
'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)


lowerCamelCase__	=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCamelCase__	=							{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

lowerCamelCase__	=							{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}


@lru_cache()
def 							__A()		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	(
		    list(range(ord("""!""" )						,  ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )						,  ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )						,  ord("""ÿ""" ) + 1 ) )
		)
		_UpperCamelCase						    =	bs[:]
		_UpperCamelCase						    =	0
		for b in range(2**8 ):
				if b not in bs:
						bs.append(__lowerCAmelCase )
						cs.append(2**8 + n )
						n += 1
		_UpperCamelCase						    =	[chr(__lowerCAmelCase ) for n in cs]
		return dict(zip(__lowerCAmelCase						,  __lowerCAmelCase ) )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	set()
		_UpperCamelCase						    =	word[0]
		for char in word[1:]:
				pairs.add((prev_char, char) )
				_UpperCamelCase						    =	char
		return pairs







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: List[str]	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Union[str, Any]	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: int	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Optional[int]	=		["input_ids", "attention_mask"]

						def __init__(			self       ,							a       ,							a       ,							a="replace"       ,							a="<s>"       ,							a="</s>"       ,							a="</s>"       ,							a="<s>"       ,							a="<unk>"       ,							a="<pad>"       ,							a="<mask>"       ,							a=False       ,							**a       ,							)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else bos_token
								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else eos_token
								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else sep_token
								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else cls_token
								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else unk_token
								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								_UpperCamelCase						    =	AddedToken(_lowerCAmelCase       ,							lstrip=_lowerCAmelCase       ,							rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,							_lowerCAmelCase				) else mask_token

								super().__init__(
								    errors=_lowerCAmelCase       ,							bos_token=_lowerCAmelCase       ,							eos_token=_lowerCAmelCase       ,							unk_token=_lowerCAmelCase       ,							sep_token=_lowerCAmelCase       ,							cls_token=_lowerCAmelCase       ,							pad_token=_lowerCAmelCase       ,							mask_token=_lowerCAmelCase       ,							add_prefix_space=_lowerCAmelCase       ,							**_lowerCAmelCase       ,							)

								with open(_lowerCAmelCase       ,							encoding="""utf-8"""				) as vocab_handle:
										_UpperCamelCase						    =	json.load(_lowerCAmelCase				)
								_UpperCamelCase						    =	{v: k for k, v in self.encoder.items()}
								_UpperCamelCase						    =	errors  # how to handle errors in decoding
								_UpperCamelCase						    =	bytes_to_unicode()
								_UpperCamelCase						    =	{v: k for k, v in self.byte_encoder.items()}
								with open(_lowerCAmelCase       ,							encoding="""utf-8"""				) as merges_handle:
										_UpperCamelCase						    =	merges_handle.read().split("""\n"""				)[1:-1]
								_UpperCamelCase						    =	[tuple(merge.split()				) for merge in bpe_merges]
								_UpperCamelCase						    =	dict(zip(_lowerCAmelCase       ,							range(len(_lowerCAmelCase				)				)				)				)
								_UpperCamelCase						    =	{}
								_UpperCamelCase						    =	add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								_UpperCamelCase						    =	re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""				)

						@property
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								return len(self.encoder				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								return dict(self.encoder       ,							**self.added_tokens_encoder				)

						def  A_				(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								if token in self.cache:
										return self.cache[token]
								_UpperCamelCase						    =	tuple(_lowerCAmelCase				)
								_UpperCamelCase						    =	get_pairs(_lowerCAmelCase				)

								if not pairs:
										return token

								while True:
										_UpperCamelCase						    =	min(_lowerCAmelCase       ,							key=lambda a				: self.bpe_ranks.get(_lowerCAmelCase       ,							float("""inf"""				)				)				)
										if bigram not in self.bpe_ranks:
												break
										_UpperCamelCase    ,	_UpperCamelCase						    =	bigram
										_UpperCamelCase						    =	[]
										_UpperCamelCase						    =	0
										while i < len(_lowerCAmelCase				):
												try:
														_UpperCamelCase						    =	word.index(_lowerCAmelCase       ,							_lowerCAmelCase				)
												except ValueError:
														new_word.extend(word[i:]				)
														break
												else:
														new_word.extend(word[i:j]				)
														_UpperCamelCase						    =	j

												if word[i] == first and i < len(_lowerCAmelCase				) - 1 and word[i + 1] == second:
														new_word.append(first + second				)
														i += 2
												else:
														new_word.append(word[i]				)
														i += 1
										_UpperCamelCase						    =	tuple(_lowerCAmelCase				)
										_UpperCamelCase						    =	new_word
										if len(_lowerCAmelCase				) == 1:
												break
										else:
												_UpperCamelCase						    =	get_pairs(_lowerCAmelCase				)
								_UpperCamelCase						    =	""" """.join(_lowerCAmelCase				)
								_UpperCamelCase						    =	word
								return word

						def  A_				(			self       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	[]
								for token in re.findall(self.pat       ,							_lowerCAmelCase				):
										_UpperCamelCase						    =	"""""".join(
										    self.byte_encoder[b] for b in token.encode("""utf-8"""				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase				).split(""" """				)				)
								return bpe_tokens

