

"""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						       =      logging.get_logger(__name__)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		try:
				with open(_A ,			"rb"			) as flax_state_f:
						__lowerCAmelCase:	List[str]   				=      from_bytes(_A ,			flax_state_f.read()			)
		except UnpicklingError as e:
				try:
						with open(_A			) 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(_A ,			_A			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:

		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:	Tuple   				=      flatten_dict(jax.tree_util.tree_map(lambda __SCREAMING_SNAKE_CASE			: x.dtype == jnp.bfloataa ,			_A			)			).values()
		if any(_A			):
				# 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:	int   				=      jax.tree_util.tree_map(
				    lambda __SCREAMING_SNAKE_CASE			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params ,			_A			)

		__lowerCAmelCase:	str   				=      ''

		__lowerCAmelCase:	Optional[int]   				=      flatten_dict(_A ,			sep="."			)
		__lowerCAmelCase:	Optional[Any]   				=      pt_model.state_dict()

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

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				__lowerCAmelCase:	Union[str, Any]   				=      flax_key_tuple.split("."			)

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

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(_A			):
								__lowerCAmelCase:	Dict   				=      (
								    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:	List[Any]   				=      '.'.join(_A			)

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

		pt_model.load_state_dict(_A			)

		# re-transform missing_keys to list
		__lowerCAmelCase:	Any   				=      list(_A			)

		if len(_A			) > 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(_A			) > 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"""


from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1_0**-1_0			)     ->     float:
		__lowerCAmelCase:	Union[str, Any]   				=      a
		while True:
				__lowerCAmelCase:	Optional[int]   				=      Decimal(__SCREAMING_SNAKE_CASE			) - (
				    Decimal(eval(__SCREAMING_SNAKE_CASE			)			) / Decimal(eval(str(diff(__SCREAMING_SNAKE_CASE			)			)			)			)  # noqa: S307
				)
				# This number dictates the accuracy of the answer
				if abs(eval(__SCREAMING_SNAKE_CASE			)			) < precision:  # noqa: S307
						return float(__SCREAMING_SNAKE_CASE			)


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
			# Find root of polynomial
			print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
			# Find Square Root of 5
			print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
			# Exponential Roots
			print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


