


import copy
import re






class   __lowercase				:
      UpperCamelCase								=       '''hp'''
      UpperCamelCase								=       {}
      UpperCamelCase								=       None





      @classmethod
      def        _lowercase				(		cls						:					Optional[int]					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					List[Any]       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase						=     prefix
         UpperCAmelCase						=     defaults
         cls.build_naming_info()





      @staticmethod
      def        _lowercase				(		__lowerCamelCase						:					str					,       __lowerCamelCase						:					List[str]       )    ->   List[str]:






         """simple docstring"""







         if len(lowerCamelCase_       ) == 0:
            return ""
         UpperCAmelCase						=     None
         if any(char.isdigit() for char in word       ):
            raise Exception(F"""Parameters should not contain numbers: \'{word}\' contains a number"""       )
         if word in info["short_word"]:
            return info["short_word"][word]
         for prefix_len in range(1					,       len(lowerCamelCase_       ) + 1       ):
            UpperCAmelCase						=     word[:prefix_len]
            if prefix in info["reverse_short_word"]:
               continue
            else:
               UpperCAmelCase						=     prefix
               break

         if short_word is None:
            # Paranoid fallback
            def int_to_alphabetic(__lowerCamelCase						:					str       ):
               UpperCAmelCase						=     """"""
               while integer != 0:
                  UpperCAmelCase						=     chr(ord("""A"""       ) + integer % 1_0       ) + s
                  integer //= 1_0
               return s

            UpperCAmelCase						=     0
            while True:
               UpperCAmelCase						=     word + """#""" + int_to_alphabetic(lowerCamelCase_       )
               if sword in info["reverse_short_word"]:
                  continue
               else:
                  UpperCAmelCase						=     sword
                  break

         UpperCAmelCase						=     short_word
         UpperCAmelCase						=     word
         return short_word





      @staticmethod
      def        _lowercase				(		__lowerCamelCase						:					int					,       __lowerCamelCase						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase						=     param_name.split("""_"""       )

         UpperCAmelCase						=     [TrialShortNamer.shortname_for_word(lowerCamelCase_					,       lowerCamelCase_       ) for word in words]

         # We try to create a separatorless short name, but if there is a collision we have to fallback
         # to a separated short name
         UpperCAmelCase						=     ["""""", """_"""]

         for separator in separators:
            UpperCAmelCase						=     separator.join(lowerCamelCase_       )
            if shortname not in info["reverse_short_param"]:
               UpperCAmelCase						=     shortname
               UpperCAmelCase						=     param_name
               return shortname

         return param_name





      @staticmethod
      def        _lowercase				(		__lowerCamelCase						:					List[str]					,       __lowerCamelCase						:					Union[str, Any]       )    ->   str:






         """simple docstring"""







         UpperCAmelCase						=     TrialShortNamer.shortname_for_key(lowerCamelCase_					,       lowerCamelCase_       )
         UpperCAmelCase						=     short_name
         UpperCAmelCase						=     param_name





      @classmethod
      def        _lowercase				(		cls						:					Optional[int]       )    ->   List[Any]:






         """simple docstring"""







         if cls.NAMING_INFO is not None:
            return

         UpperCAmelCase						=     {
             """short_word""": {},
             """reverse_short_word""": {},
             """short_param""": {},
             """reverse_short_param""": {},
         }

         UpperCAmelCase						=     list(cls.DEFAULTS.keys()       )

         for k in field_keys:
            cls.add_new_param_name(lowerCamelCase_					,       lowerCamelCase_       )

         UpperCAmelCase						=     info





      @classmethod
      def        _lowercase				(		cls						:					Dict					,       __lowerCamelCase						:					int       )    ->   Union[str, Any]:






         """simple docstring"""







         cls.build_naming_info()
         assert cls.PREFIX is not None
         UpperCAmelCase						=     [copy.copy(cls.PREFIX       )]

         for k, v in params.items():
            if k not in cls.DEFAULTS:
               raise Exception(F"""You should provide a default value for the param name {k} with value {v}"""       )
            if v == cls.DEFAULTS[k]:
               # The default value is not added to the name
               continue

            UpperCAmelCase						=     cls.NAMING_INFO["""short_param"""][k]

            if isinstance(lowerCamelCase_					,       lowerCamelCase_       ):
               UpperCAmelCase						=     1 if v else 0

            UpperCAmelCase						=     """""" if isinstance(lowerCamelCase_					,       (int, float)       ) else """-"""
            UpperCAmelCase						=     F"""{key}{sep}{v}"""
            name.append(lowerCamelCase_       )

         return "_".join(lowerCamelCase_       )





      @classmethod
      def        _lowercase				(		cls						:					Optional[int]					,       __lowerCamelCase						:					Any       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase						=     repr[len(cls.PREFIX       ) + 1 :]
         if repr == "":
            UpperCAmelCase						=     []
         else:
            UpperCAmelCase						=     repr.split("""_"""       )

         UpperCAmelCase						=     {}

         for value in values:
            if "-" in value:
               UpperCAmelCase						=     value.split("""-"""       )
            else:
               UpperCAmelCase						=     re.sub("""[0-9.]"""					,       """"""					,       lowerCamelCase_       )
               UpperCAmelCase						=     float(re.sub("""[^0-9.]"""					,       """"""					,       lowerCamelCase_       )       )

            UpperCAmelCase						=     cls.NAMING_INFO["""reverse_short_param"""][p_k]

            UpperCAmelCase						=     p_v

         for k in cls.DEFAULTS:
            if k not in parameters:
               UpperCAmelCase						=     cls.DEFAULTS[k]

         return parameters







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


__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   try:
      with open(lowerCAmelCase_  ,						"""rb"""   ) as flax_state_f:
         UpperCAmelCase							=     from_bytes(lowerCAmelCase_  ,						flax_state_f.read()   )
   except UnpicklingError as e:
      try:
         with open(lowerCAmelCase_   ) 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(lowerCAmelCase_  ,						lowerCAmelCase_   )







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:

   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
   UpperCAmelCase							=     flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_   : x.dtype == jnp.bfloataa  ,						lowerCAmelCase_   )   ).values()
   if any(lowerCAmelCase_   ):
      # 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."""   )
      UpperCAmelCase							=     jax.tree_util.tree_map(
          lambda lowerCAmelCase_   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     """"""

   UpperCAmelCase							=     flatten_dict(lowerCAmelCase_  ,						sep="""."""   )
   UpperCAmelCase							=     pt_model.state_dict()

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

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

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     jnp.transpose(lowerCAmelCase_  ,						(3, 2, 0, 1)   )
      elif flax_key_tuple_array[-1] == "kernel":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(lowerCAmelCase_   ):
            UpperCAmelCase							=     (
                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"""   )
            )

      UpperCAmelCase							=     """.""".join(lowerCAmelCase_   )

      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
            UpperCAmelCase							=     np.asarray(lowerCAmelCase_   ) if not isinstance(lowerCAmelCase_  ,						np.ndarray   ) else flax_tensor
            UpperCAmelCase							=     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
   UpperCAmelCase							=     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)."""   )
   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."""   )

   return pt_model



