



'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   '''▁'''

__lowerCAmelCase									=   {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__lowerCAmelCase									=   {
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

__lowerCAmelCase									=   {
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
__lowerCAmelCase									=   {
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    List[Any]   				=					VOCAB_FILES_NAMES
							lowerCAmelCase       :    List[str]   				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase       :    List[str]   				=					PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase       :    Union[str, Any]   				=					['input_ids', 'attention_mask']

							lowerCAmelCase       :    List[int]   				=					[]
							lowerCAmelCase       :    List[int]   				=					[]







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Union[str, Any]=None	,_UpperCAmelCase					:       Optional[Any]=None	,_UpperCAmelCase					:       Union[str, Any]="<s>"	,_UpperCAmelCase					:       Union[str, Any]="</s>"	,_UpperCAmelCase					:       int="</s>"	,_UpperCAmelCase					:       Tuple="<pad>"	,_UpperCAmelCase					:       str="<unk>"	,_UpperCAmelCase					:       Tuple="m2m100"	,_UpperCAmelCase					:       Optional[Dict[str, Any]] = None	,_UpperCAmelCase					:       List[Any]=8	,**_UpperCAmelCase					:       Union[str, Any]	,):
									_a     :      int        =							{} if sp_model_kwargs is None else sp_model_kwargs

									_a     :      int        =							language_codes
									_a     :      List[Any]        =							FAIRSEQ_LANGUAGE_CODES[language_codes]
									_a     :      Union[str, Any]        =							{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

									_a     :      List[str]        =							kwargs.get('additional_special_tokens'	,[]		)
									kwargs["additional_special_tokens"] += [
									    self.get_lang_token(_UpperCAmelCase		)
									    for lang_code in fairseq_language_code
									    if self.get_lang_token(_UpperCAmelCase		) not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    src_lang=_UpperCAmelCase	,tgt_lang=_UpperCAmelCase	,bos_token=_UpperCAmelCase	,eos_token=_UpperCAmelCase	,sep_token=_UpperCAmelCase	,unk_token=_UpperCAmelCase	,pad_token=_UpperCAmelCase	,language_codes=_UpperCAmelCase	,sp_model_kwargs=self.sp_model_kwargs	,num_madeup_words=_UpperCAmelCase	,**_UpperCAmelCase	,)

									_a     :      Any        =							vocab_file
									_a     :      int        =							load_json(_UpperCAmelCase		)
									_a     :      Dict        =							{v: k for k, v in self.encoder.items()}
									_a     :      int        =							spm_file
									_a     :      int        =							load_spm(_UpperCAmelCase	,self.sp_model_kwargs		)

									_a     :      Any        =							len(self.encoder		)

									_a     :      str        =							{
									    self.get_lang_token(_UpperCAmelCase		): self.encoder_size + i for i, lang_code in enumerate(_UpperCAmelCase		)
									}
									_a     :      Optional[int]        =							{lang_code: self.encoder_size + i for i, lang_code in enumerate(_UpperCAmelCase		)}
									_a     :      Dict        =							{v: k for k, v in self.lang_token_to_id.items()}

									_a     :      Union[str, Any]        =							src_lang if src_lang is not None else 'en'
									_a     :      Optional[int]        =							tgt_lang
									_a     :      Optional[Any]        =							self.get_lang_id(self._src_lang		)
									self.set_src_lang_special_tokens(self._src_lang		)

									_a     :      List[Any]        =							num_madeup_words







							@property
							def 							__lowercase			(							self					:       int		):
									return len(self.encoder		) + len(self.lang_token_to_id		)







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									return self._src_lang







							@src_lang.setter
							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       str		):
									_a     :      Dict        =							new_src_lang
									self.set_src_lang_special_tokens(self._src_lang		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       str		):
									return self.sp_model.encode(_UpperCAmelCase	,out_type=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       int		):
									if token in self.lang_token_to_id:
											return self.lang_token_to_id[token]
									return self.encoder.get(_UpperCAmelCase	,self.encoder[self.unk_token]		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       int		):
									if index in self.id_to_lang_token:
											return self.id_to_lang_token[index]
									return self.decoder.get(_UpperCAmelCase	,self.unk_token		)







