

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


_SCREAMING_SNAKE_CASE					:  str             =      logging.get_logger(__name__)
def     UpperCAmelCase_       (						_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     UpperCAmelCase_       (						_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





def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     if divisor % 5 == 0 or divisor % 2 == 0:
          return 0
     SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   1
     while repunit:
          SCREAMING_SNAKE_CASE__   =   (10 * repunit + 1) % divisor
          repunit_index += 1
     return repunit_index
def     UpperCAmelCase_       (						_A = 1_00_00_00    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   limit - 1
     if divisor % 2 == 0:
          divisor += 1
     while least_divisible_repunit(_A    ) <= limit:
          divisor += 2
     return divisor


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


