"""simple docstring"""




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_a   :	int 					=     logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: int=False			) ->   Optional[int]:
  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        """Loading a PyTorch model in Flax, 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

  if not is_sharded:
    _lowerCAmelCase		: List[Any]						    =  os.path.abspath(_lowerCamelCase			)
    logger.info(f"Loading PyTorch weights from {pt_path}"			)

    _lowerCAmelCase		: Dict						    =  torch.load(_lowerCamelCase    ,map_location="""cpu"""			)
    logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()			):,} parameters."			)

    _lowerCAmelCase		: int						    =  convert_pytorch_state_dict_to_flax(_lowerCamelCase    ,_lowerCamelCase			)
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    _lowerCAmelCase		: Union[str, Any]						    =  convert_pytorch_sharded_state_dict_to_flax(_lowerCamelCase    ,_lowerCamelCase			)
  return flax_state_dict





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple[str]    ,_lowerCamelCase		: np.ndarray    ,_lowerCamelCase		: Dict[str, jnp.ndarray]    ,_lowerCamelCase		: str    ,) ->   (Tuple[str], np.ndarray):

  def is_key_or_prefix_key_in_dict(_lowerCamelCase		: Tuple[str]			) -> bool:
    return len(set(_lowerCamelCase			) & {key, (model_prefix,) + key}			) > 0

  # layer norm
  _lowerCAmelCase		: Union[str, Any]						    =  pt_tuple_key[:-1] + ("""scale""",)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCamelCase			):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  _lowerCAmelCase		: int						    =  pt_tuple_key[:-1] + ("""mean""",)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCamelCase			):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  _lowerCAmelCase		: Tuple						    =  pt_tuple_key[:-1] + ("""var""",)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCamelCase			):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  _lowerCAmelCase		: Union[str, Any]						    =  pt_tuple_key[:-1] + ("""embedding""",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCamelCase			):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _lowerCAmelCase		: int						    =  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCamelCase			):
    _lowerCAmelCase		: str						    =  pt_tensor.transpose(2    ,3    ,1    ,0			)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  _lowerCAmelCase		: List[Any]						    =  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCamelCase			):
    _lowerCAmelCase		: Optional[int]						    =  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  _lowerCAmelCase		: str						    =  pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  _lowerCAmelCase		: List[str]						    =  pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  _lowerCAmelCase		: Dict						    =  None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    _lowerCAmelCase		: Optional[int]						    =  pt_tuple_key[-2] + """_g"""
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    _lowerCAmelCase		: int						    =  pt_tuple_key[-2] + """_v"""
  if name is not None:
    _lowerCAmelCase		: Union[str, Any]						    =  pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Union[str, Any]			) ->   Dict:
  # convert pytorch tensor to numpy
  _lowerCAmelCase		: Optional[int]						    =  {k: v.numpy() for k, v in pt_state_dict.items()}

  _lowerCAmelCase		: Tuple						    =  flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    _lowerCAmelCase		: Any						    =  flax_model.params["""params"""]
  else:
    _lowerCAmelCase		: Optional[Any]						    =  flax_model.params
  _lowerCAmelCase		: Optional[int]						    =  flatten_dict(_lowerCamelCase			)

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    _lowerCAmelCase		: Union[str, Any]						    =  flatten_dict(flax_model.params["""batch_stats"""]			)
    random_flax_state_dict.update(_lowerCamelCase			)

  _lowerCAmelCase		: Any						    =  {}

  _lowerCAmelCase		: Dict						    =  (model_prefix not in flax_model_params) and (
      model_prefix in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
  )
  _lowerCAmelCase		: List[str]						    =  (model_prefix in flax_model_params) and (
      model_prefix not in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    _lowerCAmelCase		: int						    =  tuple(pt_key.split("""."""			)			)

    # remove base model prefix if necessary
    _lowerCAmelCase		: Tuple						    =  pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _lowerCAmelCase		: str						    =  pt_tuple_key[1:]

