
"""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


lowercase_							:    List[Any]         =						logging.get_logger(__name__)





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


  try:
    with open(a__,    "rb"					) as flax_state_f:
      _SCREAMING_SNAKE_CASE :					List[str]				     =			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     (  lowerCamelCase__				:				Optional[Any],    lowerCamelCase__				:				Optional[int]					)	->     str:


  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 :					int				     =			flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase__					: 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 :					List[str]				     =			jax.tree_util.tree_map(
        lambda lowerCamelCase__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params,    a__					)

  _SCREAMING_SNAKE_CASE :					Any				     =			""

  _SCREAMING_SNAKE_CASE :					Any				     =			flatten_dict(a__,    sep="."					)
  _SCREAMING_SNAKE_CASE :					int				     =			pt_model.state_dict()

  # keep track of unexpected & missing keys
  _SCREAMING_SNAKE_CASE :					str				     =			[]
  _SCREAMING_SNAKE_CASE :					Optional[int]				     =			set(pt_model_dict.keys()					)

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

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

    _SCREAMING_SNAKE_CASE :					Union[str, Any]				     =			".".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 :					str				     =			np.asarray(a__					) if not isinstance(a__,    np.ndarray					) else flax_tensor
        _SCREAMING_SNAKE_CASE :					Tuple				     =			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 :					Optional[int]				     =			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'''



from math import isqrt







def   a__			(    a__		):




     """simple docstring"""


     return all(number % divisor != 0 for divisor in range(2 ,       isqrt(a__		) + 1		)		)







def   a__			(    a__ = 10**6		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = 1
     __SCREAMING_SNAKE_CASE      = 7
     while prime_candidate < max_prime:
          primes_count += is_prime(a__		)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


if __name__ == "__main__":
  print(f"""{solution() = }""")