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Dict		):
									_a     :      Dict        =							[]
									_a     :      List[str]        =							''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(_UpperCAmelCase		) + token
													_a     :      Optional[Any]        =							[]
											else:
													current_sub_tokens.append(_UpperCAmelCase		)
									out_string += self.sp_model.decode(_UpperCAmelCase		)
									return out_string.strip()







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None	,_UpperCAmelCase					:       bool = False		):

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

									_a     :      List[str]        =							[1] * len(self.prefix_tokens		)
									_a     :      Any        =							[1] * len(self.suffix_tokens		)
									if token_ids_a is None:
											return prefix_ones + ([0] * len(_UpperCAmelCase		)) + suffix_ones
									return prefix_ones + ([0] * len(_UpperCAmelCase		)) + ([0] * len(_UpperCAmelCase		)) + suffix_ones







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									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 							__lowercase			(							self					:       List[Any]		):
									_a     :      Dict        =							{self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab







							def __getstate__(							self					:       List[str]		):
									_a     :      Dict        =							self.__dict__.copy()
									_a     :      Any        =							None
									return state







							def __setstate__(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Dict		):
									_a     :      List[Any]        =							d

									# for backward compatibility
									if not hasattr(self	,'sp_model_kwargs'		):
											_a     :      Union[str, Any]        =							{}

									_a     :      Any        =							load_spm(self.spm_file	,self.sp_model_kwargs		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[str] = None		):
									_a     :      List[str]        =							Path(_UpperCAmelCase		)
									if not save_dir.is_dir():
											raise OSError(F"""{save_directory} should be a directory"""		)
									_a     :      int        =							save_dir / (
									    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
									)
									_a     :      Tuple        =							save_dir / (
									    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
									)

									save_json(self.encoder	,_UpperCAmelCase		)

									if os.path.abspath(self.spm_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.spm_file		):
											copyfile(self.spm_file	,_UpperCAmelCase		)
									elif not os.path.isfile(self.spm_file		):
											with open(_UpperCAmelCase	,'wb'		) as fi:
													_a     :      Tuple        =							self.sp_model.serialized_model_proto()
													fi.write(_UpperCAmelCase		)

									return (str(_UpperCAmelCase		), str(_UpperCAmelCase		))







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       str = "en"	,_UpperCAmelCase					:       Optional[List[str]] = None	,_UpperCAmelCase					:       str = "ro"	,**_UpperCAmelCase					:       List[Any]	,):
									_a     :      int        =							src_lang
									_a     :      List[str]        =							tgt_lang
									self.set_src_lang_special_tokens(self.src_lang		)
									return super().prepare_seqaseq_batch(_UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Optional[str]	,_UpperCAmelCase					:       Optional[str]	,**_UpperCAmelCase					:       List[Any]		):
									if src_lang is None or tgt_lang is None:
											raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
									_a     :      List[str]        =							src_lang
									_a     :      Optional[int]        =							self(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[str]        =							self.get_lang_id(_UpperCAmelCase		)
									_a     :      List[str]        =							tgt_lang_id
									return inputs







							def 							__lowercase			(							self					:       List[str]		):
									self.set_src_lang_special_tokens(self.src_lang		)







							def 							__lowercase			(							self					:       Any		):
									self.set_tgt_lang_special_tokens(self.tgt_lang		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       str		):
									_a     :      Tuple        =							self.get_lang_token(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.lang_token_to_id[lang_token]
									_a     :      Union[str, Any]        =							[self.cur_lang_id]
									_a     :      Union[str, Any]        =							[self.eos_token_id]







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       str		):
									_a     :      int        =							self.get_lang_token(_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.lang_token_to_id[lang_token]
									_a     :      Dict        =							[self.cur_lang_id]
									_a     :      Optional[int]        =							[self.eos_token_id]







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       str		):
									return self.lang_code_to_token[lang]







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       str		):
									_a     :      Optional[int]        =							self.get_lang_token(_UpperCAmelCase		)
									return self.lang_token_to_id[lang_token]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      sentencepiece.SentencePieceProcessor:
		_a     :      int        =							sentencepiece.SentencePieceProcessor(**lowerCAmelCase_  )
		spm.Load(str(lowerCAmelCase_  )  )
		return spm



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Union[Dict, List]:
		with open(lowerCAmelCase_						,			'r'  ) as f:
				return json.load(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      None:
		with open(lowerCAmelCase_						,			'w'  ) as f:
				json.dump(lowerCAmelCase_						,			lowerCAmelCase_						,			indent=2  )












