

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		       =   {
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

_a		       =   {
    '''169M''': 7_6_8,
    '''430M''': 1_0_2_4,
    '''1B5''': 2_0_4_8,
    '''3B''': 2_5_6_0,
    '''7B''': 4_0_9_6,
    '''14B''': 5_1_2_0,
}







def _a (       SCREAMING_SNAKE_CASE		:			Optional[int]		)    ->    List[Any]:

 """simple docstring"""






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

  if name != "head.weight":
   __lowerCAmelCase:   Any   	=      'rwkv.' + name

  __lowerCAmelCase:   List[Any]   	=      weight
 return state_dict







def _a (       SCREAMING_SNAKE_CASE		:			Union[str, Any]    ,					SCREAMING_SNAKE_CASE		:			Optional[Any]    ,					SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			List[Any]=None    ,					SCREAMING_SNAKE_CASE		:			Tuple=None    ,					SCREAMING_SNAKE_CASE		:			Optional[Any]=False    ,					SCREAMING_SNAKE_CASE		:			Any=None		)    ->    int:

 """simple docstring"""






 if tokenizer_file is None:
  print('No `--tokenizer_file` provided, we will use the default tokenizer.'		)
  __lowerCAmelCase:   Optional[Any]   	=      5_02_77
  __lowerCAmelCase:   Optional[Any]   	=      AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'		)
 else:
  __lowerCAmelCase:   Optional[int]   	=      PreTrainedTokenizerFast(tokenizer_file=__snake_case		)
  __lowerCAmelCase:   Optional[Any]   	=      len(__snake_case		)
 tokenizer.save_pretrained(__snake_case		)

 # 2. Build the config
 __lowerCAmelCase:   Union[str, 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:
    __lowerCAmelCase:   List[str]   	=      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}.'''		)

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

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

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   __lowerCAmelCase:   Union[str, Any]   	=      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.'		)
  __lowerCAmelCase:   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__":
  _a		       =   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		       =   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,
  )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__   	=							{
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''XLMForMultipleChoice''',
      '''XLMForQuestionAnswering''',
      '''XLMForQuestionAnsweringSimple''',
      '''XLMForSequenceClassification''',
      '''XLMForTokenClassification''',
      '''XLMModel''',
      '''XLMPreTrainedModel''',
      '''XLMWithLMHeadModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFXLMForMultipleChoice''',
      '''TFXLMForQuestionAnsweringSimple''',
      '''TFXLMForSequenceClassification''',
      '''TFXLMForTokenClassification''',
      '''TFXLMMainLayer''',
      '''TFXLMModel''',
      '''TFXLMPreTrainedModel''',
      '''TFXLMWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

