



'''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


lowerCAmelCase: Tuple							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A			,      _A    ):
  try:
    with open(_A			,      'rb'    ) as flax_state_f:
      a						: List[str]					       =					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 				lowerCamelCase__	(						_A			,      _A    ):

  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
  a						: Tuple					       =					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.'    )
    a						: int					       =					jax.tree_util.tree_map(
        lambda _A    : params.astype(np.floataa    ) if params.dtype == jnp.bfloataa else params			,      _A    )

  a						: str					       =					''

  a						: Optional[int]					       =					flatten_dict(_A			,      sep='.'    )
  a						: Optional[Any]					       =					pt_model.state_dict()

  # keep track of unexpected & missing keys
  a						: int					       =					[]
  a						: Dict					       =					set(pt_model_dict.keys()    )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    a						: Union[str, Any]					       =					flax_key_tuple.split('.'    )

    if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
      a						: List[Any]					       =					flax_key_tuple_array[:-1] + ['weight']
      a						: Optional[int]					       =					jnp.transpose(_A			,      (3, 2, 0, 1)    )
    elif flax_key_tuple_array[-1] == "kernel":
      a						: str					       =					flax_key_tuple_array[:-1] + ['weight']
      a						: int					       =					flax_tensor.T
    elif flax_key_tuple_array[-1] == "scale":
      a						: List[str]					       =					flax_key_tuple_array[:-1] + ['weight']

    if "time_embedding" not in flax_key_tuple_array:
      for i, flax_key_tuple_string in enumerate(_A    ):
        a						: Dict					       =					(
            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'    )
        )

    a						: List[Any]					       =					'.'.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
        a						: List[Any]					       =					np.asarray(_A    ) if not isinstance(_A			,      np.ndarray    ) else flax_tensor
        a						: Union[str, Any]					       =					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
  a						: Any					       =					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




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()