
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) -> bool:
		lowercase       =    sqrt((x**2) + (y**2)							)
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	lowercase       =    mean(
	    int(is_in_circle(uniform(-1.0 ,							1.0							) ,							uniform(-1.0 ,							1.0							)							)							)
	    for _ in range(__SCREAMING_SNAKE_CASE							)							)
	# The ratio of the area for circle to square is pi/4.
	lowercase       =    proportion * 4
	print(F'''The estimated value of pi is {pi_estimate}'''							)
	print(F'''The numpy value of pi is {pi}'''							)
	print(F'''The total error is {abs(pi - pi_estimate							)}'''							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = 0.0 ,							__SCREAMING_SNAKE_CASE = 1.0 ,							):
	return mean(
	    function_to_integrate(uniform(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)							) for _ in range(__SCREAMING_SNAKE_CASE							)							) * (max_value - min_value)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE = 0.0 ,							__SCREAMING_SNAKE_CASE = 1.0							):

	def identity_function(__SCREAMING_SNAKE_CASE							) -> float:
		return x

	lowercase       =    area_under_curve_estimator(
	    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    (max_value * max_value - min_value * min_value) / 2

	print('******************'							)
	print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''							)
	print(F'''Estimated value is {estimated_value}'''							)
	print(F'''Expected value is {expected_value}'''							)
	print(F'''Total error is {abs(estimated_value - expected_value							)}'''							)
	print('******************'							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):

	def function_to_integrate(__SCREAMING_SNAKE_CASE							) -> float:
		return sqrt(4.0 - x * x							)

	lowercase       =    area_under_curve_estimator(
	    __SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							0.0 ,							2.0							)

	print('******************'							)
	print('Estimating pi using area_under_curve_estimator'							)
	print(F'''Estimated value is {estimated_value}'''							)
	print(F'''Expected value is {pi}'''							)
	print(F'''Total error is {abs(estimated_value - pi							)}'''							)
	print('******************'							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    r'\w+[.]\d+'
	lowercase       =    re.findall(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	for pat in pats:
		lowercase       =    key.replace(__SCREAMING_SNAKE_CASE ,							'_'.join(pat.split('.'							)							)							)
	return key






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    pt_tuple_key[:-1] + ('scale',)
	if (
	    any('norm' in str_ for str_ in pt_tuple_key							)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		lowercase       =    pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		lowercase       =    pt_tuple_key[:-1] + ('scale',)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		lowercase       =    pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	lowercase       =    pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		lowercase       =    pt_tensor.transpose(2 ,							3 ,							1 ,							0							)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	lowercase       =    pt_tuple_key[:-1] + ('kernel',)
	if pt_tuple_key[-1] == "weight":
		lowercase       =    pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm weight
	lowercase       =    pt_tuple_key[:-1] + ('weight',)
	if pt_tuple_key[-1] == "gamma":
		return renamed_pt_tuple_key, pt_tensor

	# old PyTorch layer norm bias
	lowercase       =    pt_tuple_key[:-1] + ('bias',)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=42							):
	# Step 1: Convert pytorch tensor to numpy
	lowercase       =    {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	lowercase       =    flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE							)							)

	lowercase       =    flatten_dict(__SCREAMING_SNAKE_CASE							)
	lowercase       =    {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		lowercase       =    rename_key(__SCREAMING_SNAKE_CASE							)
		lowercase       =    tuple(renamed_pt_key.split('.'							)							)

		# Correctly rename weight parameters
		lowercase  ,					lowercase       =    rename_key_and_reshape_tensor(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

		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}.'''							)

        # also add unexpected weight so that warning is thrown
		lowercase       =    jnp.asarray(__SCREAMING_SNAKE_CASE							)

	return unflatten_dict(__SCREAMING_SNAKE_CASE							)

