





'''simple docstring'''


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


lowerCAmelCase_   :   Optional[int]       =					"src/transformers"


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







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]     )   ->    Optional[int]:
			if _re_test_backend.search(__lowerCamelCase     ) is None:
						return None
			a__     = [b[0] for b in _re_backend.findall(__lowerCamelCase     )]
			backends.sort()
			return "_and_".join(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[str]:
			with open(__lowerCamelCase					,  "r"					,  encoding="utf-8"					,  newline="\n"     ) as f:
						a__     = f.readlines()

			a__     = 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
			a__     = []
			while not lines[line_index].startswith("if TYPE_CHECKING"     ) and find_backend(lines[line_index]     ) is None:
						a__     = lines[line_index]
						# If we have everything on a single line, let's deal with it.
						if _re_one_line_import_struct.search(__lowerCamelCase     ):
									a__     = _re_one_line_import_struct.search(__lowerCamelCase     ).groups()[0]
									a__     = re.findall("\[([^\]]+)\]"					,  __lowerCamelCase     )
									for imp in imports:
												objects.extend([obj[1:-1] for obj in imp.split(", "     )]     )
									line_index += 1
									continue
						a__     = _re_import_struct_key_value.search(__lowerCamelCase     )
						if single_line_import_search is not None:
									a__     = [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

			a__     = {"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.
						a__     = 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:
									a__     = 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

									a__     = []
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(" " * 4     ):
												a__     = 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:
															a__     = _re_import_struct_add_many.search(__lowerCamelCase     ).groups()[0].split(", "     )
															a__     = [obj[1:-1] for obj in imports if len(__lowerCamelCase     ) > 0]
															objects.extend(__lowerCamelCase     )
												elif _re_between_brackets.search(__lowerCamelCase     ) is not None:
															a__     = _re_between_brackets.search(__lowerCamelCase     ).groups()[0].split(", "     )
															a__     = [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

									a__     = objects
						else:
									line_index += 1

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

			a__     = {"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.
						a__     = 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:
									a__     = 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

									a__     = []
									# Until we unindent, add backend objects to the list
									while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(" " * 8     ):
												a__     = lines[line_index]
												a__     = _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

									a__     = objects
						else:
									line_index += 1

			return import_dict_objects, type_hint_objects







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     List[str]     )   ->    Union[str, Any]:

			def find_duplicates(__lowerCamelCase  :     str     ):
						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!"]

			a__     = []
			for key in import_dict_objects.keys():
						a__     = find_duplicates(import_dict_objects[key]     )
						if duplicate_imports:
									errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''     )
						a__     = 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]     )     ):
									a__     = "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    _lowerCamelCase  ()   ->    Dict:
			a__     = []
			for root, _, files in os.walk(__lowerCamelCase     ):
						if "__init__.py" in files:
									a__     = os.path.join(__lowerCamelCase					,  "__init__.py"     )
									a__     = parse_init(__lowerCamelCase     )
									if objects is not None:
												a__     = analyze_results(*__lowerCamelCase     )
												if len(__lowerCamelCase     ) > 0:
															a__     = 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    _lowerCamelCase  ()   ->    List[str]:
			a__     = []
			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
									a__     = str((Path(__lowerCamelCase     ) / folder).relative_to(__lowerCamelCase     )     )
									a__     = short_path.replace(os.path.sep					,  "."     )
									submodules.append(__lowerCamelCase     )
						for fname in files:
									if fname == "__init__.py":
												continue
									a__     = str((Path(__lowerCamelCase     ) / fname).relative_to(__lowerCamelCase     )     )
									a__     = short_path.replace(".py"					,  ""     ).replace(os.path.sep					,  "."     )
									if len(submodule.split("."     )     ) == 1:
												submodules.append(__lowerCamelCase     )
			return submodules


lowerCAmelCase_   :   Dict       =					[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]







def    _lowerCamelCase  ()   ->    Tuple:
			# This is to make sure the transformers module imported is the one in the repo.
			a__     = importlib.util.spec_from_file_location(
			    "transformers"					,  os.path.join(__lowerCamelCase					,  "__init__.py"     )					,  submodule_search_locations=[PATH_TO_TRANSFORMERS]					,  )
			a__     = spec.loader.load_module()

			a__     = [
			    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:
						a__     = "\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


lowerCAmelCase_   :   Union[str, Any]       =					logging.get_logger(__name__)







