






"""simple docstring"""
from pickle import UnpicklingError

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

from ..utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	try:
		with open(snake_case__ , """rb"""       ) as flax_state_f:
			A_		:    Dict						     = from_bytes(snake_case__ , flax_state_f.read()       )
	except UnpicklingError as e:
		try:
			with open(snake_case__       ) as f:
				if f.read().startswith("""version"""       ):
					raise OSError(
					    """You seem to have cloned a repository without having git-lfs installed. Please"""
					    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
					    """ folder you cloned."""       )
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """       )

	return load_flax_weights_in_pytorch_model(snake_case__ , snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    """Loading 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_		:    Tuple						     = 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 they 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_		:    Tuple						     = jax.tree_util.tree_map(
		    lambda snake_case__       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params , snake_case__       )

	A_		:    Optional[Any]						     = """"""

	A_		:    List[str]						     = flatten_dict(snake_case__ , sep="""."""       )
	A_		:    Tuple						     = pt_model.state_dict()

	# keep track of unexpected & missing keys
	A_		:    List[Any]						     = []
	A_		:    List[Any]						     = set(pt_model_dict.keys()       )

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		A_		:    List[Any]						     = flax_key_tuple.split("""."""       )

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			A_		:    Any						     = flax_key_tuple_array[:-1] + ["""weight"""]
			A_		:    Union[str, Any]						     = jnp.transpose(snake_case__ , (3, 2, 0, 1)       )
		elif flax_key_tuple_array[-1] == "kernel":
			A_		:    List[str]						     = flax_key_tuple_array[:-1] + ["""weight"""]
			A_		:    Optional[int]						     = flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			A_		:    List[str]						     = flax_key_tuple_array[:-1] + ["""weight"""]

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(snake_case__       ):
				A_		:    List[Any]						     = (
				    flax_key_tuple_string.replace("""_0""" , """.0"""       )
				    .replace("""_1""" , """.1"""       )
				    .replace("""_2""" , """.2"""       )
				    .replace("""_3""" , """.3"""       )
				    .replace("""_4""" , """.4"""       )
				    .replace("""_5""" , """.5"""       )
				    .replace("""_6""" , """.6"""       )
				    .replace("""_7""" , """.7"""       )
				    .replace("""_8""" , """.8"""       )
				    .replace("""_9""" , """.9"""       )
				)

		A_		:    str						     = """.""".join(snake_case__       )

		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_		:    Any						     = np.asarray(snake_case__       ) if not isinstance(snake_case__ , np.ndarray       ) else flax_tensor
				A_		:    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
	A_		:    List[Any]						     = 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)."""       )
	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."""       )

	return pt_model









"""simple docstring"""
from __future__ import annotations






def       __UpperCamelCase						(		snake_case__ , snake_case__ = None , snake_case__ = None       ):
	if start is None:
		A_		:    Dict						     = 0

	if end is None:
		A_		:    Dict						     = len(snake_case__       ) - 1

	if start >= end:
		return

	A_		:    List[Any]						     = (start + end) // 2

	slowsort(snake_case__ , snake_case__ , snake_case__       )
	slowsort(snake_case__ , mid + 1 , snake_case__       )

	if sequence[end] < sequence[mid]:
		A_     ,  A_		:    Dict						     = sequence[mid], sequence[end]

	slowsort(snake_case__ , snake_case__ , end - 1       )


if __name__ == "__main__":
							from doctest import testmod

							testmod()

