

"""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__				:Optional[int]				   =       logging.get_logger(__name__)


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  try:
    with open(UpperCamelCase__  ,			'''rb'''  ) as flax_state_f:
      __UpperCAmelCase    :						Optional[int]       =						from_bytes(UpperCamelCase__  ,			flax_state_f.read()  )
  except UnpicklingError as e:
    try:
      with open(UpperCamelCase__  ) 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(UpperCamelCase__  ,			UpperCamelCase__  )







def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Union[str, Any]:





  """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
  __UpperCAmelCase    :						int       =						flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase_  : x.dtype == jnp.bfloataa  ,			UpperCamelCase__  )  ).values()
  if any(UpperCamelCase__  ):
    # 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    :						Tuple       =						jax.tree_util.tree_map(
        lambda UpperCAmelCase_  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params  ,			UpperCamelCase__  )

  __UpperCAmelCase    :						Tuple       =						''''''

  __UpperCAmelCase    :						Optional[Any]       =						flatten_dict(UpperCamelCase__  ,			sep='''.'''  )
  __UpperCAmelCase    :						Dict       =						pt_model.state_dict()

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

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __UpperCAmelCase    :						str       =						flax_key_tuple.split('''.'''  )

    if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
      __UpperCAmelCase    :						Optional[int]       =						flax_key_tuple_array[:-1] + ['''weight''']
      __UpperCAmelCase    :						Optional[Any]       =						jnp.transpose(UpperCamelCase__  ,			(3, 2, 0, 1)  )
    elif flax_key_tuple_array[-1] == "kernel":
      __UpperCAmelCase    :						List[Any]       =						flax_key_tuple_array[:-1] + ['''weight''']
      __UpperCAmelCase    :						List[Any]       =						flax_tensor.T
    elif flax_key_tuple_array[-1] == "scale":
      __UpperCAmelCase    :						Optional[int]       =						flax_key_tuple_array[:-1] + ['''weight''']

    if "time_embedding" not in flax_key_tuple_array:
      for i, flax_key_tuple_string in enumerate(UpperCamelCase__  ):
        __UpperCAmelCase    :						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'''  )
        )

    __UpperCAmelCase    :						Dict       =						'''.'''.join(UpperCamelCase__  )

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

  pt_model.load_state_dict(UpperCamelCase__  )

  # re-transform missing_keys to list
  __UpperCAmelCase    :						int       =						list(UpperCamelCase__  )

  if len(UpperCamelCase__  ) > 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(UpperCamelCase__  ) > 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"""





def       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  for n in range(1  ,			1_00_00_00  ):
    yield n * (n + 1) // 2


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						1
  __UpperCAmelCase    :						Any       =						2
  while i * i <= n:
    __UpperCAmelCase    :						Tuple       =						0
    while n % i == 0:
      n //= i
      multiplicity += 1
    divisors_count *= multiplicity + 1
    i += 1
  if n > 1:
    divisors_count *= 2
  return divisors_count


def       lowerCamelCase_       (   )						->Optional[int]:





  """simple docstring"""


  return next(i for i in triangle_number_generator() if count_divisors(UpperCAmelCase_  ) > 5_00  )


if __name__ == "__main__":
  print(solution())