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]					,  __lowerCamelCase  :     Union[str, Any]=False     )   ->    Dict:
			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:
						a__     = os.path.abspath(__lowerCamelCase     )
						logger.info(f'''Loading PyTorch weights from {pt_path}'''     )

						a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
						logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()     ):,} parameters.'''     )

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







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple[str]					,  __lowerCamelCase  :     np.ndarray					,  __lowerCamelCase  :     Dict[str, jnp.ndarray]					,  __lowerCamelCase  :     str					,  )   ->    (Tuple[str], np.ndarray):

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

			# layer norm
			a__     = 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
			a__     = 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
			a__     = 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
			a__     = 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
			a__     = 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     ):
						a__     = pt_tensor.transpose(2					,  3					,  1					,  0     )
						return renamed_pt_tuple_key, pt_tensor

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

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

			# old PyTorch layer norm bias
			a__     = 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
			a__     = None
			if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
						a__     = pt_tuple_key[-2] + "_g"
			elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
						a__     = pt_tuple_key[-2] + "_v"
			if name is not None:
						a__     = pt_tuple_key[:-3] + (name,)
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def    _lowerCamelCase  (__lowerCamelCase  :     int					,  __lowerCamelCase  :     List[Any]     )   ->    Any:
			# convert pytorch tensor to numpy
			a__     = {k: v.numpy() for k, v in pt_state_dict.items()}

			a__     = flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						a__     = flax_model.params["params"]
			else:
						a__     = flax_model.params
			a__     = flatten_dict(__lowerCamelCase     )

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

			a__     = {}

			a__     = (model_prefix not in flax_model_params) and (
			    model_prefix in {k.split("."     )[0] for k in pt_state_dict.keys()}
			)
			a__     = (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():
						a__     = tuple(pt_key.split("."     )     )

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

						# Correctly rename weight parameters
						a__   ,				a__     = rename_key_and_reshape_tensor(
						    __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

						# add model prefix if necessary
						a__     = (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									a__     = (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]:
												a__     = 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
									a__     = jnp.asarray(__lowerCamelCase     )

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

			return unflatten_dict(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Any     )   ->    Union[str, Any]:
			import torch

			# Load the index
			a__     = {}
			for shard_file in shard_filenames:
						# load using msgpack utils
						a__     = torch.load(__lowerCamelCase     )
						a__     = {k: v.numpy() for k, v in pt_state_dict.items()}

						a__     = 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:
									a__     = flax_model.params["params"]

									a__     = flatten_dict(__lowerCamelCase     )
									random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]     )     )
						else:
									a__     = flax_model.params
									a__     = flatten_dict(__lowerCamelCase     )

						a__     = (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("."     )[0] for k in pt_state_dict.keys()}
						)
						a__     = (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():
									a__     = tuple(pt_key.split("."     )     )

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

									# Correctly rename weight parameters
									a__   ,				a__     = rename_key_and_reshape_tensor(
									    __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
									# add model prefix if necessary
									a__     = (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												a__     = (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]:
															a__     = jnp.asarray(__lowerCamelCase     )
															continue
												if "var" in flax_key[-1]:
															a__     = 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
												a__     = jnp.asarray(__lowerCamelCase     )

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







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple     )   ->    Any:
			a__     = os.path.abspath(__lowerCamelCase     )
			logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''     )

			# import correct flax class
			a__     = getattr(__lowerCamelCase					,  "Flax" + model.__class__.__name__     )

			# load flax weight dict
			with open(__lowerCamelCase					,  "rb"     ) as state_f:
						try:
									a__     = 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    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     Tuple     )   ->    Dict:

			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
			a__     = 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."     )
						a__     = jax.tree_util.tree_map(
						    lambda __lowerCamelCase     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params					,  __lowerCamelCase     )

			a__     = flatten_dict(__lowerCamelCase     )
			a__     = pt_model.state_dict()

			a__     = (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()}
			)
			a__     = (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
			a__     = []
			a__     = set(pt_model_dict.keys()     )

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

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

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

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

						if flax_key in special_pt_names:
									a__     = 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
												a__     = np.asarray(__lowerCamelCase     ) if not isinstance(__lowerCamelCase					,  np.ndarray     ) else flax_tensor
												a__     = 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
			a__     = 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



