import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__SCREAMING_SNAKE_CASE						:   List[Any]         =			logging.get_logger(__name__)
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase=False						)	->	Optional[int]:

						'''simple docstring'''



						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    "Loading a PyTorch model in Flax, 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

						if not is_sharded:
												_snake_case						:					List[str]    =   os.path.abspath(__lowercase						)
												logger.info(F"""Loading PyTorch weights from {pt_path}"""						)

												_snake_case						:					int    =   torch.load(__lowercase ,      map_location="cpu"						)
												logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters."""						)

												_snake_case						:					str    =   convert_pytorch_state_dict_to_flax(__lowercase ,      __lowercase						)
						else:
												# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
												_snake_case						:					List[str]    =   convert_pytorch_sharded_state_dict_to_flax(__lowercase ,      __lowercase						)
						return flax_state_dict
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      )	->	(Tuple[str], np.ndarray):

						'''simple docstring'''



						def is_key_or_prefix_key_in_dict(__lowercase						) -> bool:
												return len(set(__lowercase						) & {key, (model_prefix,) + key}						) > 0

						# layer norm
						_snake_case						:					Optional[Any]    =   pt_tuple_key[:-1] + ("scale",)
						if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowercase						):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer mean
						_snake_case						:					Optional[Any]    =   pt_tuple_key[:-1] + ("mean",)
						if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowercase						):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer var
						_snake_case						:					Optional[Any]    =   pt_tuple_key[:-1] + ("var",)
						if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowercase						):
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						_snake_case						:					str    =   pt_tuple_key[:-1] + ("embedding",)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowercase						):
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						_snake_case						:					List[Any]    =   pt_tuple_key[:-1] + ("kernel",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowercase						):
												_snake_case						:					List[str]    =   pt_tensor.transpose(2 ,      3 ,      1 ,      0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						_snake_case						:					int    =   pt_tuple_key[:-1] + ("kernel",)
						if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowercase						):
												_snake_case						:					Optional[int]    =   pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

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

						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						_snake_case						:					List[str]    =   None
						if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
												_snake_case						:					int    =   pt_tuple_key[-2] + "_g"
						elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
												_snake_case						:					str    =   pt_tuple_key[-2] + "_v"
						if name is not None:
												_snake_case						:					Any    =   pt_tuple_key[:-3] + (name,)
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						_snake_case						:					int    =   {k: v.numpy() for k, v in pt_state_dict.items()}

						_snake_case						:					Optional[Any]    =   flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												_snake_case						:					List[Any]    =   flax_model.params["params"]
						else:
												_snake_case						:					Optional[Any]    =   flax_model.params
						_snake_case						:					int    =   flatten_dict(__lowercase						)

						# add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
												_snake_case						:					Optional[Any]    =   flatten_dict(flax_model.params["batch_stats"]						)
												random_flax_state_dict.update(__lowercase						)

						_snake_case						:					List[Any]    =   {}

						_snake_case						:					str    =   (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("."						)[0] for k in pt_state_dict.keys()}
						)
						_snake_case						:					Optional[Any]    =   (model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("."						)[0] for k in pt_state_dict.keys()}
						)

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												_snake_case						:					str    =   tuple(pt_key.split("."						)						)

												# remove base model prefix if necessary
												_snake_case						:					List[str]    =   pt_tuple_key[0] == model_prefix
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		_snake_case						:					List[str]    =   pt_tuple_key[1:]

												# Correctly rename weight parameters
												_snake_case    ,_snake_case						:					Any    =   rename_key_and_reshape_tensor(
												    __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)

												# add model prefix if necessary
												_snake_case						:					Tuple    =   (model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		_snake_case						:					List[str]    =   (model_prefix,) + flax_key

												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}."""						)

        # add batch stats if the model contains batchnorm layers
												if "batch_stats" in flax_model.params:
																		if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																								_snake_case						:					Dict    =   jnp.asarray(__lowercase						)
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(__lowercase ,      __lowercase						)
																								continue

																		# also add unexpected weight so that warning is thrown
																		_snake_case						:					int    =   jnp.asarray(__lowercase						)

												else:
																		# also add unexpected weight so that warning is thrown
																		_snake_case						:					List[Any]    =   jnp.asarray(__lowercase						)

						return unflatten_dict(__lowercase						)
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



						import torch

						# Load the index
						_snake_case						:					int    =   {}
						for shard_file in shard_filenames:
												# load using msgpack utils
												_snake_case						:					Any    =   torch.load(__lowercase						)
												_snake_case						:					List[str]    =   {k: v.numpy() for k, v in pt_state_dict.items()}

												_snake_case						:					Tuple    =   flax_model.base_model_prefix

												# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
												if "batch_stats" in flax_model.params:
																		_snake_case						:					List[Any]    =   flax_model.params["params"]

																		_snake_case						:					Optional[int]    =   flatten_dict(__lowercase						)
																		random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]						)						)
												else:
																		_snake_case						:					str    =   flax_model.params
																		_snake_case						:					List[Any]    =   flatten_dict(__lowercase						)

												_snake_case						:					int    =   (model_prefix not in flax_model_params) and (
												    model_prefix in {k.split("."						)[0] for k in pt_state_dict.keys()}
												)
												_snake_case						:					Optional[Any]    =   (model_prefix in flax_model_params) and (
												    model_prefix not in {k.split("."						)[0] for k in pt_state_dict.keys()}
												)
												# Need to change some parameters name to match Flax names
												for pt_key, pt_tensor in pt_state_dict.items():
																		_snake_case						:					str    =   tuple(pt_key.split("."						)						)

																		# remove base model prefix if necessary
																		_snake_case						:					List[Any]    =   pt_tuple_key[0] == model_prefix
																		if load_model_with_head_into_base_model and has_base_model_prefix:
																								_snake_case						:					List[Any]    =   pt_tuple_key[1:]

																		# Correctly rename weight parameters
																		_snake_case    ,_snake_case						:					Optional[int]    =   rename_key_and_reshape_tensor(
																		    __lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)
																		# add model prefix if necessary
																		_snake_case						:					List[Any]    =   (model_prefix,) + flax_key in random_flax_state_dict
																		if load_base_model_into_model_with_head and require_base_model_prefix:
																								_snake_case						:					List[str]    =   (model_prefix,) + flax_key

																		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}."""						)

            # add batch stats if the model contains batchnorm layers
																		if "batch_stats" in flax_model.params:
																								if "mean" in flax_key[-1]:
																														_snake_case						:					Any    =   jnp.asarray(__lowercase						)
																														continue
																								if "var" in flax_key[-1]:
																														_snake_case						:					List[Any]    =   jnp.asarray(__lowercase						)
																														continue
																								# remove num_batches_tracked key
																								if "num_batches_tracked" in flax_key[-1]:
																														flax_state_dict.pop(__lowercase ,      __lowercase						)
																														continue

																								# also add unexpected weight so that warning is thrown
																								_snake_case						:					Optional[Any]    =   jnp.asarray(__lowercase						)

																		else:
																								# also add unexpected weight so that warning is thrown
																								_snake_case						:					str    =   jnp.asarray(__lowercase						)
						return unflatten_dict(__lowercase						)
def  snake_case						(__lowercase ,      __lowercase						)	->	Any:

						'''simple docstring'''



						_snake_case						:					Tuple    =   os.path.abspath(__lowercase						)
						logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""						)

						# import correct flax class
						_snake_case						:					int    =   getattr(__lowercase ,      "Flax" + model.__class__.__name__						)

						# load flax weight dict
						with open(__lowercase ,      "rb"						) as state_f:
												try:
																		_snake_case						:					List[str]    =   from_bytes(__lowercase ,      state_f.read()						)
												except UnpicklingError:
																		raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """						)

						return load_flax_weights_in_pytorch_model(__lowercase ,      __lowercase						)

def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[int]:

						'''simple docstring'''



						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    "Loading a 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						:					str    =   flatten_dict(jax.tree_util.tree_map(lambda __lowercase						: x.dtype == jnp.bfloataa ,      __lowercase						)						).values()
						if any(__lowercase						):
												# convert all weights to fp32 if the 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						:					Optional[int]    =   jax.tree_util.tree_map(
												    lambda __lowercase						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params ,      __lowercase						)

