import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A=True							,				_A="pt"					):



 """simple docstring"""


 __a       =  {"add_prefix_space": True} if isinstance(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					) and not line.startswith(" "					) else {}
 __a       =  padding_side
 return tokenizer(
     [line]							,				max_length=_SCREAMING_SNAKE_CASE							,				padding="max_length" if pad_to_max_length else None							,				truncation=_SCREAMING_SNAKE_CASE							,				return_tensors=_SCREAMING_SNAKE_CASE							,				add_special_tokens=_SCREAMING_SNAKE_CASE							,				**_SCREAMING_SNAKE_CASE							,				)



def   __A		(			_A							,				_A							,				_A=None							,				):



 """simple docstring"""


 __a       =  input_ids.ne(_SCREAMING_SNAKE_CASE					).any(dim=0					)
 if attention_mask is None:
  return input_ids[:, keep_column_mask]
 else:
  return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class    A_	(       snake_case__ ):

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      int="train"     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      int=None     ,							__SCREAMING_SNAKE_CASE :      List[Any]=None     ,							__SCREAMING_SNAKE_CASE :      str=""     ,							):
   super().__init__()
   __a       =  Path(UpperCAmelCase_       ).joinpath(type_path + ".source"       )
   __a       =  Path(UpperCAmelCase_       ).joinpath(type_path + ".target"       )
   __a       =  self.get_char_lens(self.src_file       )
   __a       =  max_source_length
   __a       =  max_target_length
   assert min(self.src_lens       ) > 0, f"""found empty line in {self.src_file}"""
   __a       =  tokenizer
   __a       =  prefix
   if n_obs is not None:
    __a       =  self.src_lens[:n_obs]
   __a       =  src_lang
   __a       =  tgt_lang

  def __len__(		self :      Any       ):
   return len(self.src_lens       )

  def __getitem__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  index + 1  # linecache starts at 1
   __a       =  self.prefix + linecache.getline(str(self.src_file       )     ,							UpperCAmelCase_       ).rstrip("\n"       )
   __a       =  linecache.getline(str(self.tgt_file       )     ,							UpperCAmelCase_       ).rstrip("\n"       )
   assert source_line, f"""empty source line for index {index}"""
   assert tgt_line, f"""empty tgt line for index {index}"""

   # Need to add eos token manually for T5
   if isinstance(self.tokenizer     ,							UpperCAmelCase_       ):
    source_line += self.tokenizer.eos_token
    tgt_line += self.tokenizer.eos_token

   # Pad source and target to the right
   __a       =  (
       self.tokenizer.question_encoder if isinstance(self.tokenizer     ,							UpperCAmelCase_       ) else self.tokenizer
   )
   __a       =  self.tokenizer.generator if isinstance(self.tokenizer     ,							UpperCAmelCase_       ) else self.tokenizer

   __a       =  encode_line(UpperCAmelCase_     ,							UpperCAmelCase_     ,							self.max_source_length     ,							"right"       )
   __a       =  encode_line(UpperCAmelCase_     ,							UpperCAmelCase_     ,							self.max_target_length     ,							"right"       )

   __a       =  source_inputs["input_ids"].squeeze()
   __a       =  target_inputs["input_ids"].squeeze()
   __a       =  source_inputs["attention_mask"].squeeze()
   return {
       "input_ids": source_ids,
       "attention_mask": src_mask,
       "decoder_input_ids": target_ids,
   }

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   return [len(UpperCAmelCase_       ) for x in Path(UpperCAmelCase_       ).open().readlines()]

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  torch.stack([x["input_ids"] for x in batch]       )
   __a       =  torch.stack([x["attention_mask"] for x in batch]       )
   __a       =  torch.stack([x["decoder_input_ids"] for x in batch]       )
   __a       =  (
       self.tokenizer.generator.pad_token_id
       if isinstance(self.tokenizer     ,							UpperCAmelCase_       )
       else self.tokenizer.pad_token_id
   )
   __a       =  (
       self.tokenizer.question_encoder.pad_token_id
       if isinstance(self.tokenizer     ,							UpperCAmelCase_       )
       else self.tokenizer.pad_token_id
   )
   __a       =  trim_batch(UpperCAmelCase_     ,							UpperCAmelCase_       )
   __a       ,     __a       =  trim_batch(UpperCAmelCase_     ,							UpperCAmelCase_     ,							attention_mask=UpperCAmelCase_       )
   __a       =  {
       "input_ids": source_ids,
       "attention_mask": source_mask,
       "decoder_input_ids": y,
   }
   return batch
SCREAMING_SNAKE_CASE :	List[Any]							     =		getLogger(__name__)



def   __A		(			_A					):



 """simple docstring"""


 return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE					)					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  get_git_info()
 save_json(_SCREAMING_SNAKE_CASE							,				os.path.join(_SCREAMING_SNAKE_CASE							,				"git_log.json"					)					)



def   __A		(			_A							,				_A							,				_A=4							,				**_A					):



 """simple docstring"""


 with open(_SCREAMING_SNAKE_CASE							,				"w"					) as f:
  json.dump(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE							,				indent=_SCREAMING_SNAKE_CASE							,				**_SCREAMING_SNAKE_CASE					)



def   __A		(			_A					):



 """simple docstring"""


 with open(_SCREAMING_SNAKE_CASE					) as f:
  return json.load(_SCREAMING_SNAKE_CASE					)



def   __A		(			):



 """simple docstring"""


 __a       =  git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE					)
 __a       =  {
     "repo_id": str(_SCREAMING_SNAKE_CASE					),
     "repo_sha": str(repo.head.object.hexsha					),
     "repo_branch": str(repo.active_branch					),
     "hostname": str(socket.gethostname()					),
 }
 return repo_infos



def   __A		(			_A							,				_A					):