"""simple docstring"""




import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_snake_case          =   {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

_snake_case          =   {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	list(state_dict.keys()				)
  for name in state_dict_keys:
    _a  :       str             =	state_dict.pop(UpperCamelCase__				)
    # emb -> embedding
    if name.startswith("""emb."""				):
      _a  :       Dict             =	name.replace("""emb."""     ,   """embeddings."""				)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith("""blocks.0.ln0"""				):
      _a  :       Dict             =	name.replace("""blocks.0.ln0"""     ,   """blocks.0.pre_ln"""				)
    # att -> attention
    _a  :       Any             =	re.sub(R"""blocks\.(\d+)\.att"""     ,   R"""blocks.\1.attention"""     ,   UpperCamelCase__				)
    # ffn -> feed_forward
    _a  :       int             =	re.sub(R"""blocks\.(\d+)\.ffn"""     ,   R"""blocks.\1.feed_forward"""     ,   UpperCamelCase__				)
    # time_mix_k -> time_mix_key and reshape
    if name.endswith(""".time_mix_k"""				):
      _a  :       List[str]             =	name.replace(""".time_mix_k"""     ,   """.time_mix_key"""				)
    # time_mix_v -> time_mix_value and reshape
    if name.endswith(""".time_mix_v"""				):
      _a  :       Tuple             =	name.replace(""".time_mix_v"""     ,   """.time_mix_value"""				)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(""".time_mix_r"""				):
      _a  :       Dict             =	name.replace(""".time_mix_r"""     ,   """.time_mix_receptance"""				)

    if name != "head.weight":
      _a  :       Optional[int]             =	"""rwkv.""" + name

    _a  :       Any             =	weight
  return state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=False     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
    _a  :       Tuple             =	5_0_2_7_7
    _a  :       str             =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
  else:
    _a  :       int             =	PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__				)
    _a  :       int             =	len(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  # 2. Build the config
  _a  :       Optional[Any]             =	list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
  if size is None:
    # Try to infer size from the checkpoint name
    for candidate in possible_sizes:
      if candidate in checkpoint_file:
        _a  :       Tuple             =	candidate
        break
    if size is None:
      raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""				)
  if size not in possible_sizes:
    raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""				)

  _a  :       List[Any]             =	RwkvConfig(
      vocab_size=UpperCamelCase__     ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,   hidden_size=HIDEN_SIZE_MAPPING[size]     ,   )
  config.save_pretrained(UpperCamelCase__				)

  # 3. Download model file then convert state_dict
  _a  :       str             =	hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       int             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[str]             =	convert_state_dict(UpperCamelCase__				)

  # 4. Split in shards and save
  _a      ,  _a  :       List[str]             =	shard_checkpoint(UpperCamelCase__				)
  for shard_file, shard in shards.items():
    torch.save(UpperCamelCase__     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  if index is not None:
    _a  :       Optional[Any]             =	os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)
    # Save the index as well
    with open(UpperCamelCase__     ,   """w"""     ,   encoding="""utf-8"""				) as f:
      _a  :       Dict             =	json.dumps(UpperCamelCase__     ,   indent=2     ,   sort_keys=UpperCamelCase__				) + """\n"""
      f.write(UpperCamelCase__				)

    # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
    print(
        """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""				)
    _a  :       List[Any]             =	list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      _a  :       Any             =	torch.load(os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  del state_dict
  gc.collect()

  if push_to_hub:
    if model_name is None:
      raise ValueError("""Please provide a `model_name` to push the model to the Hub."""				)
    _a  :       Dict             =	AutoModelForCausalLM.from_pretrained(UpperCamelCase__				)
    model.push_to_hub(UpperCamelCase__     ,   max_shard_size="""2GB"""				)
    tokenizer.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
 )
 parser.add_argument(
     '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
 )
 parser.add_argument(
     '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
 )
 parser.add_argument(
     '--tokenizer_file',
     default=None,
     type=str,
     help='Path to the tokenizer file to use (if not provided, only the model is converted).',
 )
 parser.add_argument(
     '--size',
     default=None,
     type=str,
     help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Push to the Hub the converted model.',
 )
 parser.add_argument(
     '--model_name',
     default=None,
     type=str,
     help='Name of the pushed model on the Hub, including the username / organization.',
 )

 _snake_case          =   parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )

