
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 :			int              =	logging.get_logger(__name__)


def 				A	( lowercase   ,	lowercase			)       ->  List[Any]:
		'''simple docstring'''

		try:
				with open(lowercase   ,	'rb'			) as flax_state_f:
						UpperCamelCase          =					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 				A	( lowercase   ,	lowercase			)       ->  Dict:
		'''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
		UpperCamelCase          =					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.'			)
				UpperCamelCase          =					jax.tree_util.tree_map(
				    lambda lowercase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params   ,	lowercase			)

		UpperCamelCase          =					''

		UpperCamelCase          =					flatten_dict(lowercase   ,	sep='.'			)
		UpperCamelCase          =					pt_model.state_dict()

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

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

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

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

				UpperCamelCase          =					'.'.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
								UpperCamelCase          =					np.asarray(lowercase			) if not isinstance(lowercase   ,	np.ndarray			) else flax_tensor
								UpperCamelCase          =					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
		UpperCamelCase          =					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


from __future__ import annotations


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

		UpperCamelCase          =					[]
		UpperCamelCase     ,  UpperCamelCase          =					input_list[low:mid], input_list[mid : high + 1]
		while left and right:
				result.append((left if left[0] <= right[0] else right).pop(0			)			)
		UpperCamelCase          =					result + left + right
		return input_list


def 				A	( lowercase			)       ->  list:
		'''simple docstring'''

		if len(lowercase			) <= 1:
				return input_list
		UpperCamelCase          =					list(lowercase			)

		# iteration for two-way merging
		UpperCamelCase          =					2
		while p <= len(lowercase			):
				# getting low, high and middle value for merge-sort of single list
				for i in range(0   ,	len(lowercase			)   ,	lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					i + p - 1
						UpperCamelCase          =					(low + high + 1) // 2
						UpperCamelCase          =					merge(lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)
				# final merge of last two parts
				if p * 2 >= len(lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					merge(lowercase   ,	0   ,	lowercase   ,	len(lowercase			) - 1			)
						break
				p *= 2

		return input_list


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