
'''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


A__   :		str 		=logging.get_logger(__name__)







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       Dict:





  """simple docstring"""

  try:
    with open(__SCREAMING_SNAKE_CASE			,     """rb""" ) as flax_state_f:
      __A		:     List[str]												=		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        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       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
  __A		:     Tuple												=		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.""" )
    __A		:     Optional[Any]												=		jax.tree_util.tree_map(
        lambda __SCREAMING_SNAKE_CASE : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params			,     __SCREAMING_SNAKE_CASE )

  __A		:     List[str]												=		""""""

  __A		:     List[str]												=		flatten_dict(__SCREAMING_SNAKE_CASE			,     sep=""".""" )
  __A		:     Dict												=		pt_model.state_dict()

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

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    __A		:     str												=		flax_key_tuple.split(""".""" )

    if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
      __A		:     Tuple												=		flax_key_tuple_array[:-1] + ["""weight"""]
      __A		:     List[str]												=		jnp.transpose(__SCREAMING_SNAKE_CASE			,     (3, 2, 0, 1) )
    elif flax_key_tuple_array[-1] == "kernel":
      __A		:     int												=		flax_key_tuple_array[:-1] + ["""weight"""]
      __A		:     int												=		flax_tensor.T
    elif flax_key_tuple_array[-1] == "scale":
      __A		:     Dict												=		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 ):
        __A		:     Any												=		(
            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		:     Optional[int]												=		""".""".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
        __A		:     Dict												=		np.asarray(__SCREAMING_SNAKE_CASE ) if not isinstance(__SCREAMING_SNAKE_CASE			,     np.ndarray ) else flax_tensor
        __A		:     int												=		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
  __A		:     List[Any]												=		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





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # Print left spaces
    for _ in range(num_rows - row_idx - 1 ):
      print(end=""" """ )
    # Print row values
    for col_idx in range(row_idx + 1 ):
      if col_idx != row_idx:
        print(triangle[row_idx][col_idx]			,     end=""" """ )
      else:
        print(triangle[row_idx][col_idx]			,     end="""""" )
    print()







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    raise TypeError("""The input value of 'num_rows' should be 'int'""" )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0""" )

  __A		:     list[list[int]]												=		[]
  for current_row_idx in range(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    raise TypeError("""The input value of 'num_rows' should be 'int'""" )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0""" )

  __A		:     list[list[int]]												=		[[1]]

  for row_index in range(1			,     __SCREAMING_SNAKE_CASE ):
    __A		:     Optional[Any]												=		[0] + result[-1] + [0]
    __A		:     Optional[Any]												=		row_index + 1
    # Calculate the number of distinct elements in a row
    __A		:     int												=		sum(divmod(__SCREAMING_SNAKE_CASE			,     2 ) )
    __A		:     str												=		[
        temp_row[i - 1] + temp_row[i] for i in range(1			,     distinct_elements + 1 )
    ]
    __A		:     Tuple												=		row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    __A		:     Any												=		row_first_half + row_second_half
    result.append(__SCREAMING_SNAKE_CASE )

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		timeit(F"__main__.{call}"			,     setup="""import __main__""" )
    # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
    print(F"{call:38} -- {timing:.4f} seconds" )

  for value in range(15 ):  # (1, 7, 14):
    for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
      benchmark_a_function(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



