


'''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


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

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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  list(state_dict.keys()      )
   for name in state_dict_keys:
      _a					:  List[Any]						=  state_dict.pop(__snake_case      )
      # 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					:  Dict						=  name.replace('blocks.0.ln0'		,	'blocks.0.pre_ln'      )
      # att -> attention
      _a					:  List[str]						=  re.sub(r'blocks\.(\d+)\.att'		,	r'blocks.\1.attention'		,	__snake_case      )
      # ffn -> feed_forward
      _a					:  str						=  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'      ):
         _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					:  List[str]						=  name.replace('.time_mix_v'		,	'.time_mix_value'      )
      # time_mix_r -> time_mix_key and reshape
      if name.endswith('.time_mix_r'      ):
         _a					:  Optional[int]						=  name.replace('.time_mix_r'		,	'.time_mix_receptance'      )

      if name != "head.weight":
         _a					:  List[Any]						=  'rwkv.' + name

      _a					:  Dict						=  weight
   return state_dict


def    UpperCAmelCase_  (       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					:  int						=  5_0_2_7_7
      _a					:  Optional[int]						=  AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'      )
   else:
      _a					:  Any						=  PreTrainedTokenizerFast(tokenizer_file=__snake_case      )
      _a					:  str						=  len(__snake_case      )
   tokenizer.save_pretrained(__snake_case      )

   # 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					:  Union[str, Any]						=  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[str]						=  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
   _a					:  Optional[int]						=  hf_hub_download(__snake_case		,	__snake_case      )
   _a					:  Optional[int]						=  torch.load(__snake_case		,	map_location='cpu'      )
   _a					:  Any						=  convert_state_dict(__snake_case      )

   # 4. Split in shards and save
   _a		,						_a					:  List[Any]						=  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:
      _a					:  str						=  os.path.join(__snake_case		,	__snake_case      )
      # Save the index as well
      with open(__snake_case		,	'w'		,	encoding='utf-8'      ) as f:
         _a					:  str						=  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.'      )
      _a					:  Any						=  list(shards.keys()      )

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         _a					:  str						=  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.'      )
      _a					:  List[str]						=  AutoModelForCausalLM.from_pretrained(__snake_case      )
      model.push_to_hub(__snake_case		,	max_shard_size='2GB'      )
      tokenizer.push_to_hub(__snake_case      )


if __name__ == "__main__":
 UpperCAmelCase_      :		List[str]          =		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.",
 )

 UpperCAmelCase_      :		List[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,
 )



'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['''bs''']    ,     model_result['''ss'''] ):
           lowerCAmelCase_			=      model_result['''result'''][batch_size][sequence_length]
           self.assertIsNotNone(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sgugger/tiny-distilbert-classification'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     only_pretrain_model=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     torchscript=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Cant do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     fpaa=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     # set architectures equal to `None`
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  @unittest.skipIf(torch_device == '''cpu'''    ,     '''Can\'t do half precision''' )
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     fpaa=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result )
     self.check_results_dict_not_empty(results.memory_inference_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      benchmark.run()
     self.check_results_dict_not_empty(results.time_train_result )
     self.check_results_dict_not_empty(results.memory_train_result )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     save_to_csv=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     inference_time_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' )    ,     train_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' )    ,     inference_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' )    ,     train_time_csv_file=os.path.join(_lowerCamelCase    ,     '''train_time.csv''' )    ,     env_info_csv_file=os.path.join(_lowerCamelCase    ,     '''env.csv''' )    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        benchmark.run()
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''env.csv''' ) ).exists() )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase ):
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''sequential''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''cumulative''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''current''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''total''' ) )

     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     log_filename=os.path.join(_lowerCamelCase    ,     '''log.txt''' )    ,     log_print=_lowerCamelCase    ,     trace_memory_line_by_line=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        lowerCAmelCase_			=      benchmark.run()
        _check_summary_is_not_empty(result.inference_summary )
        _check_summary_is_not_empty(result.train_summary )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''log.txt''' ) ).exists() )





