




"""simple docstring"""






import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
			# Create universe of discourse in Python using linspace ()
			lowercase__     						=						np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

			# Create two fuzzy sets by defining any membership function
			# (trapmf(), gbellmf(), gaussmf(), etc).
			lowercase__     						=						[0, 25, 50]
			lowercase__     						=						[25, 50, 75]
			lowercase__     						=						fuzz.membership.trimf(X, abca)
			lowercase__     						=						fuzz.membership.trimf(X, abca)

			# Compute the different operations using inbuilt functions.
			lowercase__     						=						np.ones(75)
			lowercase__     						=						np.zeros((75,))
			# 1. Union = max(µA(x), µB(x))
			lowercase__     						=						fuzz.fuzzy_or(X, young, X, middle_aged)[1]
			# 2. Intersection = min(µA(x), µB(x))
			lowercase__     						=						fuzz.fuzzy_and(X, young, X, middle_aged)[1]
			# 3. Complement (A) = (1- min(µA(x))
			lowercase__     						=						fuzz.fuzzy_not(young)
			# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
			lowercase__     						=						fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
			# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
			lowercase__     						=						young + middle_aged - (young * middle_aged)
			# 6. Algebraic Product = (µA(x) * µB(x))
			lowercase__     						=						young * middle_aged
			# 7. Bounded Sum = min[1,(µA(x), µB(x))]
			lowercase__     						=						fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
			# 8. Bounded difference = min[0,(µA(x), µB(x))]
			lowercase__     						=						fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

			# max-min composition
			# max-product composition

			# Plot each set A, set B and each operation result using plot() and subplot().
			from matplotlib import pyplot as plt

			plt.figure()

			plt.subplot(4, 3, 1)
			plt.plot(X, young)
			plt.title("""Young""")
			plt.grid(True)

			plt.subplot(4, 3, 2)
			plt.plot(X, middle_aged)
			plt.title("""Middle aged""")
			plt.grid(True)

			plt.subplot(4, 3, 3)
			plt.plot(X, union)
			plt.title("""union""")
			plt.grid(True)

			plt.subplot(4, 3, 4)
			plt.plot(X, intersection)
			plt.title("""intersection""")
			plt.grid(True)

			plt.subplot(4, 3, 5)
			plt.plot(X, complement_a)
			plt.title("""complement_a""")
			plt.grid(True)

			plt.subplot(4, 3, 6)
			plt.plot(X, difference)
			plt.title("""difference a/b""")
			plt.grid(True)

			plt.subplot(4, 3, 7)
			plt.plot(X, alg_sum)
			plt.title("""alg_sum""")
			plt.grid(True)

			plt.subplot(4, 3, 8)
			plt.plot(X, alg_product)
			plt.title("""alg_product""")
			plt.grid(True)

			plt.subplot(4, 3, 9)
			plt.plot(X, bdd_sum)
			plt.title("""bdd_sum""")
			plt.grid(True)

			plt.subplot(4, 3, 10)
			plt.plot(X, bdd_difference)
			plt.title("""bdd_difference""")
			plt.grid(True)

			plt.subplots_adjust(hspace=0.5)
			plt.show()
"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()				,			UpperCamelCase__ )


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





