




"""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


A_      			=					logging.get_logger(__name__)


def       UpperCAmelCase__ (snake_case__							:      int					):
  """simple docstring"""

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


def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Tuple	,     snake_case__							:      Union[str, Any]					):
  """simple docstring"""

  _snake_case							:	Dict			=						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)
  ):
    _snake_case							:	List[str]			=						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:
    _snake_case							:	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:
    _snake_case							:	List[str]			=						pt_tuple_key[:-1] + ("""embedding""",)
    return renamed_pt_tuple_key, pt_tensor

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

  # linear layer
  _snake_case							:	List[str]			=						pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight":
    _snake_case							:	List[Any]			=						pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  _snake_case							:	Dict			=						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__ (snake_case__							:      Optional[int]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      List[Any]=42					):
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						{k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  _snake_case							:	Dict			=						flax_model.init_weights(PRNGKey(snake_case__					)					)

  _snake_case							:	List[Any]			=						flatten_dict(snake_case__					)
  _snake_case							:	Any			=						{}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    _snake_case							:	Optional[int]			=						rename_key(snake_case__					)
    _snake_case							:	Dict			=						tuple(renamed_pt_key.split("""."""					)					)

    # Correctly rename weight parameters
    _snake_case	,      _snake_case							:	str			=						rename_key_and_reshape_tensor(snake_case__	,     snake_case__	,     snake_case__					)

    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
    _snake_case							:	Optional[int]			=						jnp.asarray(snake_case__					)

  return unflatten_dict(snake_case__					)








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	str			=						int(snake_case__					)

  # Initialize Result
  _snake_case							:	str			=						[]

  # Traverse through all denomination
  for denomination in reversed(snake_case__					):
    # Find denominations
    while int(snake_case__					) >= int(snake_case__					):
      total_value -= int(snake_case__					)
      answer.append(snake_case__					)  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
     A_      			=       []
     A_      			=       '''0'''

     if (
         input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
         == "y"
     ):
          A_      			=       int(input('''Enter the number of denominations you want to add: ''').strip())

          for i in range(0, n):
               denominations.append(int(input(F'''Denomination {i}: ''').strip()))
          A_      			=       input('''Enter the change you want to make in Indian Currency: ''').strip()
     else:
          # All denominations of Indian Currency if user does not enter
          A_      			=       [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
          A_      			=       input('''Enter the change you want to make: ''').strip()

     if int(value) == 0 or int(value) < 0:
          print('''The total value cannot be zero or negative.''')

     else:
          print(F'''Following is minimal change for {value}: ''')
          A_      			=       find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=''' ''')


