




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

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

            _A							:     Dict			=						weight
      return state_dict







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_=None,snake_case_=None,snake_case_=False,snake_case_=None     ):
      # 1. If possible, build the tokenizer.
      if tokenizer_file is None:
            print("""No `--tokenizer_file` provided, we will use the default tokenizer."""     )
            _A							:     Dict			=						50277
            _A							:     str			=						AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""     )
      else:
            _A							:     int			=						PreTrainedTokenizerFast(tokenizer_file=_a     )
            _A							:     int			=						len(_a     )
      tokenizer.save_pretrained(_a     )

      # 2. Build the config
      _A							:     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							:     Dict			=						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							:     Optional[int]			=						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							:     Any			=						hf_hub_download(_a,_a     )
      _A							:     Tuple			=						torch.load(_a,map_location="""cpu"""     )
      _A							:     Tuple			=						convert_state_dict(_a     )

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

      if index is not None:
            _A							:     Tuple			=						os.path.join(_a,_a     )
            # Save the index as well
            with open(_a,"""w""",encoding="""utf-8"""     ) as f:
                  _A							:     Dict			=						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							:     Any			=						list(shards.keys()     )

            del state_dict
            del shards
            gc.collect()

            for shard_file in shard_files:
                  _A							:     List[Any]			=						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							:     List[Any]			=						AutoModelForCausalLM.from_pretrained(_a     )
            model.push_to_hub(_a,max_shard_size="""2GB"""     )
            tokenizer.push_to_hub(_a     )


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,
  )







'''simple docstring'''







import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A     =logging.getLogger(__name__)


A     ='Hello world! cécé herlolip'


A     =namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)





def 						snake_case_							(_a	:							List[Any]    ,   _a	:							Any     ):
    UpperCAmelCase			       =		BertAbsConfig(
        temp_dir='''.'''    ,   finetune_bert=_a    ,   large=_a    ,   share_emb=_a    ,   use_bert_emb=_a    ,   encoder='''bert'''    ,   max_pos=5_1_2    ,   enc_layers=6    ,   enc_hidden_size=5_1_2    ,   enc_heads=8    ,   enc_ff_size=5_1_2    ,   enc_dropout=0.2    ,   dec_layers=6    ,   dec_hidden_size=7_6_8    ,   dec_heads=8    ,   dec_ff_size=2_0_4_8    ,   dec_dropout=0.2    ,   )
    UpperCAmelCase			       =		torch.load(_a    ,   lambda _a    ,   _a     : storage     )
    UpperCAmelCase			       =		AbsSummarizer(_a    ,   torch.device('''cpu'''     )    ,   _a     )
    original.eval()

    UpperCAmelCase			       =		BertAbsSummarizer(_a    ,   torch.device('''cpu'''     )     )
    new_model.eval()

    # -------------------
    # Convert the weights
    # -------------------

    logging.info('''convert the model'''     )
    new_model.bert.load_state_dict(original.bert.state_dict()     )
    new_model.decoder.load_state_dict(original.decoder.state_dict()     )
    new_model.generator.load_state_dict(original.generator.state_dict()     )

    # ----------------------------------
    # Make sure the outpus are identical
    # ----------------------------------

    logging.info('''Make sure that the models\' outputs are identical'''     )
    UpperCAmelCase			       =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )

    # prepare the model inputs
    UpperCAmelCase			       =		tokenizer.encode('''This is sample éàalj\'-.'''     )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).unsqueeze(0     )
    UpperCAmelCase			       =		tokenizer.encode('''This is sample 3 éàalj\'-.'''     )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).unsqueeze(0     )

    # failsafe to make sure the weights reset does not affect the
    # loaded weights.
    assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight     )     ) == 0

    # forward pass
    UpperCAmelCase			       =		encoder_input_ids
    UpperCAmelCase			       =		decoder_input_ids
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		None

    # The original model does not apply the geneator layer immediatly but rather in
    # the beam search (where it combines softmax + linear layer). Since we already
    # apply the softmax in our generation process we only apply the linear layer here.
    # We make sure that the outputs of the full stack are identical
    UpperCAmelCase			       =		original(_a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		original.generator(_a     )

    UpperCAmelCase			       =		new_model(
        _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		new_model.generator(_a     )

    UpperCAmelCase			       =		torch.max(torch.abs(output_converted_model - output_original_model     )     ).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a     )     )
    UpperCAmelCase			       =		torch.max(torch.abs(output_converted_generator - output_original_generator     )     ).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a     )     )

    UpperCAmelCase			       =		torch.allclose(_a    ,   _a    ,   atol=1E-3     )
    if are_identical:
        logging.info('''all weights are equal up to 1e-3'''     )
    else:
        raise ValueError('''the weights are different. The new model is likely different from the original one.'''     )

    # The model has been saved with torch.save(model) and this is bound to the exact
    # directory structure. We save the state_dict instead.
    logging.info('''saving the model\'s state dictionary'''     )
    torch.save(
        new_model.state_dict()    ,   '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'''     )


if __name__ == "__main__":
   A     =argparse.ArgumentParser()
   parser.add_argument(
       '--bertabs_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path the official PyTorch dump.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       required=True,
       help='Path to the output PyTorch model.',
   )
   A     =parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




