




"""simple docstring"""


import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard


def  UpperCAmelCase__	(snake_case__							: List[Any]   ):
  """simple docstring"""

  assert "mock" in _fsspec_registry
  assert "bz2" in _fsspec_registry


def  UpperCAmelCase__	():
  """simple docstring"""

  assert "mock" not in _fsspec_registry
  assert "bz2" in _fsspec_registry


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  """mock-s3-bucket"""
  _snake_case							:		Optional[Any]								=  F"s3://{mock_bucket}"
  _snake_case							:		Optional[int]								=  extract_path_from_uri(snake_case__   )
  assert dataset_path.startswith("""s3://"""   ) is False

  _snake_case							:		List[Any]								=  """./local/path"""
  _snake_case							:		Optional[Any]								=  extract_path_from_uri(snake_case__   )
  assert dataset_path == new_dataset_path


def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  _snake_case							:		str								=  is_remote_filesystem(snake_case__   )
  assert is_remote is True

  _snake_case							:		Optional[int]								=  fsspec.filesystem("""file"""   )

  _snake_case							:		Optional[int]								=  is_remote_filesystem(snake_case__   )
  assert is_remote is False


@pytest.mark.parametrize("""compression_fs_class"""		,       snake_case__   )
def  UpperCAmelCase__	(snake_case__							: Any		,       snake_case__							: List[str]		,       snake_case__							: Any		,       snake_case__							: Any		,       snake_case__							: Tuple		,       snake_case__							: Optional[Any]		,       snake_case__							: Any   ):
  """simple docstring"""

  _snake_case							:		int								=  {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_file, """bz2""": bza_file, """lz4""": lza_file}
  _snake_case							:		Dict								=  input_paths[compression_fs_class.protocol]
  if input_path is None:
    _snake_case							:		int								=  F"for '{compression_fs_class.protocol}' compression protocol, "
    if compression_fs_class.protocol == "lz4":
      reason += require_lza.kwargs["reason"]
    elif compression_fs_class.protocol == "zstd":
      reason += require_zstandard.kwargs["reason"]
    pytest.skip(snake_case__   )
  _snake_case							:		Union[str, Any]								=  fsspec.filesystem(compression_fs_class.protocol		,       fo=snake_case__   )
  assert isinstance(snake_case__		,       snake_case__   )
  _snake_case							:		Dict								=  os.path.basename(snake_case__   )
  _snake_case							:		Dict								=  expected_filename[: expected_filename.rindex("""."""   )]
  assert fs.glob("""*"""   ) == [expected_filename]
  with fs.open(snake_case__		,       """r"""		,       encoding="""utf-8"""   ) as f, open(snake_case__		,       encoding="""utf-8"""   ) as expected_file:
    assert f.read() == expected_file.read()


@pytest.mark.parametrize("""protocol"""		,       ["""zip""", """gzip"""]   )
def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: Optional[Any]		,       snake_case__							: Dict   ):
  """simple docstring"""

  _snake_case							:		Any								=  {"""zip""": zip_jsonl_path, """gzip""": jsonl_gz_path}
  _snake_case							:		List[Any]								=  compressed_file_paths[protocol]
  _snake_case							:		Dict								=  """dataset.jsonl"""
  _snake_case							:		Union[str, Any]								=  F"{protocol}://{member_file_path}::{compressed_file_path}"
  _snake_case		, *_snake_case							:		str								=  fsspec.get_fs_token_paths(snake_case__   )
  assert fs.isfile(snake_case__   )
  assert not fs.isfile("""non_existing_""" + member_file_path   )


@pytest.mark.integration
def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Dict		,       snake_case__							: str		,       snake_case__							: List[Any]   ):
  """simple docstring"""

  _snake_case							:		Any								=  hf_api.dataset_info(snake_case__		,       token=snake_case__   )
  _snake_case							:		Union[str, Any]								=  HfFileSystem(repo_info=snake_case__		,       token=snake_case__   )
  assert sorted(hffs.glob("""*"""   )   ) == [".gitattributes", "data"]
  assert hffs.isdir("""data"""   )
  assert hffs.isfile(""".gitattributes"""   ) and hffs.isfile("""data/text_data.txt"""   )
  with open(snake_case__   ) as f:
    assert hffs.open("""data/text_data.txt"""		,       """r"""   ).read() == f.read()






def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  """bz2"""

  # Import module
  import datasets.filesystems

  # Overwrite protocol and reload
  register_implementation(snake_case__		,       snake_case__		,       clobber=snake_case__   )
  with pytest.warns(snake_case__   ) as warning_info:
    importlib.reload(datasets.filesystems   )

  assert len(snake_case__   ) == 1
  assert (
      str(warning_info[0].message   )
      == F"A filesystem protocol was already set for {protocol} and will be overwritten."
  )








"""simple docstring"""


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


A_      =    logging.get_logger(__name__)


