



'''simple docstring'''





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


_UpperCamelCase											=						'src/transformers'


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

def        a_     (							_lowerCAmelCase       )      ->      List[Any]:
  if _re_test_backend.search(_lowerCAmelCase       ) is None:
    return None
  __lowerCamelCase       :       Union[str, Any]           = [b[0] for b in _re_backend.findall(_lowerCAmelCase       )]
  backends.sort()
  return "_and_".join(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       )      ->      str:
  with open(_lowerCAmelCase       ,'r'       ,encoding='utf-8'       ,newline='\n'       ) as f:
    __lowerCamelCase       :       List[str]           = f.readlines()

  __lowerCamelCase       :       Optional[Any]           = 0
  while line_index < len(_lowerCAmelCase       ) and not lines[line_index].startswith('_import_structure = {'       ):
    line_index += 1

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

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

  __lowerCamelCase       :       Any           = {'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.
    __lowerCamelCase       :       Union[str, 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:
      __lowerCamelCase       :       str           = 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

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

      __lowerCamelCase       :       Tuple           = objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  __lowerCamelCase       :       Any           = []
  while (
      line_index < len(_lowerCAmelCase       )
      and find_backend(lines[line_index]       ) is None
      and not lines[line_index].startswith('else'       )
  ):
    __lowerCamelCase       :       Any           = lines[line_index]
    __lowerCamelCase       :       Dict           = _re_import.search(_lowerCAmelCase       )
    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

  __lowerCamelCase       :       Dict           = {'none': objects}
  # Let's continue with backend-specific objects
  while line_index < len(_lowerCAmelCase       ):
    # If the line is an if is_backend_available, we grab all objects associated.
    __lowerCamelCase       :       Dict           = 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:
      __lowerCamelCase       :       List[str]           = 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

      __lowerCamelCase       :       int           = []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(' ' * 8       ):
        __lowerCamelCase       :       Tuple           = lines[line_index]
        __lowerCamelCase       :       Dict           = _re_import.search(_lowerCAmelCase       )
        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

      __lowerCamelCase       :       Optional[Any]           = objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      str:

  def find_duplicates(_lowerCAmelCase       ):
    return [k for k, v in collections.Counter(_lowerCAmelCase       ).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!"]

  __lowerCamelCase       :       Any           = []
  for key in import_dict_objects.keys():
    __lowerCamelCase       :       Any           = find_duplicates(import_dict_objects[key]       )
    if duplicate_imports:
      errors.append(F'Duplicate _import_structure definitions for: {duplicate_imports}'       )
    __lowerCamelCase       :       Union[str, Any]           = 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]       )       ):
      __lowerCamelCase       :       Dict           = '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        a_     (							)      ->      Optional[int]:
  __lowerCamelCase       :       Union[str, Any]           = []
  for root, _, files in os.walk(_lowerCAmelCase       ):
    if "__init__.py" in files:
      __lowerCamelCase       :       Tuple           = os.path.join(_lowerCAmelCase       ,'__init__.py'       )
      __lowerCamelCase       :       Tuple           = parse_init(_lowerCAmelCase       )
      if objects is not None:
        __lowerCamelCase       :       List[str]           = analyze_results(*_lowerCAmelCase       )
        if len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       Any           = F'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
          failures.append('\n'.join(_lowerCAmelCase       )       )
  if len(_lowerCAmelCase       ) > 0:
    raise ValueError('\n\n'.join(_lowerCAmelCase       )       )

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


_UpperCamelCase											=						[
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]

def        a_     (							)      ->      Union[str, Any]:
  # This is to make sure the transformers module imported is the one in the repo.
  __lowerCamelCase       :       Any           = importlib.util.spec_from_file_location(
      'transformers'       ,os.path.join(_lowerCAmelCase       ,'__init__.py'       )       ,submodule_search_locations=[PATH_TO_TRANSFORMERS]       ,)
  __lowerCamelCase       :       Union[str, Any]           = spec.loader.load_module()

  __lowerCamelCase       :       Optional[int]           = [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(_lowerCAmelCase       ) > 0:
    __lowerCamelCase       :       int           = '\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


