




"""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


lowercase__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__		):
				try:
								with open(lowercase__							,    'rb'		) as flax_state_f:
												_lowerCamelCase     :			Union[str, Any]    =    from_bytes(lowercase__							,    flax_state_f.read()		)
				except UnpicklingError as e:
								try:
												with open(lowercase__		) 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(lowercase__							,    lowercase__		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				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
				_lowerCamelCase     :			Union[str, Any]    =    flatten_dict(jax.tree_util.tree_map(lambda lowercase__		: x.dtype == jnp.bfloataa							,    lowercase__		)		).values()
				if any(lowercase__		):
								# 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.'		)
								_lowerCamelCase     :			Any    =    jax.tree_util.tree_map(
								    lambda lowercase__		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params							,    lowercase__		)

				_lowerCamelCase     :			Tuple    =    ''

				_lowerCamelCase     :			Union[str, Any]    =    flatten_dict(lowercase__							,    sep='.'		)
				_lowerCamelCase     :			List[Any]    =    pt_model.state_dict()

				# keep track of unexpected & missing keys
				_lowerCamelCase     :			str    =    []
				_lowerCamelCase     :			List[str]    =    set(pt_model_dict.keys()		)

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

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												_lowerCamelCase     :			str    =    flax_key_tuple_array[:-1] + ['weight']
												_lowerCamelCase     :			Optional[Any]    =    jnp.transpose(lowercase__							,    (3, 2, 0, 1)		)
								elif flax_key_tuple_array[-1] == "kernel":
												_lowerCamelCase     :			List[Any]    =    flax_key_tuple_array[:-1] + ['weight']
												_lowerCamelCase     :			Tuple    =    flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												_lowerCamelCase     :			str    =    flax_key_tuple_array[:-1] + ['weight']

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(lowercase__		):
																_lowerCamelCase     :			List[str]    =    (
																    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'		)
																)

								_lowerCamelCase     :			int    =    '.'.join(lowercase__		)

								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
																_lowerCamelCase     :			str    =    np.asarray(lowercase__		) if not isinstance(lowercase__							,    np.ndarray		) else flax_tensor
																_lowerCamelCase     :			List[str]    =    torch.from_numpy(lowercase__		)
																# remove from missing keys
																missing_keys.remove(lowercase__		)
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(lowercase__		)

				pt_model.load_state_dict(lowercase__		)

				# re-transform missing_keys to list
				_lowerCamelCase     :			Dict    =    list(lowercase__		)

				if len(lowercase__		) > 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(lowercase__		) > 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      _snake_case			(				lowercase__		):
				stooge(lowercase__							,    0							,    len(lowercase__		) - 1		)
				return arr







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				if i >= h:
								return

				# If first element is smaller than the last then swap them
				if arr[i] > arr[h]:
								_lowerCamelCase, _lowerCamelCase     :			Optional[Any]    =    arr[h], arr[i]

				# If there are more than 2 elements in the array
				if h - i + 1 > 2:
								_lowerCamelCase     :			Union[str, Any]    =    (int)((h - i + 1) / 3		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)

								# Recursively sort last 2/3 elements
								stooge(lowercase__							,    i + t							,    (lowercase__)		)

								# Recursively sort first 2/3 elements
								stooge(lowercase__							,    lowercase__							,    (h - t)		)


if __name__ == "__main__":
			lowercase__     						=						input("""Enter numbers separated by a comma:\n""").strip()
			lowercase__     						=						[int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))