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Dict		,       snake_case__							: Union[str, Any]		,       snake_case__							: int=False   ):
  """simple docstring"""

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
        """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
        """ instructions."""   )
    raise

  if not is_sharded:
    _snake_case							:		Dict								=  os.path.abspath(snake_case__   )
    logger.info(F"Loading PyTorch weights from {pt_path}"   )

    _snake_case							:		Tuple								=  torch.load(snake_case__		,       map_location="""cpu"""   )
    logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."   )

    _snake_case							:		int								=  convert_pytorch_state_dict_to_flax(snake_case__		,       snake_case__   )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    _snake_case							:		Dict								=  convert_pytorch_sharded_state_dict_to_flax(snake_case__		,       snake_case__   )
  return flax_state_dict


def  UpperCAmelCase__	(snake_case__							: Tuple[str]		,       snake_case__							: np.ndarray		,       snake_case__							: Dict[str, jnp.ndarray]		,       snake_case__							: str		,       ):
  """simple docstring"""

  def is_key_or_prefix_key_in_dict(snake_case__							: Tuple[str]   ) -> bool:
    return len(set(snake_case__   ) & {key, (model_prefix,) + key}   ) > 0

  # layer norm
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""scale""",)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  _snake_case							:		Optional[Any]								=  pt_tuple_key[:-1] + ("""mean""",)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""var""",)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""embedding""",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _snake_case							:		Optional[int]								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case__   ):
    _snake_case							:		Dict								=  pt_tensor.transpose(2		,       3		,       1		,       0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  _snake_case							:		List[str]								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    _snake_case							:		List[Any]								=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  _snake_case							:		List[Any]								=  pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  _snake_case							:		Tuple								=  pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  _snake_case							:		Optional[Any]								=  None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[-2] + """_g"""
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    _snake_case							:		Dict								=  pt_tuple_key[-2] + """_v"""
  if name is not None:
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  {k: v.numpy() for k, v in pt_state_dict.items()}

  _snake_case							:		int								=  flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    _snake_case							:		Dict								=  flax_model.params["""params"""]
  else:
    _snake_case							:		List[Any]								=  flax_model.params
  _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    _snake_case							:		Union[str, Any]								=  flatten_dict(flax_model.params["""batch_stats"""]   )
    random_flax_state_dict.update(snake_case__   )

  _snake_case							:		Tuple								=  {}

  _snake_case							:		Dict								=  (model_prefix not in flax_model_params) and (
      model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
  )
  _snake_case							:		Optional[int]								=  (model_prefix in flax_model_params) and (
      model_prefix not in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    _snake_case							:		int								=  tuple(pt_key.split("""."""   )   )

    # remove base model prefix if necessary
    _snake_case							:		Optional[Any]								=  pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  pt_tuple_key[1:]

    # Correctly rename weight parameters
    _snake_case		, _snake_case							:		int								=  rename_key_and_reshape_tensor(
        snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )

    # add model prefix if necessary
    _snake_case							:		Dict								=  (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		int								=  (model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        _snake_case							:		Union[str, Any]								=  jnp.asarray(snake_case__   )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(snake_case__		,       snake_case__   )
        continue

      # also add unexpected weight so that warning is thrown
      _snake_case							:		List[Any]								=  jnp.asarray(snake_case__   )

    else:
      # also add unexpected weight so that warning is thrown
      _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )

  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Dict   ):
  """simple docstring"""

  import torch

  # Load the index
  _snake_case							:		str								=  {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    _snake_case							:		Union[str, Any]								=  torch.load(snake_case__   )
    _snake_case							:		Optional[Any]								=  {k: v.numpy() for k, v in pt_state_dict.items()}

    _snake_case							:		List[str]								=  flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      _snake_case							:		str								=  flax_model.params["""params"""]

      _snake_case							:		List[Any]								=  flatten_dict(snake_case__   )
      random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]   )   )
    else:
      _snake_case							:		List[Any]								=  flax_model.params
      _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

    _snake_case							:		Tuple								=  (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
    )
    _snake_case							:		Optional[Any]								=  (model_prefix in flax_model_params) and (
        model_prefix not in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      _snake_case							:		List[str]								=  tuple(pt_key.split("""."""   )   )

      # remove base model prefix if necessary
      _snake_case							:		str								=  pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        _snake_case							:		Optional[Any]								=  pt_tuple_key[1:]

      # Correctly rename weight parameters
      _snake_case		, _snake_case							:		Optional[Any]								=  rename_key_and_reshape_tensor(
          snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
      # add model prefix if necessary
      _snake_case							:		List[str]								=  (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        _snake_case							:		Any								=  (model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
              F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."   )

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          _snake_case							:		Optional[int]								=  jnp.asarray(snake_case__   )
          continue
        if "var" in flax_key[-1]:
          _snake_case							:		Any								=  jnp.asarray(snake_case__   )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(snake_case__		,       snake_case__   )
          continue

