



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     				=						logging.get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							int:






  try:
    with open(a_			,     "rb" ) as flax_state_f:
      UpperCAmelCase_						=				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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:






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

  UpperCAmelCase_						=				''''''

  UpperCAmelCase_						=				flatten_dict(a_			,     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(a_			,     (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(a_ ):
        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(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
        UpperCAmelCase_						=				np.asarray(a_ ) if not isinstance(a_			,     np.ndarray ) else flax_tensor
        UpperCAmelCase_						=				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
  UpperCAmelCase_						=				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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						res += a

				a += a
				b >>= 1

		return res







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						UpperCAmelCase_						=				((res % c) + (a % c)) % c

				a += a
				b >>= 1

		return res

