




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

__A    :				Tuple    						=	datasets.load_dataset("""xnli""", split="""test+validation""")

__A    :				Optional[Any]    						=	0
__A    :				Union[str, Any]    						=	0
__A    :				Optional[int]    						=	0
__A    :				Optional[int]    						=	0
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			if spm_diff == list(reversed(SCREAMING_SNAKE_CASE ) ):
						# AAA -> AA+A vs A+AA case.
						return True
			elif len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE ) and fast.decode(SCREAMING_SNAKE_CASE ) == fast.decode(SCREAMING_SNAKE_CASE ):
						# Second order OK
						# Barrich -> Barr + ich vs Bar + rich
						return True
			SCREAMING_SNAKE_CASE     =       slow.encode(slow.decode(SCREAMING_SNAKE_CASE ) )
			SCREAMING_SNAKE_CASE     =       fast.encode(fast.decode(SCREAMING_SNAKE_CASE ) )
			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_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       fast.encode_plus(SCREAMING_SNAKE_CASE )[0]
			SCREAMING_SNAKE_CASE     =       enc.offsets
			SCREAMING_SNAKE_CASE  ,	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_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			# 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(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) ):
						if spm_id != tok_id:
									break
			SCREAMING_SNAKE_CASE     =       i
			for i, (spm_id, tok_id) in enumerate(zip(reversed(SCREAMING_SNAKE_CASE )       ,						reversed(SCREAMING_SNAKE_CASE ) ) ):
						if spm_id != tok_id:
									break
			SCREAMING_SNAKE_CASE     =       len(SCREAMING_SNAKE_CASE ) - i

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

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

			if check_LTR_mark(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
						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(SCREAMING_SNAKE_CASE ) ):
												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]       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) and check_details(
															    SCREAMING_SNAKE_CASE       ,						spm_ids[first + i : last]       ,						tok_ids[first + j : last]       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						):
																		return True

			print(f"""Spm: {[fast.decode([spm_ids[i]] ) for i in range(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )]}""" )
			try:
						print(f"""Tok: {[fast.decode([tok_ids[i]] ) for i in range(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )]}""" )
			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(SCREAMING_SNAKE_CASE )
			return False
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			global perfect
			global imperfect
			global wrong
			global total

			SCREAMING_SNAKE_CASE     =       slow.encode(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       fast.encode(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       False
			total += 1

			if slow_ids != fast_ids:
						if check_details(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
									SCREAMING_SNAKE_CASE     =       True
									imperfect += 1
						else:
									wrong += 1
			else:
						perfect += 1

			if total % 1_00_00 == 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(SCREAMING_SNAKE_CASE )}\n{fast.tokenize(SCREAMING_SNAKE_CASE )}"""
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			global batch_total
			for i in range(len(SCREAMING_SNAKE_CASE ) ):
						# premise, all languages
						for text in dataset[i]["premise"].values():
									test_string(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

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


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

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












import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       ConvBertConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       ConvBertModel(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       load_tf_weights_in_convbert(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       TFConvBertModel.from_pretrained(SCREAMING_SNAKE_CASE       ,						from_pt=SCREAMING_SNAKE_CASE )
			tf_model.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		__A    :				str    						=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--convbert_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained ConvBERT model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__A    :				List[Any]    						=	parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






