





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


__A									=				logging.get_logger(__name__)





def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =R"\w+[.]\d+"
  lowerCamelCase__:      int			  =re.findall(__a	,    __a		)
  for pat in pats:
    lowerCamelCase__:      Optional[int]			  =key.replace(__a	,    "_".join(pat.split("."		)		)		)
  return key





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      int			  =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)
  ):
    lowerCamelCase__:      Optional[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:
    lowerCamelCase__:      int			  =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:
    lowerCamelCase__:      int			  =pt_tuple_key[:-1] + ("embedding",)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  lowerCamelCase__:      Optional[Any]			  =pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    lowerCamelCase__:      List[Any]			  =pt_tensor.transpose(2	,    3	,    1	,    0		)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  lowerCamelCase__:      Union[str, Any]			  =pt_tuple_key[:-1] + ("kernel",)
  if pt_tuple_key[-1] == "weight":
    lowerCamelCase__:      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__:      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      lowerCAmelCase_ (       __a	,    __a	,    __a=42		)     ->		Tuple:







  """simple docstring"""





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

  # Step 2: Since the model is stateless, get random Flax params
  lowerCamelCase__:      int			  =flax_model.init_weights(PRNGKey(__a		)		)

  lowerCamelCase__:      Optional[int]			  =flatten_dict(__a		)
  lowerCamelCase__:      int			  ={}

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

    # Correctly rename weight parameters
    lowerCamelCase__  ,					lowerCamelCase__:      List[str]			  =rename_key_and_reshape_tensor(__a	,    __a	,    __a		)

    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
    lowerCamelCase__:      Union[str, Any]			  =jnp.asarray(__a		)

  return unflatten_dict(__a		)









"""simple docstring"""
lowerCamelCase_					=      [
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[str]			     =					{"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
			UpperCAmelCase_     : Optional[Any]			     =					0
			UpperCAmelCase_     : Tuple			     =					0
			while place < len(A__						):
						if (place + 1 < len(A__						)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					[]
			for arabic, roman in ROMAN:
						((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : str			     =					divmod(A__	,A__						)
						result.append(roman * factor						)
						if number == 0:
									break
			return "".join(A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