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								return self.encoder.get(_lowerCAmelCase       ,							self.encoder.get(self.unk_token				)				)

						def  A_				(			self       ,							a				)					->      Tuple:


								'''simple docstring'''

								return self.decoder.get(_lowerCAmelCase				)

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	"""""".join(_lowerCAmelCase				)
								_UpperCamelCase						    =	bytearray([self.byte_decoder[c] for c in text]				).decode("""utf-8"""       ,							errors=self.errors				)
								return text

						def  A_				(			self       ,							a       ,							a = None				)					->      Optional[int]:


								'''simple docstring'''

								if not os.path.isdir(_lowerCAmelCase				):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
										return
								_UpperCamelCase						    =	os.path.join(
								    _lowerCAmelCase       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)
								_UpperCamelCase						    =	os.path.join(
								    _lowerCAmelCase       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]				)

								with open(_lowerCAmelCase       ,							"""w"""       ,							encoding="""utf-8"""				) as f:
										f.write(json.dumps(self.encoder       ,							indent=2       ,							sort_keys=_lowerCAmelCase       ,							ensure_ascii=_lowerCAmelCase				) + """\n"""				)

								_UpperCamelCase						    =	0
								with open(_lowerCAmelCase       ,							"""w"""       ,							encoding="""utf-8"""				) as writer:
										writer.write("""#version: 0.2\n"""				)
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,							key=lambda a				: kv[1]				):
												if index != token_index:
														logger.warning(
														    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
														    """ Please check that the tokenizer is not corrupted!"""				)
														_UpperCamelCase						    =	token_index
												writer.write(""" """.join(_lowerCAmelCase				) + """\n"""				)
												index += 1

								return vocab_file, merge_file

						def  A_				(			self       ,							a       ,							a = None				)					->      List[Any]:


								'''simple docstring'''

								if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								_UpperCamelCase						    =	[self.cls_token_id]
								_UpperCamelCase						    =	[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep

						def  A_				(			self       ,							a       ,							a = None       ,							a = False				)					->      str:


								'''simple docstring'''

								if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_lowerCAmelCase       ,							token_ids_a=_lowerCAmelCase       ,							already_has_special_tokens=_lowerCAmelCase				)

								if token_ids_a is None:
										return [1] + ([0] * len(_lowerCAmelCase				)) + [1]
								return [1] + ([0] * len(_lowerCAmelCase				)) + [1, 1] + ([0] * len(_lowerCAmelCase				)) + [1]

						def  A_				(			self       ,							a       ,							a = None				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.sep_token_id]
								_UpperCamelCase						    =	[self.cls_token_id]

								if token_ids_a is None:
										return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]







						def  A_				(			self       ,							a       ,							a=False       ,							**a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	kwargs.pop("""add_prefix_space"""       ,							self.add_prefix_space				)
								if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase				) > 0 and not text[0].isspace()):
										_UpperCamelCase						    =	""" """ + text
								return (text, kwargs)





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if "model" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""model."""						,  """""" )
		if "norm1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm1"""						,  """attention.output.LayerNorm""" )
		if "norm2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm2"""						,  """output.LayerNorm""" )
		if "norm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm"""						,  """LayerNorm""" )
		if "transformer" in orig_key:
				_UpperCamelCase						    =	orig_key.split(""".""" )[0].split("""_""" )[-1]
				_UpperCamelCase						    =	orig_key.replace(F'transformer_{layer_num}'						,  F'encoder.layer.{layer_num}' )
		if "mha.attn" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha.attn"""						,  """attention.self""" )
		if "mha" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha"""						,  """attention""" )
		if "W_q" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_q"""						,  """self.query""" )
		if "W_k" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_k"""						,  """self.key""" )
		if "W_v" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_v"""						,  """self.value""" )
		if "ff1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff1"""						,  """intermediate.dense""" )
		if "ff2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff2"""						,  """output.dense""" )
		if "ff" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff"""						,  """output.dense""" )
		if "mlm_class" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm.mlm_class"""						,  """cls.predictions.decoder""" )
		if "mlm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm"""						,  """cls.predictions.transform""" )
		if "cls" not in orig_key:
				_UpperCamelCase						    =	"""yoso.""" + orig_key

		return orig_key


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						_UpperCamelCase						    =	val

		_UpperCamelCase						    =	orig_state_dict["""cls.predictions.decoder.bias"""]
		_UpperCamelCase						    =	torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )["""model_state_dict"""]
		_UpperCamelCase						    =	YosoConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	YosoForMaskedLM(lowerCAmelCase )

		_UpperCamelCase						    =	convert_checkpoint_helper(config.max_position_embeddings						,  lowerCAmelCase )

		print(model.load_state_dict(lowerCAmelCase ) )
		model.eval()
		model.save_pretrained(lowerCAmelCase )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



