





'''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


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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  try:
    with open(SCREAMING_SNAKE_CASE__					,	"rb"       ) as flax_state_f:
      _snake_case     		=				from_bytes(SCREAMING_SNAKE_CASE__					,	flax_state_f.read()       )
  except UnpicklingError as e:
    try:
      with open(SCREAMING_SNAKE_CASE__       ) 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(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''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
  _snake_case     		=				flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__       : x.dtype == jnp.bfloataa					,	SCREAMING_SNAKE_CASE__       )       ).values()
  if any(SCREAMING_SNAKE_CASE__       ):
    # 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."       )
    _snake_case     		=				jax.tree_util.tree_map(
        lambda SCREAMING_SNAKE_CASE__       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params					,	SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				""

  _snake_case     		=				flatten_dict(SCREAMING_SNAKE_CASE__					,	sep="."       )
  _snake_case     		=				pt_model.state_dict()

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

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

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

    _snake_case     		=				".".join(SCREAMING_SNAKE_CASE__       )

    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
        _snake_case     		=				np.asarray(SCREAMING_SNAKE_CASE__       ) if not isinstance(SCREAMING_SNAKE_CASE__					,	np.ndarray       ) else flax_tensor
        _snake_case     		=				torch.from_numpy(SCREAMING_SNAKE_CASE__       )
        # remove from missing keys
        missing_keys.remove(SCREAMING_SNAKE_CASE__       )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(SCREAMING_SNAKE_CASE__       )

  pt_model.load_state_dict(SCREAMING_SNAKE_CASE__       )

  # re-transform missing_keys to list
  _snake_case     		=				list(SCREAMING_SNAKE_CASE__       )

  if len(SCREAMING_SNAKE_CASE__       ) > 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(SCREAMING_SNAKE_CASE__       ) > 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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[0 for i in range(len(SCREAMING_SNAKE_CASE__       )       )]

  # initialize interval's left pointer and right pointer
  _snake_case		,		_snake_case     		=				0, 0

  for i in range(1					,	len(SCREAMING_SNAKE_CASE__       )       ):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case     		=				min(right_pointer - i + 1					,	z_result[i - left_pointer]       )
      _snake_case     		=				min_edge

    while go_next(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case		,		_snake_case     		=				i, i + z_result[i] - 1

  return z_result




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return i + z_result[i] < len(SCREAMING_SNAKE_CASE__       ) and s[z_result[i]] == s[i + z_result[i]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case     		=				z_function(pattern + input_str       )

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(SCREAMING_SNAKE_CASE__       ):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


