





"""simple docstring"""




from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  for param in module.parameters():
    A_  :       List[str]										=  False





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       List[str]										=  'cuda' if torch.cuda.is_available() else 'cpu'
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    A_  :       List[str]										=  'mps'
  if device == "mps":
    print(
        'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
        ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
        ' with generations.'					)
  return device





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  plt.imshow(_UpperCAmelCase					)
  fig.axes.get_xaxis().set_visible(_UpperCAmelCase					)
  fig.axes.get_yaxis().set_visible(_UpperCAmelCase					)
  plt.show()





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Tuple										=  datetime.now()
  A_  :       Optional[int]										=  current_time.strftime('%H:%M:%S'					)
  return timestamp






"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

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

    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}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)