

"""simple docstring"""






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						=		logging.get_logger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )
    if "model" in sd.keys():
        _lowerCAmelCase       :   Union[str, Any]  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )['model']

    # pop unnecessary weights
    _lowerCAmelCase       :   List[Any]  =    [
        'decoder.version',
        'decoder.output_projection.weight',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(_lowerCamelCase   )

    _lowerCAmelCase       :   Any  =    {
        'decoder.project_in_dim.weight': 'decoder.project_in.weight',
        'decoder.project_out_dim.weight': 'decoder.project_out.weight',
        'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
        'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            _lowerCAmelCase       :   Tuple  =    sd.pop(_lowerCamelCase   )

    _lowerCAmelCase       :   Optional[Any]  =    list(sd.keys()   )
    for key in keys:
        if ".qkv_proj." in key:
            _lowerCAmelCase       :   int  =    sd[key]
            # We split QKV in separate Q,K,V

            _lowerCAmelCase       :   List[Any]  =    key.replace('.qkv_proj.' ,   '.q_proj.'   )
            _lowerCAmelCase       :   Optional[int]  =    key.replace('.qkv_proj.' ,   '.k_proj.'   )
            _lowerCAmelCase       :   Any  =    key.replace('.qkv_proj.' ,   '.v_proj.'   )

            _lowerCAmelCase       :   Optional[Any]  =    value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   int  =    torch.split(_lowerCamelCase ,   depth // 3 ,   dim=0   )

            _lowerCAmelCase       :   int  =    q
            _lowerCAmelCase       :   List[Any]  =    k
            _lowerCAmelCase       :   Optional[int]  =    v
            del sd[key]

    return sd

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    load_checkpoint(_lowerCamelCase   )

    if config is not None:
        _lowerCAmelCase       :   str  =    OPTConfig.from_pretrained(_lowerCamelCase   )
    else:
        _lowerCAmelCase       :   Tuple  =    OPTConfig()

    _lowerCAmelCase       :   Dict  =    OPTModel(_lowerCamelCase   ).half().eval()
    model.load_state_dict(_lowerCamelCase   )

    # Check results
    Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
    model.save_pretrained(_lowerCamelCase   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--fairseq_path""",
     type=str,
     help=(
         """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
         """ https://huggingface.co/models?other=opt_metasq"""
     ),
 )
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
 _lowerCAmelCase						=		parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








"""simple docstring"""






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

_lowerCAmelCase						=		get_logger()

_lowerCAmelCase						=		None







class 	__UpperCamelCase    (    TensorFormatter[Mapping, "jax.Array", Mapping]						):

  def __init__(		self    ,_A=None    ,_A=None    ,**_A      ):
      '''simple docstring'''

      super().__init__(features=_A      )
      import jax
      from jaxlib.xla_client import Device

      if isinstance(_A    ,_A      ):
          raise ValueError(
              F"""Expected {device} to be a `str` not {type(_A      )}, as `jaxlib.xla_extension.Device` """
              'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
              'the device with `str()` to get its string identifier that will be internally mapped '
              'to the actual `jaxlib.xla_extension.Device`.'      )
      _lowerCAmelCase       :   int  =    device if isinstance(_A    ,_A      ) else str(jax.devices()[0]      )
      # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
      # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
          _lowerCAmelCase       :   Any  =    self._map_devices_to_str()
      if self.device not in list(DEVICE_MAPPING.keys()      ):
          logger.warning(
              F"""Device with string identifier {self.device} not listed among the available """
              F"""devices: {list(DEVICE_MAPPING.keys()      )}, so falling back to the default """
              F"""device: {str(jax.devices()[0]      )}."""      )
          _lowerCAmelCase       :   List[str]  =    str(jax.devices()[0]      )
      _lowerCAmelCase       :   int  =    jnp_array_kwargs

  @staticmethod
  def __lowerCamelCase		(		):
      '''simple docstring'''

      import jax

      return {str(_A      ): device for device in jax.devices()}

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax
      import jax.numpy as jnp

      if isinstance(_A    ,_A      ) and column:
          if all(
              isinstance(_A    ,jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
              return jnp.stack(_A    ,axis=0      )
      return column

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax
      import jax.numpy as jnp

      if isinstance(_A    ,(str, bytes, type(_A      ))      ):
          return value
      elif isinstance(_A    ,(np.character, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.character      ):
          return value.tolist()

      _lowerCAmelCase       :   Optional[Any]  =    {}

      if isinstance(_A    ,(np.number, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.integer      ):
          # the default int precision depends on the jax config
          # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
          if jax.config.jax_enable_xaa:
              _lowerCAmelCase       :   List[str]  =    {'dtype': jnp.intaa}
          else:
              _lowerCAmelCase       :   Tuple  =    {'dtype': jnp.intaa}
      elif isinstance(_A    ,(np.number, np.ndarray)      ) and np.issubdtype(value.dtype    ,np.floating      ):
          _lowerCAmelCase       :   Any  =    {'dtype': jnp.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
          import PIL.Image

          if isinstance(_A    ,PIL.Image.Image      ):
              _lowerCAmelCase       :   int  =    np.asarray(_A      )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
          _lowerCAmelCase       :   Optional[Any]  =    self._map_devices_to_str()

      with jax.default_device(DEVICE_MAPPING[self.device]      ):
          # calling jnp.array on a np.ndarray does copy the data
          # see https://github.com/google/jax/issues/4486
          return jnp.array(_A    ,**{**default_dtype, **self.jnp_array_kwargs}      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      import jax

      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
          import torch

          if isinstance(_A    ,torch.Tensor      ):
              return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
      if hasattr(_A    ,'__array__'      ) and not isinstance(_A    ,jax.Array      ):
          _lowerCAmelCase       :   Optional[Any]  =    data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_A    ,np.ndarray      ):
          if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
              return self._consolidate([self.recursive_tensorize(_A      ) for substruct in data_struct]      )
      elif isinstance(_A    ,(list, tuple)      ):
          return self._consolidate([self.recursive_tensorize(_A      ) for substruct in data_struct]      )
      return self._tensorize(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return map_nested(self._recursive_tensorize    ,_A    ,map_list=_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.numpy_arrow_extractor().extract_row(_A      )
      _lowerCAmelCase       :   int  =    self.python_features_decoder.decode_row(_A      )
      return self.recursive_tensorize(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.numpy_arrow_extractor().extract_column(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.python_features_decoder.decode_column(_A    ,pa_table.column_names[0]      )
      _lowerCAmelCase       :   Optional[Any]  =    self.recursive_tensorize(_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self._consolidate(_A      )
      return column







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.numpy_arrow_extractor().extract_batch(_A      )
      _lowerCAmelCase       :   Any  =    self.python_features_decoder.decode_batch(_A      )
      _lowerCAmelCase       :   str  =    self.recursive_tensorize(_A      )
      for column_name in batch:
          _lowerCAmelCase       :   Optional[Any]  =    self._consolidate(batch[column_name]      )
      return batch