_UpperCamelCase											=						logging.get_logger(__name__)

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=False       )      ->      Tuple:
  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:
    __lowerCamelCase       :       str           = os.path.abspath(_lowerCAmelCase       )
    logger.info(F'Loading PyTorch weights from {pt_path}'       )

    __lowerCamelCase       :       Optional[Any]           = torch.load(_lowerCAmelCase       ,map_location='cpu'       )
    logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters.'       )

    __lowerCamelCase       :       Tuple           = convert_pytorch_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    __lowerCamelCase       :       Union[str, Any]           = convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase       ,_lowerCAmelCase       )
  return flax_state_dict

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,)      ->      (Tuple[str], np.ndarray):

  def is_key_or_prefix_key_in_dict(_lowerCAmelCase       ) -> bool:
    return len(set(_lowerCAmelCase       ) & {key, (model_prefix,) + key}       ) > 0

  # layer norm
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('scale',)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  __lowerCamelCase       :       int           = pt_tuple_key[:-1] + ('mean',)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  __lowerCamelCase       :       List[str]           = pt_tuple_key[:-1] + ('var',)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('embedding',)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  __lowerCamelCase       :       List[str]           = 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(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[Any]           = pt_tensor.transpose(2       ,3       ,1       ,0       )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  __lowerCamelCase       :       Optional[Any]           = pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase       ):
    __lowerCamelCase       :       Dict           = pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  __lowerCamelCase       :       Tuple           = pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  __lowerCamelCase       :       int           = 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
  __lowerCamelCase       :       Optional[int]           = None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    __lowerCamelCase       :       Union[str, Any]           = pt_tuple_key[-2] + '_g'
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    __lowerCamelCase       :       Tuple           = pt_tuple_key[-2] + '_v'
  if name is not None:
    __lowerCamelCase       :       Any           = pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  # convert pytorch tensor to numpy
  __lowerCamelCase       :       Optional[int]           = {k: v.numpy() for k, v in pt_state_dict.items()}

  __lowerCamelCase       :       str           = flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    __lowerCamelCase       :       List[Any]           = flax_model.params['params']
  else:
    __lowerCamelCase       :       List[str]           = flax_model.params
  __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    __lowerCamelCase       :       Optional[int]           = flatten_dict(flax_model.params['batch_stats']       )
    random_flax_state_dict.update(_lowerCAmelCase       )

  __lowerCamelCase       :       str           = {}

  __lowerCamelCase       :       Union[str, Any]           = (model_prefix not in flax_model_params) and (
      model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
  )
  __lowerCamelCase       :       List[str]           = (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():
    __lowerCamelCase       :       Any           = tuple(pt_key.split('.'       )       )

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

    # Correctly rename weight parameters
    __lowerCamelCase       ,__lowerCamelCase       :       Dict           = rename_key_and_reshape_tensor(
        _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )

    # add model prefix if necessary
    __lowerCamelCase       :       Dict           = (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Dict           = (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]:
        __lowerCamelCase       :       List[Any]           = jnp.asarray(_lowerCAmelCase       )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
        continue

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

    else:
      # also add unexpected weight so that warning is thrown
      __lowerCamelCase       :       str           = jnp.asarray(_lowerCAmelCase       )

  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Any:
  import torch

  # Load the index
  __lowerCamelCase       :       Optional[int]           = {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    __lowerCamelCase       :       Dict           = torch.load(_lowerCAmelCase       )
    __lowerCamelCase       :       Dict           = {k: v.numpy() for k, v in pt_state_dict.items()}

    __lowerCamelCase       :       Optional[Any]           = 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:
      __lowerCamelCase       :       str           = flax_model.params['params']

      __lowerCamelCase       :       Tuple           = flatten_dict(_lowerCAmelCase       )
      random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']       )       )
    else:
      __lowerCamelCase       :       Dict           = flax_model.params
      __lowerCamelCase       :       Optional[int]           = flatten_dict(_lowerCAmelCase       )

