import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PhobertTokenizer
  lowercase_			=     False




  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
       SCREAMING_SNAKE_CASE__				:				Tuple			=		dict(zip(a_     , range(len(a_				)				)				)				)
       SCREAMING_SNAKE_CASE__				:				Any			=		['#version: 0.2', 'l à</w>']
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{'unk_token': '<unk>'}

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES['vocab_file']				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES['merges_file']				)

       with open(self.vocab_file     , 'w'     , encoding='utf-8'				) as fp:
            for token in vocab_tokens:
                 fp.write(F'''{token} {vocab_tokens[token]}\n'''				)
       with open(self.merges_file     , 'w'     , encoding='utf-8'				) as fp:
            fp.write('\n'.join(a_				)				)




  def 	__lowercase(      self     :	str     , **a_     :	Any				)->       Tuple:




       """simple docstring"""
       kwargs.update(self.special_tokens_map				)
       return PhobertTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	List[str]     , a_     :	Any				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'Tôi là VinAI Research'
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
       return input_text, output_text




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		PhobertTokenizer(self.vocab_file     , self.merges_file     , **self.special_tokens_map				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Tôi là VinAI Research'
       SCREAMING_SNAKE_CASE__				:				Tuple			=		'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
       SCREAMING_SNAKE_CASE__				:				Dict			=		tokenizer.tokenize(a_				)
       print(a_				)
       self.assertListEqual(a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		tokens + [tokenizer.unk_token]

       SCREAMING_SNAKE_CASE__				:				Tuple			=		[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a_				)     , a_				)




# 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     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     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     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