 """simple docstring"""


 return list(map(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 with open(_SCREAMING_SNAKE_CASE							,				"wb"					) as f:
  return pickle.dump(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)



def   __A		(			_A					):



 """simple docstring"""


 def remove_articles(_A					):
  return re.sub(r"\b(a|an|the)\b"							,				" "							,				_SCREAMING_SNAKE_CASE					)

 def white_space_fix(_A					):
  return " ".join(text.split()					)

 def remove_punc(_A					):
  __a       =  set(string.punctuation					)
  return "".join(ch for ch in text if ch not in exclude					)

 def lower(_A					):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE					)					)					)					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  normalize_answer(_SCREAMING_SNAKE_CASE					).split()
 __a       =  normalize_answer(_SCREAMING_SNAKE_CASE					).split()
 __a       =  Counter(_SCREAMING_SNAKE_CASE					) & Counter(_SCREAMING_SNAKE_CASE					)
 __a       =  sum(common.values()					)
 if num_same == 0:
  return 0
 __a       =  1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
 __a       =  1.0 * num_same / len(_SCREAMING_SNAKE_CASE					)
 __a       =  (2 * precision * recall) / (precision + recall)
 return fa



def   __A		(			_A							,				_A					):



 """simple docstring"""


 return normalize_answer(_SCREAMING_SNAKE_CASE					) == normalize_answer(_SCREAMING_SNAKE_CASE					)



def   __A		(			_A							,				_A					):



 """simple docstring"""


 assert len(_SCREAMING_SNAKE_CASE					) == len(_SCREAMING_SNAKE_CASE					)
 __a       =  0
 for hypo, pred in zip(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					):
  em += exact_match_score(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)
 if len(_SCREAMING_SNAKE_CASE					) > 0:
  em /= len(_SCREAMING_SNAKE_CASE					)
 return {"em": em}



def   __A		(			_A					):



 """simple docstring"""


 return model_prefix.startswith("rag"					)






def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 __a       =  "dropout_rate"
 for p in extra_params:
  if getattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					):
   if not hasattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					) and not hasattr(_SCREAMING_SNAKE_CASE							,				equivalent_param[p]					):
    logger.info("config doesn't have a `{}` attribute".format(_SCREAMING_SNAKE_CASE					)					)
    delattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)
    continue
   __a       =  p if hasattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					) else equivalent_param[p]
   setattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE							,				getattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)					)
   delattr(_SCREAMING_SNAKE_CASE							,				_SCREAMING_SNAKE_CASE					)
 return hparams, config



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


SCREAMING_SNAKE_CASE :	str							     =		{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

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



def   __A		(			_A					):



 """simple docstring"""


 __a       =  list(state_dict.keys()					)
 for name in state_dict_keys:
  __a       =  state_dict.pop(_A					)
  # emb -> embedding
  if name.startswith("emb."					):
   __a       =  name.replace("emb."							,				"embeddings."					)
  # ln_0 -> pre_ln (only present at block 0)
  if name.startswith("blocks.0.ln0"					):
   __a       =  name.replace("blocks.0.ln0"							,				"blocks.0.pre_ln"					)
  # att -> attention
  __a       =  re.sub(r"blocks\.(\d+)\.att"							,				r"blocks.\1.attention"							,				_A					)
  # ffn -> feed_forward
  __a       =  re.sub(r"blocks\.(\d+)\.ffn"							,				r"blocks.\1.feed_forward"							,				_A					)
  # time_mix_k -> time_mix_key and reshape
  if name.endswith(".time_mix_k"					):
   __a       =  name.replace(".time_mix_k"							,				".time_mix_key"					)
  # time_mix_v -> time_mix_value and reshape
  if name.endswith(".time_mix_v"					):
   __a       =  name.replace(".time_mix_v"							,				".time_mix_value"					)
  # time_mix_r -> time_mix_key and reshape
  if name.endswith(".time_mix_r"					):
   __a       =  name.replace(".time_mix_r"							,				".time_mix_receptance"					)

  if name != "head.weight":
   __a       =  "rwkv." + name

  __a       =  weight
 return state_dict



def   __A		(			_A							,				_A							,				_A							,				_A=None							,				_A=None							,				_A=False							,				_A=None					):



 """simple docstring"""


 if tokenizer_file is None:
  print("No `--tokenizer_file` provided, we will use the default tokenizer."					)
  __a       =  5_0277
  __a       =  AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"					)
 else:
  __a       =  PreTrainedTokenizerFast(tokenizer_file=_A					)
  __a       =  len(_A					)
 tokenizer.save_pretrained(_A					)

 # 2. Build the config
 __a       =  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       =  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       =  RwkvConfig(
     vocab_size=_A							,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]							,				hidden_size=HIDEN_SIZE_MAPPING[size]							,				)
 config.save_pretrained(_A					)

 # 3. Download model file then convert state_dict
 __a       =  hf_hub_download(_A							,				_A					)
 __a       =  torch.load(_A							,				map_location="cpu"					)
 __a       =  convert_state_dict(_A					)

 # 4. Split in shards and save
 __a       ,     __a       =  shard_checkpoint(_A					)
 for shard_file, shard in shards.items():
  torch.save(_A							,				os.path.join(_A							,				_A					)					)

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

  # 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(shards.keys()					)

  del state_dict
  del shards
  gc.collect()

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

 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       =  AutoModelForCausalLM.from_pretrained(_A					)
  model.push_to_hub(_A							,				max_shard_size="2GB"					)
  tokenizer.push_to_hub(_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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.""",
  )

  SCREAMING_SNAKE_CASE :	Tuple							     =		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,
  )


