A+AA case.
								return True
				elif len(UpperCamelCase_) == len(UpperCamelCase_) and fast.decode(UpperCamelCase_) == fast.decode(UpperCamelCase_):
								# Second order OK
								# Barrich -> Barr + ich vs Bar + rich
								return True
				_SCREAMING_SNAKE_CASE	     =slow.encode(slow.decode(UpperCamelCase_))
				_SCREAMING_SNAKE_CASE	     =fast.encode(fast.decode(UpperCamelCase_))
				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   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =fast.encode_plus(UpperCamelCase_)[0]
				_SCREAMING_SNAKE_CASE	     =enc.offsets
				_SCREAMING_SNAKE_CASE	     =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   lowerCamelCase(      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(UpperCamelCase_		,UpperCamelCase_)):
								if spm_id != tok_id:
												break
				_SCREAMING_SNAKE_CASE	     =i
				for i, (spm_id, tok_id) in enumerate(zip(reversed(UpperCamelCase_)		,reversed(UpperCamelCase_))):
								if spm_id != tok_id:
												break
				_SCREAMING_SNAKE_CASE	     =len(UpperCamelCase_) - i

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

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

				if check_LTR_mark(UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_):
								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	     =Counter(spm_ids[first:last])
								_SCREAMING_SNAKE_CASE	     =Counter(tok_ids[first:last])

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

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

				_SCREAMING_SNAKE_CASE	     =fast.decode(spm_ids[:first])
				_SCREAMING_SNAKE_CASE	     =fast.decode(spm_ids[last:])
				_SCREAMING_SNAKE_CASE	     =fast.decode(spm_ids[first:last])
				print()
				print(UpperCamelCase_)
				return False







def   lowerCamelCase(      a__		,a__		,a__):
				global perfect
				global imperfect
				global wrong
				global total

				_SCREAMING_SNAKE_CASE	     =slow.encode(UpperCamelCase_)
				_SCREAMING_SNAKE_CASE	     =fast.encode(UpperCamelCase_)

				_SCREAMING_SNAKE_CASE	     =False
				total += 1

				if slow_ids != fast_ids:
								if check_details(UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_):
												_SCREAMING_SNAKE_CASE	     =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(UpperCamelCase_)}\n{fast.tokenize(UpperCamelCase_)}"







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

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


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

																					print(f"""========================== Checking {name}: {checkpoint} ==========================""")
																					snake_case_  :	List[Any]							  =							slow_class.from_pretrained(checkpoint, force_download=True)
																					snake_case_  :	Optional[int]							  =							fast_class.from_pretrained(checkpoint, force_download=True)
																					test_tokenizer(slow, fast)
																					print(f"""Accuracy {perfect * 1_00 / total:.2f}""")





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
	_lowerCamelCase					:     Any					  =  argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	_lowerCamelCase					:     Dict					  =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

