

'''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


UpperCAmelCase :       int			   =     logging.get_logger(__name__)







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     try:
          with open(a__      ,    """rb"""						) as flax_state_f:
               __SCREAMING_SNAKE_CASE     =      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    a__       (  a__      ,    a__						):




     """simple docstring"""


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

     __SCREAMING_SNAKE_CASE     =      """"""

     __SCREAMING_SNAKE_CASE     =      flatten_dict(a__      ,    sep="""."""						)
     __SCREAMING_SNAKE_CASE     =      pt_model.state_dict()

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

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

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

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(a__						):
                    __SCREAMING_SNAKE_CASE     =      (
                        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"""						)
                    )

          __SCREAMING_SNAKE_CASE     =      """.""".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
                    __SCREAMING_SNAKE_CASE     =      np.asarray(a__						) if not isinstance(a__      ,    np.ndarray						) else flax_tensor
                    __SCREAMING_SNAKE_CASE     =      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
     __SCREAMING_SNAKE_CASE     =      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









'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      name
      __SCREAMING_SNAKE_CASE     =      value
      __SCREAMING_SNAKE_CASE     =      weight
 def __repr__(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      return self.value
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return self.name
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return self.weight


 def       UpperCAmelCase__		(		self					:       int			)  -> Tuple:


      """simple docstring"""




      return self.value / self.weight







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     for i in range(len(a__						)						):
          menu.append(Things(name[i]      ,    value[i]      ,    weight[i]						)						)
     return menu







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      sorted(a__      ,    key=a__      ,    reverse=a__						)
     __SCREAMING_SNAKE_CASE     =      []
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      0.0, 0.0
     for i in range(len(a__						)						):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]						)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)







def    a__       (  ):




     """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()






