




"""simple docstring"""


import collections
import importlib.util
import os
import re
from pathlib import Path


A_      =    'src/transformers'


# Matches is_xxx_available()
A_      =    re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
A_      =    re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A_      =    re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
A_      =    re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
A_      =    re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A_      =    re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
A_      =    re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
A_      =    re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
A_      =    re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
A_      =    re.compile(r'''^\s*try:''')
# Catches a line with else:
A_      =    re.compile(r'''^\s*else:''')


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

  if _re_test_backend.search(snake_case_   ) is None:
    return None
  _snake_case							:		Any								=  [b[0] for b in _re_backend.findall(snake_case_   )]
  backends.sort()
  return "_and_".join(snake_case_   )


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

  with open(snake_case_		,       """r"""		,       encoding="""utf-8"""		,       newline="""\n"""   ) as f:
    _snake_case							:		Any								=  f.readlines()

  _snake_case							:		List[Any]								=  0
  while line_index < len(snake_case_   ) and not lines[line_index].startswith("""_import_structure = {"""   ):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(snake_case_   ):
    return None

  # First grab the objects without a specific backend in _import_structure
  _snake_case							:		List[Any]								=  []
  while not lines[line_index].startswith("""if TYPE_CHECKING"""   ) and find_backend(lines[line_index]   ) is None:
    _snake_case							:		List[str]								=  lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(snake_case_   ):
      _snake_case							:		Tuple								=  _re_one_line_import_struct.search(snake_case_   ).groups()[0]
      _snake_case							:		Union[str, Any]								=  re.findall("""\[([^\]]+)\]"""		,       snake_case_   )
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(""", """   )]   )
      line_index += 1
      continue
    _snake_case							:		Union[str, Any]								=  _re_import_struct_key_value.search(snake_case_   )
    if single_line_import_search is not None:
      _snake_case							:		Optional[Any]								=  [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """   ) if len(snake_case_   ) > 0]
      objects.extend(snake_case_   )
    elif line.startswith(""" """ * 8 + """\""""   ):
      objects.append(line[9:-3]   )
    line_index += 1

  _snake_case							:		List[str]								=  {"""none""": objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith("""if TYPE_CHECKING"""   ):
    # If the line is an if not is_backend_available, we grab all objects associated.
    _snake_case							:		Optional[Any]								=  find_backend(lines[line_index]   )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]   ) is None:
      _snake_case							:		Union[str, Any]								=  None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]   ) is None:
        line_index += 1

      line_index += 1

      _snake_case							:		str								=  []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 4   ):
        _snake_case							:		Union[str, Any]								=  lines[line_index]
        if _re_import_struct_add_one.search(snake_case_   ) is not None:
          objects.append(_re_import_struct_add_one.search(snake_case_   ).groups()[0]   )
        elif _re_import_struct_add_many.search(snake_case_   ) is not None:
          _snake_case							:		Any								=  _re_import_struct_add_many.search(snake_case_   ).groups()[0].split(""", """   )
          _snake_case							:		List[Any]								=  [obj[1:-1] for obj in imports if len(snake_case_   ) > 0]
          objects.extend(snake_case_   )
        elif _re_between_brackets.search(snake_case_   ) is not None:
          _snake_case							:		Any								=  _re_between_brackets.search(snake_case_   ).groups()[0].split(""", """   )
          _snake_case							:		Any								=  [obj[1:-1] for obj in imports if len(snake_case_   ) > 0]
          objects.extend(snake_case_   )
        elif _re_quote_object.search(snake_case_   ) is not None:
          objects.append(_re_quote_object.search(snake_case_   ).groups()[0]   )
        elif line.startswith(""" """ * 8 + """\""""   ):
          objects.append(line[9:-3]   )
        elif line.startswith(""" """ * 12 + """\""""   ):
          objects.append(line[13:-3]   )
        line_index += 1

      _snake_case							:		int								=  objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  _snake_case							:		Optional[Any]								=  []
  while (
      line_index < len(snake_case_   )
      and find_backend(lines[line_index]   ) is None
      and not lines[line_index].startswith("""else"""   )
  ):
    _snake_case							:		Union[str, Any]								=  lines[line_index]
    _snake_case							:		Any								=  _re_import.search(snake_case_   )
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
    elif line.startswith(""" """ * 8   ):
      objects.append(line[8:-2]   )
    line_index += 1

