





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


UpperCAmelCase_					:   Dict 							=						logging.get_logger(__name__)






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


     try:
          with open(_a					,   """rb"""			) as flax_state_f:
               lowerCAmelCase_					:       Dict    =		from_bytes(_a					,   flax_state_f.read()			)
     except UnpicklingError as e:
          try:
               with open(_a			) 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(_a					,   _a			)







def 							_lowerCAmelCase (  _a   :					str					,   _a   :					Union[str, Any]			)	->			Optional[Any]:


     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
     lowerCAmelCase_					:       str    =		flatten_dict(jax.tree_util.tree_map(lambda _a			: x.dtype == jnp.bfloataa					,   _a			)			).values()
     if any(_a			):
          # 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."""			)
          lowerCAmelCase_					:       Optional[int]    =		jax.tree_util.tree_map(
              lambda _a			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params					,   _a			)

     lowerCAmelCase_					:       Optional[Any]    =		""""""

     lowerCAmelCase_					:       Dict    =		flatten_dict(_a					,   sep="""."""			)
     lowerCAmelCase_					:       Dict    =		pt_model.state_dict()

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

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          lowerCAmelCase_					:       str    =		flax_key_tuple.split("""."""			)

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

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(_a			):
                    lowerCAmelCase_					:       List[str]    =		(
                        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"""			)
                    )

          lowerCAmelCase_					:       int    =		""".""".join(_a			)

          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_					:       Optional[Any]    =		np.asarray(_a			) if not isinstance(_a					,   np.ndarray			) else flax_tensor
                    lowerCAmelCase_					:       Union[str, Any]    =		torch.from_numpy(_a			)
                    # remove from missing keys
                    missing_keys.remove(_a			)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(_a			)

     pt_model.load_state_dict(_a			)

     # re-transform missing_keys to list
     lowerCAmelCase_					:       Optional[Any]    =		list(_a			)

     if len(_a			) > 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(_a			) > 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







def 							_lowerCAmelCase (  _a   :					int					,   _a   :					bool = False			)	->			bool:
     if n == 2:
          return True
     if not n % 2 or n < 2:
          return False
     if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
          return False
     if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
          raise ValueError(
              """Warning: upper bound of deterministic test is exceeded. """
              """Pass allow_probable=True to allow probabilistic test. """
              """A return value of True indicates a probable prime."""			)
     # array bounds provided by analysis
     lowerCAmelCase_					:       Optional[int]    =		[
         20_47,
         1_37_36_53,
         25_32_60_01,
         32_15_03_17_51,
         2_15_23_02_89_87_47,
         3_47_47_49_66_03_83,
         3_41_55_00_71_72_83_21,
         1,
         3_82_51_23_05_65_46_41_30_51,
         1,
         1,
         31_86_65_85_78_34_03_11_51_16_74_61,
         3_31_70_44_06_46_79_88_73_85_96_19_81,
     ]

     lowerCAmelCase_					:       Dict    =		[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
     for idx, _p in enumerate(_a					,   1			):
          if n < _p:
               # then we have our last prime to check
               lowerCAmelCase_					:       str    =		primes[:idx]
               break
     lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		n - 1, 0
     # break up n -1 into a power of 2 (s) and
     # remaining odd component
     # essentially, solve for d * 2 ** s == n - 1
     while d % 2 == 0:
          d //= 2
          s += 1
     for prime in plist:
          lowerCAmelCase_					:       Tuple    =		False
          for r in range(_a			):
               lowerCAmelCase_					:       Tuple    =		pow(_a					,   d * 2**r					,   _a			)
               # see article for analysis explanation for m
               if (r == 0 and m == 1) or ((m + 1) % n == 0):
                    lowerCAmelCase_					:       List[Any]    =		True
                    # this loop will not determine compositeness
                    break
          if pr:
               continue
          # if pr is False, then the above loop never evaluated to true,
          # and the n MUST be composite
          return False
     return True






def 							_lowerCAmelCase (  )	->			None:
     assert not miller_rabin(5_61			)
     assert miller_rabin(5_63			)
     # 2047

     assert not miller_rabin(83_82_01			)
     assert miller_rabin(83_82_07			)
     # 1_373_653

     assert not miller_rabin(17_31_60_01			)
     assert miller_rabin(17_31_60_17			)
     # 25_326_001

     assert not miller_rabin(30_78_38_66_41			)
     assert miller_rabin(30_78_38_66_53			)
     # 3_215_031_751

     assert not miller_rabin(1_71_30_45_57_48_01			)
     assert miller_rabin(1_71_30_45_57_48_19			)
     # 2_152_302_898_747

     assert not miller_rabin(2_77_97_99_72_83_07			)
     assert miller_rabin(2_77_97_99_72_83_27			)
     # 3_474_749_660_383

     assert not miller_rabin(1_13_85_00_23_90_94_41			)
     assert miller_rabin(1_13_85_00_23_90_95_27			)
     # 341_550_071_728_321

     assert not miller_rabin(1_27_50_41_01_88_48_80_43_51			)
     assert miller_rabin(1_27_50_41_01_88_48_80_43_91			)
     # 3_825_123_056_546_413_051

     assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67			)
     assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51			)
     # 318_665_857_834_031_151_167_461

     assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33			)
     assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59			)
     # 3_317_044_064_679_887_385_961_981
     # upper limit for probabilistic test


if __name__ == "__main__":
      test_miller_rabin()
