'''simple docstring'''





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      return (torch.arange(state.num_processes		) + 1.0 + (state.num_processes * state.process_index)).to(state.device		)
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				create_tensor(__UpperCAmelCase		)
      _lowerCamelCase			    =				gather(__UpperCAmelCase		)
      assert gathered_tensor.tolist() == list(range(1 ,						state.num_processes**2 + 1		)		)
def       __magic_name__(  __UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				[state.process_index]
      _lowerCamelCase			    =				gather_object(__UpperCAmelCase		)
      assert len(__UpperCAmelCase		) == state.num_processes, F'{gathered_obj}, {len(__UpperCAmelCase		)} != {state.num_processes}'
      assert gathered_obj == list(range(state.num_processes		)		), F'{gathered_obj} != {list(range(state.num_processes		)		)}'
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				create_tensor(__UpperCAmelCase		)
      _lowerCamelCase			    =				broadcast(__UpperCAmelCase		)
      assert broadcasted_tensor.shape == torch.Size([state.num_processes]		)
      assert broadcasted_tensor.tolist() == list(range(1 ,						state.num_processes + 1		)		)
def       __magic_name__(  __UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      if state.is_main_process:
            _lowerCamelCase			    =				torch.arange(state.num_processes + 1		).to(state.device		)
      else:
            _lowerCamelCase			    =				torch.arange(state.num_processes		).to(state.device		)
      _lowerCamelCase			    =				pad_across_processes(__UpperCAmelCase		)
      assert padded_tensor.shape == torch.Size([state.num_processes + 1]		)
      if not state.is_main_process:
            assert padded_tensor.tolist() == list(range(0 ,						state.num_processes		)		) + [0]
def       __magic_name__(  __UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      if state.num_processes != 2:
            return
      _lowerCamelCase			    =				create_tensor(__UpperCAmelCase		)
      _lowerCamelCase			    =				reduce(__UpperCAmelCase ,						'''sum'''		)
      _lowerCamelCase			    =				torch.tensor([4.0, 6]		).to(state.device		)
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase		), F'{reduced_tensor} != {truth_tensor}'
def       __magic_name__(  __UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

      if state.num_processes != 2:
            return
      _lowerCamelCase			    =				create_tensor(__UpperCAmelCase		)
      _lowerCamelCase			    =				reduce(__UpperCAmelCase ,						'''mean'''		)
      _lowerCamelCase			    =				torch.tensor([2.0, 3]		).to(state.device		)
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase		), F'{reduced_tensor} != {truth_tensor}'
def       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      main()
def       __magic_name__(  )						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				PartialState()
      state.print(F'State: {state}'		)
      state.print('''testing gather'''		)
      test_gather(__UpperCAmelCase		)
      state.print('''testing gather_object'''		)
      test_gather_object(__UpperCAmelCase		)
      state.print('''testing broadcast'''		)
      test_broadcast(__UpperCAmelCase		)
      state.print('''testing pad_across_processes'''		)
      test_pad_across_processes(__UpperCAmelCase		)
      state.print('''testing reduce_sum'''		)
      test_reduce_sum(__UpperCAmelCase		)
      state.print('''testing reduce_mean'''		)
      test_reduce_mean(__UpperCAmelCase		)


if __name__ == "__main__":
     main()
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= 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).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

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