"""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


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	{
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}



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

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

              __a     				=  weight
       return state_dict



def 					_lowerCamelCase(					a ,  a ,  a ,  a=None ,  a=None ,  a=False ,  a=None							):
       # 1. If possible, build the tokenizer.
       if tokenizer_file is None:
              print("No `--tokenizer_file` provided, we will use the default tokenizer."							)
              __a     				=  5_0_2_7_7
              __a     				=  AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"							)
       else:
              __a     				=  PreTrainedTokenizerFast(tokenizer_file=a							)
              __a     				=  len(a							)
       tokenizer.save_pretrained(a							)

       # 2. Build the config
       __a     				=  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     				=  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     				=  RwkvConfig(
           vocab_size=a ,  num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] ,  hidden_size=HIDEN_SIZE_MAPPING[size] ,  )
       config.save_pretrained(a							)

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

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

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

              # 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(shards.keys()							)

              del state_dict
              del shards
              gc.collect()

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Optional[int]			=	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.""",
   )

   SCREAMING_SNAKE_CASE__:int			=	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"""



from ...processing_utils import ProcessorMixin






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[str]           =		"""WhisperFeatureExtractor"""
 _snake_case   :			Any           =		"""WhisperTokenizer"""


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase  ,						lowerCamelCase		)
        __a     				=  self.feature_extractor
        __a     				=  False


 def 		a__ (				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase=True		):
        return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase  ,						language=lowerCamelCase  ,						no_timestamps=lowerCamelCase		)


 def __call__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        # For backward compatibility
        if self._in_target_context_manager:
               return self.current_processor(*lowerCamelCase  ,						**lowerCamelCase		)

        __a     				=  kwargs.pop("audio"  ,						lowerCamelCase		)
        __a     				=  kwargs.pop("sampling_rate"  ,						lowerCamelCase		)
        __a     				=  kwargs.pop("text"  ,						lowerCamelCase		)
        if len(lowerCamelCase		) > 0:
               __a     				=  args[0]
               __a     				=  args[1:]

        if audio is None and text is None:
               raise ValueError("You need to specify either an `audio` or `text` input to process."		)

        if audio is not None:
               __a     				=  self.feature_extractor(lowerCamelCase  ,						*lowerCamelCase  ,						sampling_rate=lowerCamelCase  ,						**lowerCamelCase		)
        if text is not None:
               __a     				=  self.tokenizer(lowerCamelCase  ,						**lowerCamelCase		)

        if text is None:
               return inputs

        elif audio is None:
               return encodings
        else:
               __a     				=  encodings["input_ids"]
               return inputs


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase="np"		):
        return self.tokenizer.get_prompt_ids(lowerCamelCase  ,						return_tensors=lowerCamelCase		)
