





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
    import tensorflow as tf

    from ..tf_utils import stable_softmax


if is_torch_available():
    import torch


UpperCamelCase__   : Optional[Any]					  = logging.get_logger(__name__)







@add_end_docstrings(
    A_						,       r'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '						,       )
class   _UpperCamelCase  (       A_  ):







     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  GenericTensor		):






      '''simple docstring'''







      if self.framework == "tf":
       UpperCAmelCase_								=     tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
      elif self.framework == "pt":
       UpperCAmelCase_								=     torch.nonzero(input_ids == self.tokenizer.mask_token_id ,					as_tuple=__lowercase		)
      else:
       raise ValueError("""Unsupported framework"""		)
      return masked_index





     def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  GenericTensor		):






      '''simple docstring'''







      UpperCAmelCase_								=     self.get_masked_index(__lowercase		)
      UpperCAmelCase_								=     np.prod(masked_index.shape		)
      if numel < 1:
       raise PipelineException(
           """fill-mask""" ,					self.model.base_model_prefix ,					F"""No mask_token ({self.tokenizer.mask_token}) found on the input""" ,					)





     def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  GenericTensor		):






      '''simple docstring'''







      if isinstance(__lowercase ,					__lowercase		):
       for model_input in model_inputs:
        self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0]		)
      else:
       for input_ids in model_inputs["input_ids"]:
        self._ensure_exactly_one_mask_token(__lowercase		)





     def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any]=None ,					**__lowercase    :  Any		):






      '''simple docstring'''







      if return_tensors is None:
       UpperCAmelCase_								=     self.framework
      UpperCAmelCase_								=     self.tokenizer(__lowercase ,					return_tensors=__lowercase		)
      self.ensure_exactly_one_mask_token(__lowercase		)
      return model_inputs





     def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  Union[str, Any]		):






      '''simple docstring'''







      UpperCAmelCase_								=     self.model(**__lowercase		)
      UpperCAmelCase_								=     model_inputs["""input_ids"""]
      return model_outputs





     def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  str ,					__lowercase    :  List[Any]=5 ,					__lowercase    :  int=None		):






      '''simple docstring'''







      if target_ids is not None and target_ids.shape[0] < top_k:
       UpperCAmelCase_								=     target_ids.shape[0]
      UpperCAmelCase_								=     model_outputs["""input_ids"""][0]
      UpperCAmelCase_								=     model_outputs["""logits"""]

      if self.framework == "tf":
       UpperCAmelCase_								=     tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()[:, 0]

       UpperCAmelCase_								=     outputs.numpy()

       UpperCAmelCase_								=     outputs[0, masked_index, :]
       UpperCAmelCase_								=     stable_softmax(__lowercase ,					axis=-1		)
       if target_ids is not None:
        UpperCAmelCase_								=     tf.gather_nd(tf.squeeze(__lowercase ,					0		) ,					target_ids.reshape(-1 ,					1		)		)
        UpperCAmelCase_								=     tf.expand_dims(__lowercase ,					0		)

       UpperCAmelCase_								=     tf.math.top_k(__lowercase ,					k=__lowercase		)
       UpperCAmelCase_						,	UpperCAmelCase_								=     topk.values.numpy(), topk.indices.numpy()
      else:
       UpperCAmelCase_								=     torch.nonzero(input_ids == self.tokenizer.mask_token_id ,					as_tuple=__lowercase		).squeeze(-1		)
       # Fill mask pipeline supports only one ${mask_token} per sample

       UpperCAmelCase_								=     outputs[0, masked_index, :]
       UpperCAmelCase_								=     logits.softmax(dim=-1		)
       if target_ids is not None:
        UpperCAmelCase_								=     probs[..., target_ids]

       UpperCAmelCase_						,	UpperCAmelCase_								=     probs.topk(__lowercase		)

      UpperCAmelCase_								=     []
      UpperCAmelCase_								=     values.shape[0] == 1
      for i, (_values, _predictions) in enumerate(zip(values.tolist() ,					predictions.tolist()		)		):
       UpperCAmelCase_								=     []
       for v, p in zip(_values ,					_predictions		):
        # Copy is important since we're going to modify this array in place
        UpperCAmelCase_								=     input_ids.numpy().copy()
        if target_ids is not None:
         UpperCAmelCase_								=     target_ids[p].tolist()

