


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


UpperCAmelCase      =   logging.get_logger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      try:
            with open(SCREAMING_SNAKE_CASE , '''rb''' ) as flax_state_f:
                  snake_case_	=				from_bytes(SCREAMING_SNAKE_CASE , flax_state_f.read() )
      except UnpicklingError as e:
            try:
                  with open(SCREAMING_SNAKE_CASE ) 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(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      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
      snake_case_	=				flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE : x.dtype == jnp.bfloataa , SCREAMING_SNAKE_CASE ) ).values()
      if any(SCREAMING_SNAKE_CASE ):
            # 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.''' )
            snake_case_	=				jax.tree_util.tree_map(
                lambda SCREAMING_SNAKE_CASE : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , SCREAMING_SNAKE_CASE )

      snake_case_	=				''''''

      snake_case_	=				flatten_dict(SCREAMING_SNAKE_CASE , sep='''.''' )
      snake_case_	=				pt_model.state_dict()

      # keep track of unexpected & missing keys
      snake_case_	=				[]
      snake_case_	=				set(pt_model_dict.keys() )

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            snake_case_	=				flax_key_tuple.split('''.''' )

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  snake_case_	=				flax_key_tuple_array[:-1] + ['''weight''']
                  snake_case_	=				jnp.transpose(SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
            elif flax_key_tuple_array[-1] == "kernel":
                  snake_case_	=				flax_key_tuple_array[:-1] + ['''weight''']
                  snake_case_	=				flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  snake_case_	=				flax_key_tuple_array[:-1] + ['''weight''']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE ):
                        snake_case_	=				(
                            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''' )
                        )

            snake_case_	=				'''.'''.join(SCREAMING_SNAKE_CASE )

            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
                        snake_case_	=				np.asarray(SCREAMING_SNAKE_CASE ) if not isinstance(SCREAMING_SNAKE_CASE , np.ndarray ) else flax_tensor
                        snake_case_	=				torch.from_numpy(SCREAMING_SNAKE_CASE )
                        # remove from missing keys
                        missing_keys.remove(SCREAMING_SNAKE_CASE )
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(SCREAMING_SNAKE_CASE )

      pt_model.load_state_dict(SCREAMING_SNAKE_CASE )

      # re-transform missing_keys to list
      snake_case_	=				list(SCREAMING_SNAKE_CASE )

      if len(SCREAMING_SNAKE_CASE ) > 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(SCREAMING_SNAKE_CASE ) > 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



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  list[int]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )

      # Initialize Result
      snake_case_	=				[]

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

      return answer


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

   if (
       input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
       == "y"
   ):
      UpperCAmelCase      =   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()))
      UpperCAmelCase      =   input("""Enter the change you want to make in Indian Currency: """).strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase      =   [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase      =   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}: ''')
      UpperCAmelCase      =   find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=""" """)