




'''simple docstring'''





# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType

class   _a							(		__lowerCAmelCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= data






     def __iter__(					self       )				->     List[str]:
          for element in self.data:
               yield element







def       __a      (  _UpperCamelCase:       Union[str, Any]=True       )		->					Dict:

     """simple docstring"""




     _snake_case				= Accelerator(even_batches=_UpperCamelCase       )
     assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
     return accelerator





def       __a      (  _UpperCamelCase:       Accelerator   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       bool = False       )		->					Dict:

     """simple docstring"""




     if iterable:
          _snake_case				= DummyIterableDataset(torch.as_tensor(range(_UpperCamelCase       )       )       )
     else:
          _snake_case				= TensorDataset(torch.as_tensor(range(_UpperCamelCase       )       )       )

     _snake_case				= DataLoader(_UpperCamelCase   ,		batch_size=_UpperCamelCase       )
     _snake_case				= accelerator.prepare(_UpperCamelCase       )

     return dl





def       __a      (  _UpperCamelCase:       Accelerator   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       int   ,		_UpperCamelCase:       List[int]   ,		_UpperCamelCase:       List[int]   ,		)		->					str:

     """simple docstring"""




     _snake_case				= create_dataloader(accelerator=_UpperCamelCase   ,		dataset_size=_UpperCamelCase   ,		batch_size=_UpperCamelCase       )

     _snake_case				= [len(batch[0]       ) for batch in dl]

     if accelerator.process_index == 0:
          assert batch_sizes == process_0_expected_batch_sizes
     elif accelerator.process_index == 1:
          assert batch_sizes == process_1_expected_batch_sizes





def       __a      (  )		->					Tuple:

     """simple docstring"""




     _snake_case				= create_accelerator()

     # without padding, we would expect a different number of batches
     verify_dataloader_batch_sizes(
         _UpperCamelCase   ,		dataset_size=3   ,		batch_size=1   ,		process_0_expected_batch_sizes=[1, 1]   ,		process_1_expected_batch_sizes=[1, 1]   ,		)

     # without padding, we would expect the same number of batches, but different sizes
     verify_dataloader_batch_sizes(
         _UpperCamelCase   ,		dataset_size=7   ,		batch_size=2   ,		process_0_expected_batch_sizes=[2, 2]   ,		process_1_expected_batch_sizes=[2, 2]   ,		)





def       __a      (  )		->					List[str]:

     """simple docstring"""




     _snake_case				= create_accelerator(even_batches=_UpperCamelCase       )

     verify_dataloader_batch_sizes(
         _UpperCamelCase   ,		dataset_size=3   ,		batch_size=1   ,		process_0_expected_batch_sizes=[1, 1]   ,		process_1_expected_batch_sizes=[1]   ,		)

     verify_dataloader_batch_sizes(
         _UpperCamelCase   ,		dataset_size=7   ,		batch_size=2   ,		process_0_expected_batch_sizes=[2, 2]   ,		process_1_expected_batch_sizes=[2, 1]   ,		)





def       __a      (  )		->					List[str]:

     """simple docstring"""




     _snake_case				= create_accelerator(even_batches=_UpperCamelCase       )

     _snake_case				= torch.nn.Linear(1   ,		1       )
     _snake_case				= accelerator.prepare(_UpperCamelCase       )

     _snake_case				= create_dataloader(_UpperCamelCase   ,		dataset_size=3   ,		batch_size=1       )

     _snake_case				= []
     with accelerator.join_uneven_inputs([ddp_model]       ):
          for batch_idx, batch in enumerate(_UpperCamelCase       ):
               _snake_case				= ddp_model(batch[0].float()       )
               _snake_case				= output.sum()
               loss.backward()
               batch_idxs.append(_UpperCamelCase       )

     accelerator.wait_for_everyone()

     if accelerator.process_index == 0:
          assert batch_idxs == [0, 1]
     elif accelerator.process_index == 1:
          assert batch_idxs == [0]





def       __a      (  _UpperCamelCase:       Union[str, Any]       )		->					Any:

     """simple docstring"""




     with warnings.catch_warnings(record=_UpperCamelCase       ) as w:
          with accelerator.join_uneven_inputs([Mock()]       ):
               pass

          assert issubclass(w[-1].category   ,		_UpperCamelCase       )
          assert "only supported for multi-GPU" in str(w[-1].message       )





def       __a      (  )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= True
     _snake_case				= False
     _snake_case				= create_accelerator(even_batches=_UpperCamelCase       )
     _snake_case				= torch.nn.Linear(1   ,		1       )
     _snake_case				= accelerator.prepare(_UpperCamelCase       )
     _snake_case				= create_dataloader(_UpperCamelCase   ,		dataset_size=3   ,		batch_size=1       )
     _snake_case				= create_dataloader(_UpperCamelCase   ,		dataset_size=3   ,		batch_size=1       )

     with accelerator.join_uneven_inputs([ddp_model]   ,		even_batches=_UpperCamelCase       ):
          _snake_case				= train_dl.batch_sampler.even_batches
          _snake_case				= valid_dl.batch_sampler.even_batches

     assert train_dl_overridden_value == overridden_even_batches
     assert valid_dl_overridden_value == overridden_even_batches
     assert train_dl.batch_sampler.even_batches == default_even_batches
     assert valid_dl.batch_sampler.even_batches == default_even_batches





def       __a      (  )		->					List[str]:

     """simple docstring"""




     _snake_case				= True
     _snake_case				= False
     _snake_case				= create_accelerator(even_batches=_UpperCamelCase       )
     _snake_case				= torch.nn.Linear(1   ,		1       )
     _snake_case				= accelerator.prepare(_UpperCamelCase       )
     create_dataloader(_UpperCamelCase   ,		dataset_size=3   ,		batch_size=1   ,		iterable=_UpperCamelCase       )
     _snake_case				= create_dataloader(_UpperCamelCase   ,		dataset_size=3   ,		batch_size=1       )

     with warnings.catch_warnings():
          warnings.filterwarnings("ignore"       )
          try:
               with accelerator.join_uneven_inputs([ddp_model]   ,		even_batches=_UpperCamelCase       ):
                    _snake_case				= batch_dl.batch_sampler.even_batches
          except AttributeError:
               # ensure attribute error is not raised when processing iterable dl
               raise AssertionError

     assert batch_dl_overridden_value == overridden_even_batches
     assert batch_dl.batch_sampler.even_batches == default_even_batches





def       __a      (  )		->					Any:

     """simple docstring"""




     _snake_case				= create_accelerator()
     _snake_case				= torch.nn.Linear(1   ,		1       )
     _snake_case				= accelerator.prepare(_UpperCamelCase       )
     create_dataloader(_UpperCamelCase   ,		dataset_size=3   ,		batch_size=1   ,		iterable=_UpperCamelCase       )

     with warnings.catch_warnings(record=_UpperCamelCase       ) as w:
          with accelerator.join_uneven_inputs([ddp_model]   ,		even_batches=_UpperCamelCase       ):
               pass

          assert issubclass(w[-1].category   ,		_UpperCamelCase       )
          assert "only supported for map-style datasets" in str(w[-1].message       )





def       __a      (  )		->					str:

     """simple docstring"""




     _snake_case				= create_accelerator()

     accelerator.print("Test that even_batches variable ensures uniform batches across processes"       )
     test_default_ensures_even_batch_sizes()

     accelerator.print("Run tests with even_batches disabled"       )
     test_can_disable_even_batches()

     accelerator.print("Test joining uneven inputs"       )
     test_can_join_uneven_inputs()

     accelerator.print("Test overriding even_batches when joining uneven inputs"       )
     test_join_can_override_even_batches()

     accelerator.print("Test overriding even_batches for mixed dataloader types"       )
     test_join_can_override_for_mixed_type_dataloaders()

     accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"       )
     test_join_raises_warning_for_iterable_when_overriding_even_batches()

     accelerator.print("Test join with non DDP distributed raises warning"       )
     _snake_case				= accelerator.state.distributed_type
     _snake_case				= DistributedType.FSDP
     test_join_raises_warning_for_non_ddp_distributed(_UpperCamelCase       )
     _snake_case				= original_state


if __name__ == "__main__":
   main()








'''simple docstring'''





from __future__ import annotations

from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     None:
          _snake_case				= num_of_nodes
          _snake_case				= []
          _snake_case				= {}






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:
          self.m_edges.append([u_node, v_node, weight]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     int:

          if self.m_component[u_node] == u_node:
               return u_node
          return self.find_component(self.m_component[u_node]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if self.m_component[u_node] != u_node:
               for k in self.m_component:
                    _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if component_size[u_node] <= component_size[v_node]:
               _snake_case				= v_node
               component_size[v_node] += component_size[u_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )

          elif component_size[u_node] >= component_size[v_node]:
               _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )
               component_size[u_node] += component_size[v_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     None:
          _snake_case				= []
          _snake_case				= 0

          _snake_case				= [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
               self.m_component.update({node: node}       )
               component_size.append(1       )

          _snake_case				= self.m_num_of_nodes

          while num_of_components > 1:
               for edge in self.m_edges:
                    _snake_case				,   _snake_case				,   _snake_case				= edge

                    _snake_case				= self.m_component[u]
                    _snake_case				= self.m_component[v]

                    if u_component != v_component:

                         for component in (u_component, v_component):
                              if (
                                  minimum_weight_edge[component] == -1
                                  or minimum_weight_edge[component][2] > w
                              ):
                                   _snake_case				= [u, v, w]

               for edge in minimum_weight_edge:
                    if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         _snake_case				,   _snake_case				,   _snake_case				= edge

                         _snake_case				= self.m_component[u]
                         _snake_case				= self.m_component[v]

                         if u_component != v_component:
                              mst_weight += w
                              self.union(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                              print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                              num_of_components -= 1

               _snake_case				= [-1] * self.m_num_of_nodes
          print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""       )







def       __a      (  )		->					None:

     """simple docstring"""






if __name__ == "__main__":
   import doctest

   doctest.testmod()