        UpperCAmelCase_								=     p
        # Filter padding out:
        UpperCAmelCase_								=     tokens[np.where(tokens != self.tokenizer.pad_token_id		)]
        # Originally we skip special tokens to give readable output.
        # For multi masks though, the other [MASK] would be removed otherwise
        # making the output look odd, so we add them back
        UpperCAmelCase_								=     self.tokenizer.decode(__lowercase ,					skip_special_tokens=__lowercase		)
        UpperCAmelCase_								=     {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]		), """sequence""": sequence}
        row.append(__lowercase		)
       result.append(__lowercase		)
      if single_mask:
       return result[0]
      return result





     def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Any=None		):






      '''simple docstring'''







      if isinstance(__lowercase ,					__lowercase		):
       UpperCAmelCase_								=     [targets]
      try:
       UpperCAmelCase_								=     self.tokenizer.get_vocab()
      except Exception:
       UpperCAmelCase_								=     {}
      UpperCAmelCase_								=     []
      for target in targets:
       UpperCAmelCase_								=     vocab.get(__lowercase ,					__lowercase		)
       if id_ is None:
        UpperCAmelCase_								=     self.tokenizer(
            __lowercase ,					add_special_tokens=__lowercase ,					return_attention_mask=__lowercase ,					return_token_type_ids=__lowercase ,					max_length=1 ,					truncation=__lowercase ,					)["""input_ids"""]
        if len(__lowercase		) == 0:
         logger.warning(
             F"""The specified target token `{target}` does not exist in the model vocabulary. """
             """We cannot replace it with anything meaningful, ignoring it"""		)
         continue
        UpperCAmelCase_								=     input_ids[0]
        # XXX: If users encounter this pass
        # it becomes pretty slow, so let's make sure
        # The warning enables them to fix the input to
        # get faster performance.
        logger.warning(
            F"""The specified target token `{target}` does not exist in the model vocabulary. """
            F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_		)}`."""		)
       target_ids.append(id_		)
      UpperCAmelCase_								=     list(set(__lowercase		)		)
      if len(__lowercase		) == 0:
       raise ValueError("""At least one target must be provided when passed."""		)
      UpperCAmelCase_								=     np.array(__lowercase		)
      return target_ids





     def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  Optional[int]=None ,					__lowercase    :  Optional[Any]=None		):






      '''simple docstring'''







      UpperCAmelCase_								=     {}

      if targets is not None:
       UpperCAmelCase_								=     self.get_target_ids(__lowercase ,					__lowercase		)
       UpperCAmelCase_								=     target_ids

      if top_k is not None:
       UpperCAmelCase_								=     top_k

      if self.tokenizer.mask_token_id is None:
       raise PipelineException(
           """fill-mask""" ,					self.model.base_model_prefix ,					"""The tokenizer does not define a `mask_token`."""		)
      return {}, {}, postprocess_params





     def __call__(						self    :  Union[str, Any] ,					__lowercase    :  Any ,					*__lowercase    :  Union[str, Any] ,					**__lowercase    :  Optional[int]		):






      '''simple docstring'''







      UpperCAmelCase_								=     super().__call__(__lowercase ,					**__lowercase		)
      if isinstance(__lowercase ,					__lowercase		) and len(__lowercase		) == 1:
       return outputs[0]
      return outputs









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

UpperCamelCase__   : Union[str, Any]					  = {
    """169M""": 7_68,
    """430M""": 10_24,
    """1B5""": 20_48,
    """3B""": 25_60,
    """7B""": 40_96,
    """14B""": 51_20,
}




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

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

		UpperCAmelCase_								=     weight
	return state_dict




def    A_(			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."""   )
		UpperCAmelCase_								=     50277
		UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""   )
	else:
		UpperCAmelCase_								=     PreTrainedTokenizerFast(tokenizer_file=A   )
		UpperCAmelCase_								=     len(A   )
	tokenizer.save_pretrained(A   )

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

	UpperCAmelCase_								=     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
	UpperCAmelCase_								=     hf_hub_download(A				,      A   )
	UpperCAmelCase_								=     torch.load(A				,      map_location="""cpu"""   )
	UpperCAmelCase_								=     convert_state_dict(A   )

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

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

		del state_dict
		del shards
		gc.collect()

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


if __name__ == "__main__":
				UpperCamelCase__   : 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.""",
				)

				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,
				)


