





'''simple docstring'''






import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
      from transformers import LlamaTokenizerFast
except ImportError as e:
      warnings.warn(e)
      warnings.warn(
          'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
      )
      __lowerCamelCase						: List[str]		=   None
__lowerCamelCase						: Union[str, Any]		=   {
    '7B': 1_1008,
    '13B': 1_3824,
    '30B': 1_7920,
    '65B': 2_2016,
    '70B': 2_8672,
}
__lowerCamelCase						: Optional[Any]		=   {
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=1  ,      __SCREAMING_SNAKE_CASE=256   ):


  """simple docstring"""

  return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3   )   ) + multiple_of - 1) // multiple_of)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  with open(__SCREAMING_SNAKE_CASE  ,      '''r'''   ) as f:
    return json.load(__SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  with open(__SCREAMING_SNAKE_CASE  ,      '''w'''   ) as f:
    json.dump(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=True   ):


  """simple docstring"""

  os.makedirs(__SCREAMING_SNAKE_CASE  ,      exist_ok=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=os.path.join(__SCREAMING_SNAKE_CASE  ,      '''tmp'''   )
  os.makedirs(__SCREAMING_SNAKE_CASE  ,      exist_ok=__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=read_json(os.path.join(__SCREAMING_SNAKE_CASE  ,      '''params.json'''   )   )
  _UpperCamelCase       			=NUM_SHARDS[model_size]
  _UpperCamelCase       			=params['''n_layers''']
  _UpperCamelCase       			=params['''n_heads''']
  _UpperCamelCase       			=n_heads // num_shards
  _UpperCamelCase       			=params['''dim''']
  _UpperCamelCase       			=dim // n_heads
  _UpperCamelCase       			=1_0_0_0_0.0
  _UpperCamelCase       			=1.0 / (base ** (torch.arange(0  ,      __SCREAMING_SNAKE_CASE  ,      2   ).float() / dims_per_head))

  if "n_kv_heads" in params:
    _UpperCamelCase       			=params['''n_kv_heads''']  # for GQA / MQA
    _UpperCamelCase       			=n_heads_per_shard // num_key_value_heads
    _UpperCamelCase       			=dim // num_key_value_heads
  else:  # compatibility with other checkpoints
    _UpperCamelCase       			=n_heads
    _UpperCamelCase       			=n_heads_per_shard
    _UpperCamelCase       			=dim

  # permute for sliced rotary
  def permute(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=n_heads  ,      __SCREAMING_SNAKE_CASE=dim  ,      __SCREAMING_SNAKE_CASE=dim   ):
    return w.view(__SCREAMING_SNAKE_CASE  ,      dima // n_heads // 2  ,      2  ,      __SCREAMING_SNAKE_CASE   ).transpose(1  ,      2   ).reshape(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  print(f'''Fetching all parameters from the checkpoint at {input_base_path}.'''   )
  # Load weights
  if model_size == "7B":
    # Not sharded
    # (The sharded implementation would also work, but this is simpler.)
    _UpperCamelCase       			=torch.load(os.path.join(__SCREAMING_SNAKE_CASE  ,      '''consolidated.00.pth'''   )  ,      map_location='''cpu'''   )
  else:
    # Sharded
    _UpperCamelCase       			=[
        torch.load(os.path.join(__SCREAMING_SNAKE_CASE  ,      f'''consolidated.{i:02d}.pth'''   )  ,      map_location='''cpu'''   )
        for i in range(__SCREAMING_SNAKE_CASE   )
    ]
  _UpperCamelCase       			=0
  _UpperCamelCase       			={'''weight_map''': {}}
  for layer_i in range(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=f'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
    if model_size == "7B":
      # Unsharded
      _UpperCamelCase       			={
          f'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
              loaded[f'''layers.{layer_i}.attention.wq.weight''']   ),
          f'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
              loaded[f'''layers.{layer_i}.attention.wk.weight''']   ),
          f'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[f'''layers.{layer_i}.attention.wv.weight'''],
          f'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[f'''layers.{layer_i}.attention.wo.weight'''],
          f'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w1.weight'''],
          f'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w2.weight'''],
          f'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w3.weight'''],
          f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[f'''layers.{layer_i}.attention_norm.weight'''],
          f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[f'''layers.{layer_i}.ffn_norm.weight'''],
      }
    else:
      # Sharded
      # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
      # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
      # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

      _UpperCamelCase       			={
          f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
              f'''layers.{layer_i}.attention_norm.weight'''
          ].clone(),
          f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
              f'''layers.{layer_i}.ffn_norm.weight'''
          ].clone(),
      }
      _UpperCamelCase       			=permute(
          torch.cat(
              [
                  loaded[i][f'''layers.{layer_i}.attention.wq.weight'''].view(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
                  for i in range(__SCREAMING_SNAKE_CASE   )
              ]  ,      dim=0  ,      ).reshape(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )   )
      _UpperCamelCase       			=permute(
          torch.cat(
              [
                  loaded[i][f'''layers.{layer_i}.attention.wk.weight'''].view(
                      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
                  for i in range(__SCREAMING_SNAKE_CASE   )
              ]  ,      dim=0  ,      ).reshape(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      )
      _UpperCamelCase       			=torch.cat(
          [
              loaded[i][f'''layers.{layer_i}.attention.wv.weight'''].view(
                  __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
              for i in range(__SCREAMING_SNAKE_CASE   )
          ]  ,      dim=0  ,      ).reshape(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

