

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


a_    :						List[str]    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  List[Any]	)		->							Dict:
 '''simple docstring'''


 try:
  with open(_UpperCamelCase		,							'rb'	) as flax_state_f:
   SCREAMING_SNAKE_CASE								=			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		:  Union[str, Any]		,							_UpperCamelCase		:  List[Any]	)		->							List[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
 SCREAMING_SNAKE_CASE								=			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.'	)
  SCREAMING_SNAKE_CASE								=			jax.tree_util.tree_map(
      lambda _UpperCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			''

 SCREAMING_SNAKE_CASE								=			flatten_dict(_UpperCamelCase		,							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(_UpperCamelCase		,							(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(_UpperCamelCase	):
    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(_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
    SCREAMING_SNAKE_CASE								=			np.asarray(_UpperCamelCase	) if not isinstance(_UpperCamelCase		,							np.ndarray	) else flax_tensor
    SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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









def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							list[str]:
 '''simple docstring'''


 return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase	) - ngram_size + 1	)]


if __name__ == "__main__":
      from doctest import testmod

      testmod()