						_snake_case						:					int    =   flatten_dict(__lowercase						)
						_snake_case						:					int    =   pt_model.state_dict()

						_snake_case						:					Any    =   (pt_model.base_model_prefix in flax_state) and (
						    pt_model.base_model_prefix not in {k.split("."						)[0] for k in pt_model_dict.keys()}
						)
						_snake_case						:					Optional[int]    =   (pt_model.base_model_prefix not in flax_state) and (
						    pt_model.base_model_prefix in {k.split("."						)[0] for k in pt_model_dict.keys()}
						)

						# keep track of unexpected & missing keys
						_snake_case						:					Union[str, Any]    =   []
						_snake_case						:					Union[str, Any]    =   set(pt_model_dict.keys()						)

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												_snake_case						:					Tuple    =   flax_key_tuple[0] == pt_model.base_model_prefix
												_snake_case						:					Optional[Any]    =   ".".join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

												# adapt flax_key to prepare for loading from/to base model only
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		_snake_case						:					Optional[Any]    =   flax_key_tuple[1:]
												elif load_base_model_into_model_with_head and require_base_model_prefix:
																		_snake_case						:					Optional[int]    =   (pt_model.base_model_prefix,) + flax_key_tuple

												# rename flax weights to PyTorch format
												if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowercase						) not in pt_model_dict:
																		# conv layer
																		_snake_case						:					Optional[int]    =   flax_key_tuple[:-1] + ("weight",)
																		_snake_case						:					str    =   jnp.transpose(__lowercase ,      (3, 2, 0, 1)						)
												elif flax_key_tuple[-1] == "kernel" and ".".join(__lowercase						) not in pt_model_dict:
																		# linear layer
																		_snake_case						:					Tuple    =   flax_key_tuple[:-1] + ("weight",)
																		_snake_case						:					Any    =   flax_tensor.T
												elif flax_key_tuple[-1] in ["scale", "embedding"]:
																		_snake_case						:					Optional[Any]    =   flax_key_tuple[:-1] + ("weight",)

												# adding batch stats from flax batch norm to pt
												elif "mean" in flax_key_tuple[-1]:
																		_snake_case						:					str    =   flax_key_tuple[:-1] + ("running_mean",)
												elif "var" in flax_key_tuple[-1]:
																		_snake_case						:					Optional[int]    =   flax_key_tuple[:-1] + ("running_var",)

												if "batch_stats" in flax_state:
																		_snake_case						:					str    =   ".".join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
												else:
																		_snake_case						:					Any    =   ".".join(__lowercase						)

												# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
												_snake_case						:					Dict    =   {}
												# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
												for key in pt_model_dict:
																		_snake_case						:					Tuple    =   key.split("."						)
																		_snake_case						:					int    =   None
																		if key_components[-3::2] == ["parametrizations", "original0"]:
																								_snake_case						:					Any    =   key_components[-2] + "_g"
																		elif key_components[-3::2] == ["parametrizations", "original1"]:
																								_snake_case						:					Optional[int]    =   key_components[-2] + "_v"
																		if name is not None:
																								_snake_case						:					Any    =   key_components[:-3] + [name]
																								_snake_case						:					Tuple    =   ".".join(__lowercase						)
																								_snake_case						:					List[str]    =   key

												if flax_key in special_pt_names:
																		_snake_case						:					Union[str, Any]    =   special_pt_names[flax_key]

												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						:					List[Any]    =   np.asarray(__lowercase						) if not isinstance(__lowercase ,      np.ndarray						) else flax_tensor
																								_snake_case						:					List[Any]    =   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
						_snake_case						:					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)."						)
						else:
												logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""						)
						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."						)
						else:
												logger.warning(
												    F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
												    "If your task is similar to the task the model of the checkpoint was trained on, "
												    F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""						)

						return pt_model
from __future__ import annotations
def  snake_case						(__lowercase ,      __lowercase						)	->	float:

						'''simple docstring'''



						_snake_case						:					Any    =   sorted(numsa + numsa						)
						_snake_case    ,_snake_case						:					Any    =   divmod(len(__lowercase						) ,      2						)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[float(x) for x in input('Enter the elements of first array: ').split()]
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			[float(x) for x in input('Enter the elements of second array: ').split()]
				print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')