# 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 os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(__lowercase )   ,  'Tatoeba directory does not exist.' )
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           return TatoebaConverter(save_dir=A_						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.resolver.convert_models(['''heb-eng''']						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.resolver.write_model_card('''opus-mt-he-en'''				,				dry_run=A_						)
           assert mmeta["long_pair"] == "heb-eng"