    __lowerCamelCase       :       List[Any]           = (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
    )
    __lowerCamelCase       :       List[str]           = (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():
      __lowerCamelCase       :       Optional[Any]           = tuple(pt_key.split('.'       )       )

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

      # Correctly rename weight parameters
      __lowerCamelCase       ,__lowerCamelCase       :       Any           = rename_key_and_reshape_tensor(
          _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
      # add model prefix if necessary
      __lowerCamelCase       :       Tuple           = (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        __lowerCamelCase       :       Optional[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]:
          __lowerCamelCase       :       int           = jnp.asarray(_lowerCAmelCase       )
          continue
        if "var" in flax_key[-1]:
          __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_lowerCAmelCase       ,_lowerCAmelCase       )
          continue

        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Optional[int]           = jnp.asarray(_lowerCAmelCase       )

      else:
        # also add unexpected weight so that warning is thrown
        __lowerCamelCase       :       Tuple           = jnp.asarray(_lowerCAmelCase       )
  return unflatten_dict(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  __lowerCamelCase       :       Union[str, Any]           = os.path.abspath(_lowerCAmelCase       )
  logger.info(F'Loading Flax weights from {flax_checkpoint_path}'       )

  # import correct flax class
  __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,'Flax' + model.__class__.__name__       )

  # load flax weight dict
  with open(_lowerCAmelCase       ,'rb'       ) as state_f:
    try:
      __lowerCamelCase       :       Tuple           = from_bytes(_lowerCAmelCase       ,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(_lowerCAmelCase       ,_lowerCAmelCase       )


def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:

  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
  __lowerCamelCase       :       Any           = flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase       : x.dtype == jnp.bfloataa       ,_lowerCAmelCase       )       ).values()
  if any(_lowerCAmelCase       ):
    # 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.'       )
    __lowerCamelCase       :       Dict           = jax.tree_util.tree_map(
        lambda _lowerCAmelCase       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params       ,_lowerCAmelCase       )

  __lowerCamelCase       :       Any           = flatten_dict(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = pt_model.state_dict()

  __lowerCamelCase       :       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()}
  )
  __lowerCamelCase       :       Tuple           = (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
  __lowerCamelCase       :       Any           = []
  __lowerCamelCase       :       Union[str, Any]           = set(pt_model_dict.keys()       )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __lowerCamelCase       :       List[str]           = flax_key_tuple[0] == pt_model.base_model_prefix
    __lowerCamelCase       :       Dict           = '.'.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:
      __lowerCamelCase       :       List[Any]           = flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      __lowerCamelCase       :       Optional[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(_lowerCAmelCase       ) not in pt_model_dict:
      # conv layer
      __lowerCamelCase       :       Tuple           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       Tuple           = jnp.transpose(_lowerCAmelCase       ,(3, 2, 0, 1)       )
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase       ) not in pt_model_dict:
      # linear layer
      __lowerCamelCase       :       Dict           = flax_key_tuple[:-1] + ('weight',)
      __lowerCamelCase       :       str           = flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      __lowerCamelCase       :       Optional[Any]           = flax_key_tuple[:-1] + ('weight',)

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

    if "batch_stats" in flax_state:
      __lowerCamelCase       :       Tuple           = '.'.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
    else:
      __lowerCamelCase       :       Optional[int]           = '.'.join(_lowerCAmelCase       )

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

    if flax_key in special_pt_names:
      __lowerCamelCase       :       int           = 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
        __lowerCamelCase       :       Tuple           = np.asarray(_lowerCAmelCase       ) if not isinstance(_lowerCAmelCase       ,np.ndarray       ) else flax_tensor
        __lowerCamelCase       :       List[str]           = torch.from_numpy(_lowerCAmelCase       )
        # remove from missing keys
        missing_keys.remove(_lowerCAmelCase       )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(_lowerCAmelCase       )

  pt_model.load_state_dict(_lowerCAmelCase       )

  # re-transform missing_keys to list
  __lowerCamelCase       :       int           = list(_lowerCAmelCase       )

  if len(_lowerCAmelCase       ) > 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(_lowerCAmelCase       ) > 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






