






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


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

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




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

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

              _UpperCAmelCase							=    weight
       return state_dict




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=None   ,      __snake_case=None   ,      __snake_case=False   ,      __snake_case=None )				->						Optional[Any]:
       # 1. If possible, build the tokenizer.
       if tokenizer_file is None:
              print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
              _UpperCAmelCase							=    5_0_2_7_7
              _UpperCAmelCase							=    AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
       else:
              _UpperCAmelCase							=    PreTrainedTokenizerFast(tokenizer_file=__snake_case )
              _UpperCAmelCase							=    len(__snake_case )
       tokenizer.save_pretrained(__snake_case )

       # 2. Build the config
       _UpperCAmelCase							=    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:
                            _UpperCAmelCase							=    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}.""" )

       _UpperCAmelCase							=    RwkvConfig(
           vocab_size=__snake_case   ,      num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]   ,      hidden_size=HIDEN_SIZE_MAPPING[size]   ,      )
       config.save_pretrained(__snake_case )

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

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

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

              # 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.""" )
              _UpperCAmelCase							=    list(shards.keys() )

              del state_dict
              del shards
              gc.collect()

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

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


if __name__ == "__main__":
  __a:     Dict   					=					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.''',
  )

  __a:     Union[str, Any]   					=					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 unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers import AutoModelForCausalLM



@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											A      						=    TextIteratorStreamer(__UpperCamelCase       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()
											A      						=    ''
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    greedy_ids[:, input_ids.shape[1] :]
											A      						=    tokenizer.decode(new_greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_prompt=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
											# with actual models -- the dummy models' tokenizers are not aligned with their models, and
											# `skip_special_tokens=True` has no effect on them
											A      						=    AutoTokenizer.from_pretrained('distilgpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('distilgpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    torch.ones((1, 5)						, device=__UpperCamelCase       ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=1						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											A      						=    cs.out[:-1]  # Remove the final "\n"
											A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
											self.assertEqual(streamer_text_tokenized.input_ids.shape						, (1, 1)       )


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    TextIteratorStreamer(__UpperCamelCase						, timeout=0.0_0_1       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    ''
																		for new_text in streamer:
																									streamer_text += new_text