





import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)

UpperCamelCase__   : Union[str, Any]					  = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
UpperCamelCase__   : List[Any]					  = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class   _UpperCamelCase  :







     '''simple docstring'''

     lowerCamelCase     :     str			    =       field(
         default=A_						,       metadata={'help': 'Model type selected in the list: ' + ', '.join(A_  )}  )
     lowerCamelCase     :     str			    =       field(
         default=A_						,       metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}  )
     lowerCamelCase     :     int			    =       field(
         default=128						,       metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }						,       )
     lowerCamelCase     :     int			    =       field(
         default=128						,       metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}						,       )
     lowerCamelCase     :     int			    =       field(
         default=64						,       metadata={
             'help': (
                 'The maximum number of tokens for the question. Questions longer than this will '
                 'be truncated to this length.'
             )
         }						,       )
     lowerCamelCase     :     int			    =       field(
         default=30						,       metadata={
             'help': (
                 'The maximum length of an answer that can be generated. This is needed because the start '
                 'and end predictions are not conditioned on one another.'
             )
         }						,       )
     lowerCamelCase     :     bool			    =       field(
         default=A_						,       metadata={'help': 'Overwrite the cached training and evaluation sets'}  )
     lowerCamelCase     :     bool			    =       field(
         default=A_						,       metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}  )
     lowerCamelCase     :     float			    =       field(
         default=0.0						,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}  )
     lowerCamelCase     :     int			    =       field(
         default=20						,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}  )
     lowerCamelCase     :     int			    =       field(
         default=0						,       metadata={
             'help': (
                 'language id of input for language-specific xlm models (see'
                 ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
             )
         }						,       )
     lowerCamelCase     :     int			    =       field(default=1						,       metadata={'help': 'multiple threads for converting example to features'}  )







class   _UpperCamelCase  (       A_  ):







     '''simple docstring'''

     lowerCamelCase     :     List[str]			    =       'train'
     lowerCamelCase     :     Optional[int]			    =       'dev'







class   _UpperCamelCase  (       A_  ):







     '''simple docstring'''

     lowerCamelCase     :     SquadDataTrainingArguments
     lowerCamelCase     :     List[SquadFeatures]
     lowerCamelCase     :     Split
     lowerCamelCase     :     bool





     def __init__(						self    :  str ,					__lowercase    :  SquadDataTrainingArguments ,					__lowercase    :  PreTrainedTokenizer ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Union[str, Split] = Split.train ,					__lowercase    :  Optional[bool] = False ,					__lowercase    :  Optional[str] = None ,					__lowercase    :  Optional[str] = "pt" ,					):






      '''simple docstring'''







      UpperCAmelCase_								=     args
      UpperCAmelCase_								=     is_language_sensitive
      UpperCAmelCase_								=     SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
      if isinstance(__lowercase ,					__lowercase		):
       try:
        UpperCAmelCase_								=     Split[mode]
       except KeyError:
        raise KeyError("""mode is not a valid split name"""		)
      UpperCAmelCase_								=     mode
      # Load data features from cache or dataset file
      UpperCAmelCase_								=     """v2""" if args.version_2_with_negative else """v1"""
      UpperCAmelCase_								=     os.path.join(
          cache_dir if cache_dir is not None else args.data_dir ,					F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}""" ,					)

      # Make sure only the first process in distributed training processes the dataset,
      # and the others will use the cache.
      UpperCAmelCase_								=     cached_features_file + """.lock"""
      with FileLock(__lowercase		):
       if os.path.exists(__lowercase		) and not args.overwrite_cache:
        UpperCAmelCase_								=     time.time()
        UpperCAmelCase_								=     torch.load(__lowercase		)

        # Legacy cache files have only features, while new cache files
        # will have dataset and examples also.
        UpperCAmelCase_								=     self.old_features["""features"""]
        UpperCAmelCase_								=     self.old_features.get("""dataset""" ,					__lowercase		)
        UpperCAmelCase_								=     self.old_features.get("""examples""" ,					__lowercase		)
        logger.info(
            F"""Loading features from cached file {cached_features_file} [took %.3f s]""" ,					time.time() - start		)

        if self.dataset is None or self.examples is None:
         logger.warning(
             F"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
             """ future run"""		)
       else:
        if mode == Split.dev:
         UpperCAmelCase_								=     self.processor.get_dev_examples(args.data_dir		)
        else:
         UpperCAmelCase_								=     self.processor.get_train_examples(args.data_dir		)

        UpperCAmelCase_						,	UpperCAmelCase_								=     squad_convert_examples_to_features(
            examples=self.examples ,					tokenizer=__lowercase ,					max_seq_length=args.max_seq_length ,					doc_stride=args.doc_stride ,					max_query_length=args.max_query_length ,					is_training=mode == Split.train ,					threads=args.threads ,					return_dataset=__lowercase ,					)

        UpperCAmelCase_								=     time.time()
        torch.save(
            {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} ,					__lowercase ,					)
        # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
        logger.info(
            F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""		)





     def __len__(						self    :  int		):






      '''simple docstring'''







      return len(self.features		)





     def __getitem__(						self    :  List[Any] ,					__lowercase    :  Optional[Any]		):






      '''simple docstring'''







      UpperCAmelCase_								=     self.features[i]

      UpperCAmelCase_								=     torch.tensor(feature.input_ids ,					dtype=torch.long		)
      UpperCAmelCase_								=     torch.tensor(feature.attention_mask ,					dtype=torch.long		)
      UpperCAmelCase_								=     torch.tensor(feature.token_type_ids ,					dtype=torch.long		)
      UpperCAmelCase_								=     torch.tensor(feature.cls_index ,					dtype=torch.long		)
      UpperCAmelCase_								=     torch.tensor(feature.p_mask ,					dtype=torch.float		)
      UpperCAmelCase_								=     torch.tensor(feature.is_impossible ,					dtype=torch.float		)

      UpperCAmelCase_								=     {
          """input_ids""": input_ids,
          """attention_mask""": attention_mask,
          """token_type_ids""": token_type_ids,
      }

      if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
       del inputs["token_type_ids"]

      if self.args.model_type in ["xlnet", "xlm"]:
       inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}		)
       if self.args.version_2_with_negative:
        inputs.update({"""is_impossible""": is_impossible}		)
       if self.is_language_sensitive:
        inputs.update({"""langs""": (torch.ones(input_ids.shape ,					dtype=torch.intaa		) * self.args.lang_id)}		)

      if self.mode == Split.train:
       UpperCAmelCase_								=     torch.tensor(feature.start_position ,					dtype=torch.long		)
       UpperCAmelCase_								=     torch.tensor(feature.end_position ,					dtype=torch.long		)
       inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}		)

      return inputs









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,
				)