    # Correctly rename weight parameters
    _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  rename_key_and_reshape_tensor(
        _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)

    # add model prefix if necessary
    _lowerCAmelCase		: Optional[Any]						    =  (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      _lowerCAmelCase		: Optional[Any]						    =  (model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
            f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."			)

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        _lowerCAmelCase		: Optional[int]						    =  jnp.asarray(_lowerCamelCase			)
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(_lowerCamelCase    ,_lowerCamelCase			)
        continue

      # also add unexpected weight so that warning is thrown
      _lowerCAmelCase		: Dict						    =  jnp.asarray(_lowerCamelCase			)

    else:
      # also add unexpected weight so that warning is thrown
      _lowerCAmelCase		: Optional[Any]						    =  jnp.asarray(_lowerCamelCase			)

  return unflatten_dict(_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: int			) ->   List[str]:
  import torch

  # Load the index
  _lowerCAmelCase		: List[Any]						    =  {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    _lowerCAmelCase		: Tuple						    =  torch.load(_lowerCamelCase			)
    _lowerCAmelCase		: int						    =  {k: v.numpy() for k, v in pt_state_dict.items()}

    _lowerCAmelCase		: Optional[Any]						    =  flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      _lowerCAmelCase		: Tuple						    =  flax_model.params["""params"""]

      _lowerCAmelCase		: List[str]						    =  flatten_dict(_lowerCamelCase			)
      random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]			)			)
    else:
      _lowerCAmelCase		: Union[str, Any]						    =  flax_model.params
      _lowerCAmelCase		: Dict						    =  flatten_dict(_lowerCamelCase			)

    _lowerCAmelCase		: Any						    =  (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
    )
    _lowerCAmelCase		: Any						    =  (model_prefix in flax_model_params) and (
        model_prefix not in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      _lowerCAmelCase		: List[Any]						    =  tuple(pt_key.split("""."""			)			)

      # remove base model prefix if necessary
      _lowerCAmelCase		: Union[str, Any]						    =  pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        _lowerCAmelCase		: Union[str, Any]						    =  pt_tuple_key[1:]

      # Correctly rename weight parameters
      _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  rename_key_and_reshape_tensor(
          _lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
      # add model prefix if necessary
      _lowerCAmelCase		: int						    =  (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        _lowerCAmelCase		: Any						    =  (model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
              f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."			)

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          _lowerCAmelCase		: Optional[Any]						    =  jnp.asarray(_lowerCamelCase			)
          continue
        if "var" in flax_key[-1]:
          _lowerCAmelCase		: Tuple						    =  jnp.asarray(_lowerCamelCase			)
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(_lowerCamelCase    ,_lowerCamelCase			)
          continue

        # also add unexpected weight so that warning is thrown
        _lowerCAmelCase		: List[Any]						    =  jnp.asarray(_lowerCamelCase			)

      else:
        # also add unexpected weight so that warning is thrown
        _lowerCAmelCase		: Union[str, Any]						    =  jnp.asarray(_lowerCamelCase			)
  return unflatten_dict(_lowerCamelCase			)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: List[str]			) ->   Optional[Any]:
  _lowerCAmelCase		: Tuple						    =  os.path.abspath(_lowerCamelCase			)
  logger.info(f"Loading Flax weights from {flax_checkpoint_path}"			)

  # import correct flax class
  _lowerCAmelCase		: int						    =  getattr(_lowerCamelCase    ,"""Flax""" + model.__class__.__name__			)

  # load flax weight dict
  with open(_lowerCamelCase    ,"""rb"""			) as state_f:
    try:
      _lowerCAmelCase		: Dict						    =  from_bytes(_lowerCamelCase    ,state_f.read()			)
    except UnpicklingError:
      raise EnvironmentError(f"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "			)

  return load_flax_weights_in_pytorch_model(_lowerCamelCase    ,_lowerCamelCase			)






def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str			) ->   Optional[int]:

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        """Loading a 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
  _lowerCAmelCase		: List[str]						    =  flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase			: x.dtype == jnp.bfloataa    ,_lowerCamelCase			)			).values()
  if any(_lowerCamelCase			):
    # convert all weights to fp32 if the 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."""			)
    _lowerCAmelCase		: Dict						    =  jax.tree_util.tree_map(
        lambda _lowerCamelCase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params    ,_lowerCamelCase			)

  _lowerCAmelCase		: Tuple						    =  flatten_dict(_lowerCamelCase			)
  _lowerCAmelCase		: List[Any]						    =  pt_model.state_dict()

  _lowerCAmelCase		: str						    =  (pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split("""."""			)[0] for k in pt_model_dict.keys()}
  )
  _lowerCAmelCase		: int						    =  (pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split("""."""			)[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  _lowerCAmelCase		: List[Any]						    =  []
  _lowerCAmelCase		: str						    =  set(pt_model_dict.keys()			)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    _lowerCAmelCase		: int						    =  flax_key_tuple[0] == pt_model.base_model_prefix
    _lowerCAmelCase		: Union[str, Any]						    =  """.""".join((pt_model.base_model_prefix,) + flax_key_tuple			) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _lowerCAmelCase		: int						    =  flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      _lowerCAmelCase		: str						    =  (pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCamelCase			) not in pt_model_dict:
      # conv layer
      _lowerCAmelCase		: str						    =  flax_key_tuple[:-1] + ("""weight""",)
      _lowerCAmelCase		: Any						    =  jnp.transpose(_lowerCamelCase    ,(3, 2, 0, 1)			)
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase			) not in pt_model_dict:
      # linear layer
      _lowerCAmelCase		: Optional[Any]						    =  flax_key_tuple[:-1] + ("""weight""",)
      _lowerCAmelCase		: str						    =  flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      _lowerCAmelCase		: str						    =  flax_key_tuple[:-1] + ("""weight""",)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      _lowerCAmelCase		: Dict						    =  flax_key_tuple[:-1] + ("""running_mean""",)
    elif "var" in flax_key_tuple[-1]:
      _lowerCAmelCase		: Optional[Any]						    =  flax_key_tuple[:-1] + ("""running_var""",)

    if "batch_stats" in flax_state:
      _lowerCAmelCase		: Union[str, Any]						    =  """.""".join(flax_key_tuple[1:]			)  # Remove the params/batch_stats header
    else:
      _lowerCAmelCase		: Tuple						    =  """.""".join(_lowerCamelCase			)

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    _lowerCAmelCase		: List[Any]						    =  {}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      _lowerCAmelCase		: Tuple						    =  key.split("""."""			)
      _lowerCAmelCase		: List[str]						    =  None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        _lowerCAmelCase		: Optional[int]						    =  key_components[-2] + """_g"""
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        _lowerCAmelCase		: List[Any]						    =  key_components[-2] + """_v"""
      if name is not None:
        _lowerCAmelCase		: Dict						    =  key_components[:-3] + [name]
        _lowerCAmelCase		: Optional[Any]						    =  """.""".join(_lowerCamelCase			)
        _lowerCAmelCase		: Union[str, Any]						    =  key

    if flax_key in special_pt_names:
      _lowerCAmelCase		: Any						    =  special_pt_names[flax_key]

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

  pt_model.load_state_dict(_lowerCamelCase			)

  # re-transform missing_keys to list
  _lowerCAmelCase		: Dict						    =  list(_lowerCamelCase			)

  if len(_lowerCamelCase			) > 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)."""			)
  else:
    logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"			)
  if len(_lowerCamelCase			) > 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."""			)
  else:
    logger.warning(
        f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
        """If your task is similar to the task the model of the checkpoint was trained on, """
        f"you can already use {pt_model.__class__.__name__} for predictions without further training."			)

  return pt_model





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change contrast to 170
          _a   :	str 					=     change_contrast(img, 170)
          cont_img.save('image_data/lena_high_contrast.png', format='png')