        # also add unexpected weight so that warning is thrown
        _snake_case							:		List[str]								=  jnp.asarray(snake_case__   )

      else:
        # also add unexpected weight so that warning is thrown
        _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )
  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  os.path.abspath(snake_case__   )
  logger.info(F"Loading Flax weights from {flax_checkpoint_path}"   )

  # import correct flax class
  _snake_case							:		Union[str, Any]								=  getattr(snake_case__		,       """Flax""" + model.__class__.__name__   )

  # load flax weight dict
  with open(snake_case__		,       """rb"""   ) as state_f:
    try:
      _snake_case							:		Dict								=  from_bytes(snake_case__		,       state_f.read()   )
    except UnpicklingError:
      raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "   )

  return load_flax_weights_in_pytorch_model(snake_case__		,       snake_case__   )






def  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
        """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
        """ instructions."""   )
    raise

  # check if we have bf16 weights
  _snake_case							:		Optional[int]								=  flatten_dict(jax.tree_util.tree_map(lambda snake_case__   : x.dtype == jnp.bfloataa		,       snake_case__   )   ).values()
  if any(snake_case__   ):
    # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
    # and bf16 is not fully supported in PT yet.
    logger.warning(
        """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
        """before loading those in PyTorch model."""   )
    _snake_case							:		Optional[int]								=  jax.tree_util.tree_map(
        lambda snake_case__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params		,       snake_case__   )

  _snake_case							:		Dict								=  flatten_dict(snake_case__   )
  _snake_case							:		Optional[Any]								=  pt_model.state_dict()

  _snake_case							:		Union[str, Any]								=  (pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split("""."""   )[0] for k in pt_model_dict.keys()}
  )
  _snake_case							:		Optional[int]								=  (pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split("""."""   )[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  _snake_case							:		str								=  []
  _snake_case							:		Tuple								=  set(pt_model_dict.keys()   )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    _snake_case							:		Tuple								=  flax_key_tuple[0] == pt_model.base_model_prefix
    _snake_case							:		Optional[Any]								=  """.""".join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _snake_case							:		List[str]								=  flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  (pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case__   ) not in pt_model_dict:
      # conv layer
      _snake_case							:		Union[str, Any]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Optional[int]								=  jnp.transpose(snake_case__		,       (3, 2, 0, 1)   )
    elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__   ) not in pt_model_dict:
      # linear layer
      _snake_case							:		Optional[int]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Union[str, Any]								=  flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      _snake_case							:		int								=  flax_key_tuple[:-1] + ("""weight""",)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      _snake_case							:		Tuple								=  flax_key_tuple[:-1] + ("""running_mean""",)
    elif "var" in flax_key_tuple[-1]:
      _snake_case							:		Optional[int]								=  flax_key_tuple[:-1] + ("""running_var""",)

    if "batch_stats" in flax_state:
      _snake_case							:		int								=  """.""".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
    else:
      _snake_case							:		int								=  """.""".join(snake_case__   )

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    _snake_case							:		Optional[Any]								=  {}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      _snake_case							:		List[str]								=  key.split("""."""   )
      _snake_case							:		Optional[int]								=  None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        _snake_case							:		int								=  key_components[-2] + """_g"""
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        _snake_case							:		Union[str, Any]								=  key_components[-2] + """_v"""
      if name is not None:
        _snake_case							:		Dict								=  key_components[:-3] + [name]
        _snake_case							:		Dict								=  """.""".join(snake_case__   )
        _snake_case							:		str								=  key

    if flax_key in special_pt_names:
      _snake_case							:		Union[str, Any]								=  special_pt_names[flax_key]

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
            F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."   )
      else:
        # add weight to pytorch dict
        _snake_case							:		List[str]								=  np.asarray(snake_case__   ) if not isinstance(snake_case__		,       np.ndarray   ) else flax_tensor
        _snake_case							:		List[Any]								=  torch.from_numpy(snake_case__   )
        # remove from missing keys
        missing_keys.remove(snake_case__   )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(snake_case__   )

  pt_model.load_state_dict(snake_case__   )

  # re-transform missing_keys to list
  _snake_case							:		List[str]								=  list(snake_case__   )

  if len(snake_case__   ) > 0:
    logger.warning(
        """Some weights of the Flax model were not used when initializing the PyTorch model"""
        F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
        F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
        """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
        F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
        """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
        """ FlaxBertForSequenceClassification model)."""   )
  else:
    logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"   )
  if len(snake_case__   ) > 0:
    logger.warning(
        F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
        F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
        """ use it for predictions and inference."""   )
  else:
    logger.warning(
        F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
        """If your task is similar to the task the model of the checkpoint was trained on, """
        F"you can already use {pt_model.__class__.__name__} for predictions without further training."   )

  return pt_model