  _snake_case							:		List[str]								=  {"""none""": objects}
  # Let's continue with backend-specific objects
  while line_index < len(snake_case_   ):
    # If the line is an if is_backend_available, we grab all objects associated.
    _snake_case							:		List[Any]								=  find_backend(lines[line_index]   )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]   ) is None:
      _snake_case							:		Any								=  None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]   ) is None:
        line_index += 1

      line_index += 1

      _snake_case							:		Union[str, Any]								=  []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 8   ):
        _snake_case							:		str								=  lines[line_index]
        _snake_case							:		Dict								=  _re_import.search(snake_case_   )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
        elif line.startswith(""" """ * 12   ):
          objects.append(line[12:-2]   )
        line_index += 1

      _snake_case							:		Any								=  objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects


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

  def find_duplicates(snake_case__							: Tuple   ):
    return [k for k, v in collections.Counter(snake_case_   ).items() if v > 1]

  if list(import_dict_objects.keys()   ) != list(type_hint_objects.keys()   ):
    return ["Both sides of the init do not have the same backends!"]

  _snake_case							:		str								=  []
  for key in import_dict_objects.keys():
    _snake_case							:		str								=  find_duplicates(import_dict_objects[key]   )
    if duplicate_imports:
      errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}"   )
    _snake_case							:		Tuple								=  find_duplicates(type_hint_objects[key]   )
    if duplicate_type_hints:
      errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"   )

    if sorted(set(import_dict_objects[key]   )   ) != sorted(set(type_hint_objects[key]   )   ):
      _snake_case							:		int								=  """base imports""" if key == """none""" else F"{key} backend"
      errors.append(F"Differences for {name}:"   )
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(F"  {a} in TYPE_HINT but not in _import_structure."   )
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(F"  {a} in _import_structure but not in TYPE_HINT."   )
  return errors


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

  _snake_case							:		Dict								=  []
  for root, _, files in os.walk(snake_case_   ):
    if "__init__.py" in files:
      _snake_case							:		List[Any]								=  os.path.join(snake_case_		,       """__init__.py"""   )
      _snake_case							:		Tuple								=  parse_init(snake_case_   )
      if objects is not None:
        _snake_case							:		Dict								=  analyze_results(*snake_case_   )
        if len(snake_case_   ) > 0:
          _snake_case							:		List[Any]								=  F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
          failures.append("""\n""".join(snake_case_   )   )
  if len(snake_case_   ) > 0:
    raise ValueError("""\n\n""".join(snake_case_   )   )


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

  _snake_case							:		Tuple								=  []
  for path, directories, files in os.walk(snake_case_   ):
    for folder in directories:
      # Ignore private modules
      if folder.startswith("""_"""   ):
        directories.remove(snake_case_   )
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(snake_case_   ) / folder).glob("""*.py"""   )   )   ) == 0:
        continue
      _snake_case							:		str								=  str((Path(snake_case_   ) / folder).relative_to(snake_case_   )   )
      _snake_case							:		str								=  short_path.replace(os.path.sep		,       """."""   )
      submodules.append(snake_case_   )
    for fname in files:
      if fname == "__init__.py":
        continue
      _snake_case							:		int								=  str((Path(snake_case_   ) / fname).relative_to(snake_case_   )   )
      _snake_case							:		Optional[int]								=  short_path.replace(""".py"""		,       """"""   ).replace(os.path.sep		,       """."""   )
      if len(submodule.split("""."""   )   ) == 1:
        submodules.append(snake_case_   )
  return submodules


A_      =    [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]


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

  _snake_case							:		str								=  importlib.util.spec_from_file_location(
      """transformers"""		,       os.path.join(snake_case_		,       """__init__.py"""   )		,       submodule_search_locations=[PATH_TO_TRANSFORMERS]		,       )
  _snake_case							:		Dict								=  spec.loader.load_module()

  _snake_case							:		Dict								=  [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(snake_case_   ) > 0:
    _snake_case							:		Dict								=  """\n""".join(F"- {module}" for module in module_not_registered   )
    raise ValueError(
        """The following submodules are not properly registered in the main init of Transformers:\n"""
        F"{list_of_modules}\n"
        """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""   )


if __name__ == "__main__":
     check_all_inits()
     check_submodules()








"""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


