





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

SCREAMING_SNAKE_CASE_						   =       {
    """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    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						list(state_dict.keys()	)
      for name in state_dict_keys:
            SCREAMING_SNAKE_CASE		=						state_dict.pop(_SCREAMING_SNAKE_CASE	)
            # 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""" ,						_SCREAMING_SNAKE_CASE	)
            # ffn -> feed_forward
            SCREAMING_SNAKE_CASE		=						re.sub(r"""blocks\.(\d+)\.ffn""" ,						r"""blocks.\1.feed_forward""" ,						_SCREAMING_SNAKE_CASE	)
            # 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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=False ,						_SCREAMING_SNAKE_CASE=None	) ->   Optional[Any]:


      '''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=_SCREAMING_SNAKE_CASE	)
            SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE	)
      tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE	)

      # 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=_SCREAMING_SNAKE_CASE ,						num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] ,						hidden_size=HIDEN_SIZE_MAPPING[size] ,						)
      config.save_pretrained(_SCREAMING_SNAKE_CASE	)

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

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

      if index is not None:
            SCREAMING_SNAKE_CASE		=						os.path.join(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
            # Save the index as well
            with open(_SCREAMING_SNAKE_CASE ,						"""w""" ,						encoding="""utf-8"""	) as f:
                  SCREAMING_SNAKE_CASE		=						json.dumps(_SCREAMING_SNAKE_CASE ,						indent=2 ,						sort_keys=_SCREAMING_SNAKE_CASE	) + """\n"""
                  f.write(_SCREAMING_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."""	)
            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(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)	)
                  torch.save({k: v.cpu().clone() for k, v in state_dict.items()} ,						os.path.join(_SCREAMING_SNAKE_CASE ,						_SCREAMING_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."""	)
            SCREAMING_SNAKE_CASE		=						AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE	)
            model.push_to_hub(_SCREAMING_SNAKE_CASE ,						max_shard_size="""2GB"""	)
            tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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_						   =       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,
      )









import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

SCREAMING_SNAKE_CASE_						   =       random.Random()



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=1.0 ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   Tuple:


      '''simple docstring'''


      if rng is None:
            SCREAMING_SNAKE_CASE		=						global_rng

      SCREAMING_SNAKE_CASE		=						[]
      for batch_idx in range(shape[0]	):
            values.append([]	)
            for _ in range(shape[1]	):
                  values[-1].append(rng.random() * scale	)

      return values

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def __init__(							self						:    List[Any]						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Optional[int]=7						,lowerCamelCase__						:    Optional[Any]=400						,lowerCamelCase__						:    List[str]=2000						,lowerCamelCase__						:    List[str]=2048						,lowerCamelCase__						:    Any=128						,lowerCamelCase__						:    List[str]=1						,lowerCamelCase__						:    str=512						,lowerCamelCase__						:    Optional[Any]=30						,lowerCamelCase__						:    Tuple=44100						,)     ->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						min_seq_length
           SCREAMING_SNAKE_CASE		=						max_seq_length
           SCREAMING_SNAKE_CASE		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           SCREAMING_SNAKE_CASE		=						spectrogram_length
           SCREAMING_SNAKE_CASE		=						feature_size
           SCREAMING_SNAKE_CASE		=						num_audio_channels
           SCREAMING_SNAKE_CASE		=						hop_length
           SCREAMING_SNAKE_CASE		=						chunk_length
           SCREAMING_SNAKE_CASE		=						sampling_rate






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Optional[int]:




           '''simple docstring'''


           return {
               "spectrogram_length": self.spectrogram_length,
               "feature_size": self.feature_size,
               "num_audio_channels": self.num_audio_channels,
               "hop_length": self.hop_length,
               "chunk_length": self.chunk_length,
               "sampling_rate": self.sampling_rate,
           }






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    Tuple=False						,lowerCamelCase__						:    Union[str, Any]=False       )     ->		str:




           '''simple docstring'''


           def _flatten(lowerCamelCase__						:    List[Any]       ):
                 return list(itertools.chain(*lowerCamelCase__       )       )

           if equal_length:
                 SCREAMING_SNAKE_CASE		=						[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
           else:
                 # make sure that inputs increase in size
                 SCREAMING_SNAKE_CASE		=						[
                     floats_list((x, self.feature_size)       )
                     for x in range(self.min_seq_length						,self.max_seq_length						,self.seq_length_diff       )
                 ]
           if numpify:
                 SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for x in speech_inputs]
           return speech_inputs

@require_torch
@require_torchaudio
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[Any]			=			TvltFeatureExtractor






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						TvltFeatureExtractionTester(self       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""spectrogram_length"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""feature_size"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""num_audio_channels"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""hop_length"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""chunk_length"""       )       )
           self.assertTrue(hasattr(lowerCamelCase__						,"""sampling_rate"""       )       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						feat_extract_first.save_pretrained(lowerCamelCase__       )[0]
                 check_json_file_has_correct_format(lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_pretrained(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						dict_first.pop("""mel_filters"""       )
           SCREAMING_SNAKE_CASE		=						dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""feat_extract.json"""       )
                 feat_extract_first.to_json_file(lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						self.feature_extraction_class.from_json_file(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						feat_extract_first.to_dict()
           SCREAMING_SNAKE_CASE		=						feat_extract_second.to_dict()
           SCREAMING_SNAKE_CASE		=						dict_first.pop("""mel_filters"""       )
           SCREAMING_SNAKE_CASE		=						dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__       )       )
           self.assertEqual(lowerCamelCase__						,lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict       )

           # create three inputs of length 800, 1000, and 1200
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in range(800						,1400						,200       )]
           SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__       ) for speech_input in speech_inputs]

           # Test not batched input
           SCREAMING_SNAKE_CASE		=						feature_extractor(np_speech_inputs[0]						,return_tensors="""np"""						,sampling_rate=44100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test batched
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test audio masking
           SCREAMING_SNAKE_CASE		=						feature_extractor(
               lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100						,mask_audio=lowerCamelCase__       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test 2-D numpy arrays are batched.
           SCREAMING_SNAKE_CASE		=						[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           SCREAMING_SNAKE_CASE		=						np.asarray(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""						,sampling_rate=44100       ).audio_values
           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    str       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""						,"""clean"""						,split="""validation"""       )
           # automatic decoding with librispeech
           SCREAMING_SNAKE_CASE		=						ds.sort("""id"""       ).select(range(lowerCamelCase__       )       )[:num_samples]["""audio"""]

           return [x["array"] for x in speech_samples]






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._load_datasamples(1       )
           SCREAMING_SNAKE_CASE		=						TvltFeatureExtractor()
           SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""pt"""       ).audio_values

           self.assertEquals(audio_values.shape						,(1, 1, 192, 128)       )

           SCREAMING_SNAKE_CASE		=						torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]       )
           self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,lowerCamelCase__						,atol=1e-4       )       )


