
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							"▁"

lowerCamelCase__	=							{"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase__	=							{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase__	=							{
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
lowerCamelCase__	=							["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]







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

						UpperCamelCase_							: List[int]	=		[]
						UpperCamelCase_							: List[int]	=		[]

						def __init__(			self       ,							a       ,							a=None       ,							a=None       ,							a="</s>"       ,							a="</s>"       ,							a="<s>"       ,							a="<unk>"       ,							a="<pad>"       ,							a="<mask>"       ,							a = None       ,							**a       ,							)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	AddedToken(a       ,							lstrip=a       ,							rstrip=a				) if isinstance(a       ,							a				) else mask_token

								_UpperCamelCase						    =	{} if sp_model_kwargs is None else sp_model_kwargs

								_UpperCamelCase						    =	kwargs.get("""additional_special_tokens"""       ,							[]				)
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    src_lang=a       ,							tgt_lang=a       ,							eos_token=a       ,							unk_token=a       ,							sep_token=a       ,							cls_token=a       ,							pad_token=a       ,							mask_token=a       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**a       ,							)

								_UpperCamelCase						    =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(str(a				)				)
								_UpperCamelCase						    =	vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								_UpperCamelCase						    =	{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								_UpperCamelCase						    =	1

								_UpperCamelCase						    =	len(self.sp_model				)
								_UpperCamelCase						    =	{
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a				)
								}
								_UpperCamelCase						    =	{v: k for k, v in self.lang_code_to_id.items()}
								_UpperCamelCase						    =	len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id				)
								_UpperCamelCase						    =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}

								_UpperCamelCase						    =	src_lang if src_lang is not None else """en_XX"""
								_UpperCamelCase						    =	self.lang_code_to_id[self._src_lang]
								_UpperCamelCase						    =	tgt_lang
								self.set_src_lang_special_tokens(self._src_lang				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return len(self.sp_model				) + len(self.lang_code_to_id				) + self.fairseq_offset + 1  # Plus 1 for the mask token

						@property
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								return self._src_lang

						@src_lang.setter
						def  A_				(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	new_src_lang
								self.set_src_lang_special_tokens(self._src_lang				)

						def __getstate__(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.__dict__.copy()
								_UpperCamelCase						    =	None
								return state

						def __setstate__(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	d

								# for backward compatibility
								if not hasattr(self       ,							"""sp_model_kwargs"""				):
										_UpperCamelCase						    =	{}

								_UpperCamelCase						    =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(self.vocab_file				)

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	{self.convert_ids_to_tokens(a				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab

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


								'''simple docstring'''

								return self.sp_model.encode(a       ,							out_type=a				)

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
								_UpperCamelCase						    =	self.sp_model.PieceToId(a				)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset				)

						def  A_				(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	""""""
								_UpperCamelCase						    =	False
								for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
												if not prev_is_special:
														out_string += " "
												out_string += self.sp_model.decode(a				) + token
												_UpperCamelCase						    =	True
												_UpperCamelCase						    =	[]
										else:
												current_sub_tokens.append(a				)
												_UpperCamelCase						    =	False
								out_string += self.sp_model.decode(a				)
								return out_string.strip()

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


								'''simple docstring'''

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

								if os.path.abspath(self.vocab_file				) != os.path.abspath(a				) and os.path.isfile(self.vocab_file				):
										copyfile(self.vocab_file       ,							a				)
								elif not os.path.isfile(self.vocab_file				):
										with open(a       ,							"""wb"""				) as fi:
												_UpperCamelCase						    =	self.sp_model.serialized_model_proto()
												fi.write(a				)

								return (out_vocab_file,)

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


								'''simple docstring'''

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

								_UpperCamelCase						    =	[1] * len(self.prefix_tokens				)
								_UpperCamelCase						    =	[1] * len(self.suffix_tokens				)
								if token_ids_a is None:
										return prefix_ones + ([0] * len(a				)) + suffix_ones
								return prefix_ones + ([0] * len(a				)) + ([0] * len(a				)) + suffix_ones

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


								'''simple docstring'''

								if token_ids_a is None:
										return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

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


								'''simple docstring'''

								if src_lang is None or tgt_lang is None:
										raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
								_UpperCamelCase						    =	src_lang
								_UpperCamelCase						    =	self(a       ,							add_special_tokens=a       ,							return_tensors=a       ,							**a				)
								_UpperCamelCase						    =	self.convert_tokens_to_ids(a				)
								_UpperCamelCase						    =	tgt_lang_id
								return inputs

						def  A_				(			self       ,							a       ,							a = "en_XX"       ,							a = None       ,							a = "ro_RO"       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	src_lang
								_UpperCamelCase						    =	tgt_lang
								return super().prepare_seqaseq_batch(a       ,							a       ,							**a				)

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								return self.set_src_lang_special_tokens(self.src_lang				)

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return self.set_tgt_lang_special_tokens(self.tgt_lang				)

						def  A_				(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	self.lang_code_to_id[src_lang]
								_UpperCamelCase						    =	[self.cur_lang_code_id]
								_UpperCamelCase						    =	[self.eos_token_id]







						def  A_				(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	self.lang_code_to_id[tgt_lang]
								_UpperCamelCase						    =	[self.cur_lang_code_id]
								_UpperCamelCase						    =	[self.eos_token_id]





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)



