




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


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

_lowerCamelCase	:		List[Any]							       =   {
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    list(state_dict.keys()						)
       for name in state_dict_keys:
              A__       =    state_dict.pop(lowercase_						)
              # 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''' ,				lowercase_						)
              # ffn -> feed_forward
              A__       =    re.sub(R'''blocks\.(\d+)\.ffn''' ,				R'''blocks.\1.feed_forward''' ,				lowercase_						)
              # 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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_=None ,				lowercase_=None ,				lowercase_=False ,				lowercase_=None						)   ->       Any:



       """simple docstring"""

       if tokenizer_file is None:
              print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''						)
              A__       =    50_277
              A__       =    AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''						)
       else:
              A__       =    PreTrainedTokenizerFast(tokenizer_file=lowercase_						)
              A__       =    len(lowercase_						)
       tokenizer.save_pretrained(lowercase_						)

       # 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=lowercase_ ,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] ,				hidden_size=HIDEN_SIZE_MAPPING[size] ,				)
       config.save_pretrained(lowercase_						)

       # 3. Download model file then convert state_dict
       A__       =    hf_hub_download(lowercase_ ,				lowercase_						)
       A__       =    torch.load(lowercase_ ,				map_location='''cpu'''						)
       A__       =    convert_state_dict(lowercase_						)

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

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

              # 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(lowercase_ ,				lowercase_						)						)
                     torch.save({k: v.cpu().clone() for k, v in state_dict.items()} ,				os.path.join(lowercase_ ,				lowercase_						)						)

       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(lowercase_						)
              model.push_to_hub(lowercase_ ,				max_shard_size='''2GB'''						)
              tokenizer.push_to_hub(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[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.""",
  )

  _lowerCamelCase	:		str							       =   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,
  )








import os
import sys
import unittest


_lowerCamelCase	:		Optional[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase	:		Any							       =   os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase	:		str							       =   os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)

           A__       =    {'''BertModelTest''': '''BertModelTester'''}

           A__       =    {
               '''BlipModelTest''': '''BlipModelTester''',
               '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
               '''BlipTextModelTest''': '''BlipTextModelTester''',
               '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
               '''BlipVQAModelTest''': '''BlipVQAModelTester''',
               '''BlipVisionModelTest''': '''BlipVisionModelTester''',
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    get_model_to_test_mapping(UpperCAmelCase__)
           A__       =    get_model_to_test_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTest'''],
               '''BertForMultipleChoice''': ['''BertModelTest'''],
               '''BertForNextSentencePrediction''': ['''BertModelTest'''],
               '''BertForPreTraining''': ['''BertModelTest'''],
               '''BertForQuestionAnswering''': ['''BertModelTest'''],
               '''BertForSequenceClassification''': ['''BertModelTest'''],
               '''BertForTokenClassification''': ['''BertModelTest'''],
               '''BertLMHeadModel''': ['''BertModelTest'''],
               '''BertModel''': ['''BertModelTest'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
               '''BlipModel''': ['''BlipModelTest'''],
               '''BlipTextModel''': ['''BlipTextModelTest'''],
               '''BlipVisionModel''': ['''BlipVisionModelTest'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->str:





           '''simple docstring'''

           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTester'''],
               '''BertForMultipleChoice''': ['''BertModelTester'''],
               '''BertForNextSentencePrediction''': ['''BertModelTester'''],
               '''BertForPreTraining''': ['''BertModelTester'''],
               '''BertForQuestionAnswering''': ['''BertModelTester'''],
               '''BertForSequenceClassification''': ['''BertModelTester'''],
               '''BertForTokenClassification''': ['''BertModelTester'''],
               '''BertLMHeadModel''': ['''BertModelTester'''],
               '''BertModel''': ['''BertModelTester'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
               '''BlipModel''': ['''BlipModelTester'''],
               '''BlipTextModel''': ['''BlipTextModelTester'''],
               '''BlipVisionModel''': ['''BlipVisionModelTester'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