      _UpperCamelCase       			=torch.cat(
          [loaded[i][f'''layers.{layer_i}.attention.wo.weight'''] for i in range(__SCREAMING_SNAKE_CASE   )]  ,      dim=1   )
      _UpperCamelCase       			=torch.cat(
          [loaded[i][f'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(__SCREAMING_SNAKE_CASE   )]  ,      dim=0   )
      _UpperCamelCase       			=torch.cat(
          [loaded[i][f'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(__SCREAMING_SNAKE_CASE   )]  ,      dim=1   )
      _UpperCamelCase       			=torch.cat(
          [loaded[i][f'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(__SCREAMING_SNAKE_CASE   )]  ,      dim=0   )

    _UpperCamelCase       			=inv_freq
    for k, v in state_dict.items():
      _UpperCamelCase       			=filename
      param_count += v.numel()
    torch.save(__SCREAMING_SNAKE_CASE  ,      os.path.join(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )   )

  _UpperCamelCase       			=f'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
  if model_size == "7B":
    # Unsharded
    _UpperCamelCase       			={
        '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
        '''model.norm.weight''': loaded['''norm.weight'''],
        '''lm_head.weight''': loaded['''output.weight'''],
    }
  else:
    _UpperCamelCase       			={
        '''model.norm.weight''': loaded[0]['''norm.weight'''],
        '''model.embed_tokens.weight''': torch.cat(
            [loaded[i]['''tok_embeddings.weight'''] for i in range(__SCREAMING_SNAKE_CASE   )]  ,      dim=1   ),
        '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(__SCREAMING_SNAKE_CASE   )]  ,      dim=0   ),
    }

  for k, v in state_dict.items():
    _UpperCamelCase       			=filename
    param_count += v.numel()
  torch.save(__SCREAMING_SNAKE_CASE  ,      os.path.join(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )   )

  # Write configs
  _UpperCamelCase       			={'''total_size''': param_count * 2}
  write_json(__SCREAMING_SNAKE_CASE  ,      os.path.join(__SCREAMING_SNAKE_CASE  ,      '''pytorch_model.bin.index.json'''   )   )
  _UpperCamelCase       			=params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
  _UpperCamelCase       			=params['''multiple_of'''] if '''multiple_of''' in params else 256
  _UpperCamelCase       			=LlamaConfig(
      hidden_size=__SCREAMING_SNAKE_CASE  ,      intermediate_size=compute_intermediate_size(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )  ,      num_attention_heads=params['''n_heads''']  ,      num_hidden_layers=params['''n_layers''']  ,      rms_norm_eps=params['''norm_eps''']  ,      num_key_value_heads=__SCREAMING_SNAKE_CASE  ,      )
  config.save_pretrained(__SCREAMING_SNAKE_CASE   )

  # Make space so we can load the model properly now.
  del state_dict
  del loaded
  gc.collect()

  print('''Loading the checkpoint in a Llama model.'''   )
  _UpperCamelCase       			=LlamaForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE  ,      torch_dtype=torch.floataa  ,      low_cpu_mem_usage=__SCREAMING_SNAKE_CASE   )
  # Avoid saving this as part of the config.
  del model.config._name_or_path

  print('''Saving in the Transformers format.'''   )
  model.save_pretrained(__SCREAMING_SNAKE_CASE  ,      safe_serialization=__SCREAMING_SNAKE_CASE   )
  shutil.rmtree(__SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
  print(f'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''   )
  _UpperCamelCase       			=tokenizer_class(__SCREAMING_SNAKE_CASE   )
  tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE   )


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=argparse.ArgumentParser()
  parser.add_argument(
      '''--input_dir'''  ,      help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''  ,      )
  parser.add_argument(
      '''--model_size'''  ,      choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']  ,      )
  parser.add_argument(
      '''--output_dir'''  ,      help='''Location to write HF model and tokenizer'''  ,      )
  parser.add_argument('''--safe_serialization'''  ,      type=__SCREAMING_SNAKE_CASE  ,      help='''Whether or not to save using `safetensors`.'''   )
  _UpperCamelCase       			=parser.parse_args()
  if args.model_size != "tokenizer_only":
    write_model(
        model_path=args.output_dir  ,      input_base_path=os.path.join(args.input_dir  ,      args.model_size   )  ,      model_size=args.model_size  ,      safe_serialization=args.safe_serialization  ,      )
  _UpperCamelCase       			=os.path.join(args.input_dir  ,      '''tokenizer.model'''   )
  write_tokenizer(args.output_dir  ,      __SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase						: str		=   {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: List[str]		=   ['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: str		=   ['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Optional[Any]		=   [
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Union[str, Any]		=   [
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Optional[int]		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






