






"""simple docstring"""




import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Any							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=None							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						List[str]=None							,						**UpperCAmelCase__		:						str							)					->       Optional[Any]:
   _a  :       List[str]             =	parent
   _a  :       str             =	config_class
   _a  :       Tuple             =	has_text_modality
   _a  :       str             =	kwargs
   _a  :       Optional[int]             =	common_properties


 def _lowercase	(     self		:						Optional[Any]							)					->       Optional[Any]:
   _a  :       Tuple             =	self.config_class(**self.inputs_dict							)
   _a  :       Any             =	(
       ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
       if self.common_properties is None
       else self.common_properties
   )

   # Add common fields for text models
   if self.has_text_modality:
     common_properties.extend(["""vocab_size"""]							)

   # Test that config has the common properties as getters
   for prop in common_properties:
     self.parent.assertTrue(hasattr(UpperCAmelCase__							,						UpperCAmelCase__							)							,						msg=f"""`{prop}` does not exist"""							)

   # Test that config has the common properties as setter
   for idx, name in enumerate(UpperCAmelCase__							):
     try:
       setattr(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)
       self.parent.assertEqual(
           getattr(UpperCAmelCase__							,						UpperCAmelCase__							)							,						UpperCAmelCase__							,						msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__							,						UpperCAmelCase__							)}"""							)
     except NotImplementedError:
       # Some models might not be able to implement setters for common_properties
       # In that case, a NotImplementedError is raised
       pass

        # Test if config class can be called with Config(prop_name=..)
   for idx, name in enumerate(UpperCAmelCase__							):
     try:
       _a  :       str             =	self.config_class(**{name: idx}							)
       self.parent.assertEqual(
           getattr(UpperCAmelCase__							,						UpperCAmelCase__							)							,						UpperCAmelCase__							,						msg=f"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase__							,						UpperCAmelCase__							)}"""							)
     except NotImplementedError:
       # Some models might not be able to implement setters for common_properties
       # In that case, a NotImplementedError is raised
       pass


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       str             =	self.config_class(**self.inputs_dict							)
   _a  :       Union[str, Any]             =	json.loads(config.to_json_string()							)
   for key, value in self.inputs_dict.items():
     self.parent.assertEqual(obj[key]							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Optional[Any]							)					->       int:
   _a  :       Dict             =	self.config_class(**self.inputs_dict							)

   with tempfile.TemporaryDirectory() as tmpdirname:
     _a  :       str             =	os.path.join(UpperCAmelCase__							,						"""config.json"""							)
     config_first.to_json_file(UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	self.config_class.from_json_file(UpperCAmelCase__							)

   self.parent.assertEqual(config_second.to_dict()							,						config_first.to_dict()							)


 def _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       str             =	self.config_class(**self.inputs_dict							)

   with tempfile.TemporaryDirectory() as tmpdirname:
     config_first.save_pretrained(UpperCAmelCase__							)
     _a  :       Tuple             =	self.config_class.from_pretrained(UpperCAmelCase__							)

   self.parent.assertEqual(config_second.to_dict()							,						config_first.to_dict()							)


 def _lowercase	(     self		:						int							)					->       Optional[int]:
   _a  :       str             =	self.config_class(**self.inputs_dict							)

   _a  :       int             =	"""test"""
   with tempfile.TemporaryDirectory() as tmpdirname:
     _a  :       List[Any]             =	os.path.join(UpperCAmelCase__							,						UpperCAmelCase__							)
     config_first.save_pretrained(UpperCAmelCase__							)
     _a  :       str             =	self.config_class.from_pretrained(UpperCAmelCase__							,						subfolder=UpperCAmelCase__							)

   self.parent.assertEqual(config_second.to_dict()							,						config_first.to_dict()							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       List[str]:
   _a  :       Union[str, Any]             =	self.config_class(**self.inputs_dict							,						num_labels=5							)
   self.parent.assertEqual(len(config.idalabel							)							,						5							)
   self.parent.assertEqual(len(config.labelaid							)							,						5							)

   _a  :       int             =	3
   self.parent.assertEqual(len(config.idalabel							)							,						3							)
   self.parent.assertEqual(len(config.labelaid							)							,						3							)


 def _lowercase	(     self		:						List[str]							)					->       Union[str, Any]:
   if self.config_class.is_composition:
     return
   _a  :       Union[str, Any]             =	self.config_class()
   self.parent.assertIsNotNone(UpperCAmelCase__							)


 def _lowercase	(     self		:						Any							)					->       Union[str, Any]:
   _a  :       List[str]             =	copy.deepcopy(UpperCAmelCase__							)
   _a  :       str             =	self.config_class(**UpperCAmelCase__							)
   _a  :       str             =	[]
   for key, value in config_common_kwargs.items():
     if key == "torch_dtype":
       if not is_torch_available():
         continue
       else:
         import torch

         if config.torch_dtype != torch.floataa:
           wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa)							)
     elif getattr(UpperCAmelCase__							,						UpperCAmelCase__							) != value:
       wrong_values.append((key, getattr(UpperCAmelCase__							,						UpperCAmelCase__							), value)							)

   if len(UpperCAmelCase__							) > 0:
     _a  :       List[str]             =	"""\n""".join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]							)
     raise ValueError(f"""The following keys were not properly set in the config:\n{errors}"""							)







 def _lowercase	(     self		:						Any							)					->       Any:
   self.create_and_test_config_common_properties()
   self.create_and_test_config_to_json_string()
   self.create_and_test_config_to_json_file()
   self.create_and_test_config_from_and_save_pretrained()
   self.create_and_test_config_from_and_save_pretrained_subfolder()
   self.create_and_test_config_with_num_labels()
   self.check_config_can_be_init_without_params()
   self.check_config_arguments_init()









"""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


_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__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	list(state_dict.keys()				)
  for name in state_dict_keys:
    _a  :       str             =	state_dict.pop(UpperCamelCase__				)
    # emb -> embedding
    if name.startswith("""emb."""				):
      _a  :       Dict             =	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  :       Any             =	re.sub(R"""blocks\.(\d+)\.att"""     ,   R"""blocks.\1.attention"""     ,   UpperCamelCase__				)
    # ffn -> feed_forward
    _a  :       int             =	re.sub(R"""blocks\.(\d+)\.ffn"""     ,   R"""blocks.\1.feed_forward"""     ,   UpperCamelCase__				)
    # 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  :       Tuple             =	name.replace(""".time_mix_v"""     ,   """.time_mix_value"""				)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(""".time_mix_r"""				):
      _a  :       Dict             =	name.replace(""".time_mix_r"""     ,   """.time_mix_receptance"""				)

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

    _a  :       Any             =	weight
  return state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=False     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
    _a  :       Tuple             =	5_0_2_7_7
    _a  :       str             =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
  else:
    _a  :       int             =	PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__				)
    _a  :       int             =	len(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  # 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  :       Tuple             =	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[Any]             =	RwkvConfig(
      vocab_size=UpperCamelCase__     ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,   hidden_size=HIDEN_SIZE_MAPPING[size]     ,   )
  config.save_pretrained(UpperCamelCase__				)

  # 3. Download model file then convert state_dict
  _a  :       str             =	hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       int             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[str]             =	convert_state_dict(UpperCamelCase__				)

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

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

    # 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[Any]             =	list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

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

  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  :       Dict             =	AutoModelForCausalLM.from_pretrained(UpperCamelCase__				)
    model.push_to_hub(UpperCamelCase__     ,   max_shard_size="""2GB"""				)
    tokenizer.push_to_hub(UpperCamelCase__				)


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,
 )

