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

__lowerCamelCase			: Optional[Any]							   =		{
    '''169M''': 768,
    '''430M''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any]	)							->					str:







  """simple docstring"""





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

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

    SCREAMING_SNAKE_CASE__										=      weight
  return state_dict



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[Any]=None ,     __UpperCamelCase    :       List[Any]=None ,     __UpperCamelCase    :       Optional[Any]=False ,     __UpperCamelCase    :       Dict=None	)							->					Optional[int]:







  """simple docstring"""





  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""	)
    SCREAMING_SNAKE_CASE__										=      5_02_77
    SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""	)
  else:
    SCREAMING_SNAKE_CASE__										=      PreTrainedTokenizerFast(tokenizer_file=__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      len(__UpperCamelCase	)
  tokenizer.save_pretrained(__UpperCamelCase	)

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

  SCREAMING_SNAKE_CASE__										=      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
  SCREAMING_SNAKE_CASE__										=      hf_hub_download(__UpperCamelCase ,     __UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      torch.load(__UpperCamelCase ,     map_location="""cpu"""	)
  SCREAMING_SNAKE_CASE__										=      convert_state_dict(__UpperCamelCase	)

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

  if index is not None:
    SCREAMING_SNAKE_CASE__										=      os.path.join(__UpperCamelCase ,     __UpperCamelCase	)
    # Save the index as well
    with open(__UpperCamelCase ,     """w""" ,     encoding="""utf-8"""	) as f:
      SCREAMING_SNAKE_CASE__										=      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."""	)
    SCREAMING_SNAKE_CASE__										=      list(shards.keys()	)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      SCREAMING_SNAKE_CASE__										=      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."""	)
    SCREAMING_SNAKE_CASE__										=      AutoModelForCausalLM.from_pretrained(__UpperCamelCase	)
    model.push_to_hub(__UpperCamelCase ,     max_shard_size="""2GB"""	)
    tokenizer.push_to_hub(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: 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.''',
   )

   __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,
   )

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
   __lowerCamelCase			: str							   =		input('''Enter image url: ''').strip()
   print(F"""Downloading image from {url} ...""")
   __lowerCamelCase			: Any							   =		BeautifulSoup(requests.get(url).content, '''html.parser''')
   # The image URL is in the content field of the first meta tag with property og:image
   __lowerCamelCase			: List[Any]							   =		soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
   __lowerCamelCase			: Tuple							   =		requests.get(image_url).content
   __lowerCamelCase			: Union[str, Any]							   =		F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
   with open(file_name, '''wb''') as fp:
      fp.write(image_data)
   print(F"""Done. Image saved to disk as {file_name}.""")
