

"""simple docstring"""






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DebertaTokenizer
  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		DebertaTokenizerFast

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowerCAmelCase       :   List[str]  =    [
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      _lowerCAmelCase       :   Optional[int]  =    dict(zip(_A    ,range(len(_A      )      )      )      )
      _lowerCAmelCase       :   Any  =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      _lowerCAmelCase       :   int  =    {'unk_token': '[UNK]'}

      _lowerCAmelCase       :   List[Any]  =    os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
      _lowerCAmelCase       :   List[str]  =    os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['merges_file']      )
      with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as fp:
          fp.write(json.dumps(_A      ) + '\n'      )
      with open(self.merges_file    ,'w'    ,encoding='utf-8'      ) as fp:
          fp.write('\n'.join(_A      )      )

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      kwargs.update(self.special_tokens_map      )
      return self.tokenizer_class.from_pretrained(self.tmpdirname    ,**_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    'lower newer'
      _lowerCAmelCase       :   Dict  =    'lower newer'
      return input_text, output_text

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.get_tokenizer()
      _lowerCAmelCase       :   Optional[Any]  =    'lower newer'
      _lowerCAmelCase       :   Dict  =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      _lowerCAmelCase       :   Optional[int]  =    tokenizer.tokenize(_A      )
      self.assertListEqual(_A    ,_A      )

      _lowerCAmelCase       :   List[str]  =    tokens + [tokenizer.unk_token]
      _lowerCAmelCase       :   Dict  =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.get_tokenizer()
      _lowerCAmelCase       :   Any  =    tokenizer('Hello'    ,'World'      )
      _lowerCAmelCase       :   Optional[int]  =    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']    ,_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.tokenizer_class.from_pretrained('microsoft/deberta-base'      )

      _lowerCAmelCase       :   Optional[Any]  =    tokenizer.encode('sequence builders'    ,add_special_tokens=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    tokenizer.encode('multi-sequence build'    ,add_special_tokens=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    tokenizer.encode(
          'sequence builders'    ,add_special_tokens=_A    ,add_prefix_space=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    tokenizer.encode(
          'sequence builders'    ,'multi-sequence build'    ,add_special_tokens=_A    ,add_prefix_space=_A      )

      _lowerCAmelCase       :   int  =    tokenizer.build_inputs_with_special_tokens(_A      )
      _lowerCAmelCase       :   List[Any]  =    tokenizer.build_inputs_with_special_tokens(_A    ,_A      )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    [self.tokenizer_class]
      if self.test_rust_tokenizer:
          tokenizer_classes.append(self.rust_tokenizer_class      )

      for tokenizer_class in tokenizer_classes:
          _lowerCAmelCase       :   Dict  =    tokenizer_class.from_pretrained('microsoft/deberta-base'      )

          _lowerCAmelCase       :   Tuple  =    [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          _lowerCAmelCase       :   List[str]  =    tokenizer(_A    ,padding=_A      )
          _lowerCAmelCase       :   Dict  =    [tokenizer.decode(_A    ,skip_special_tokens=_A      ) for seq in encoding['input_ids']]

          # fmt: off
          _lowerCAmelCase       :   Tuple  =    {
              'input_ids': [
                  [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
              ],
              'token_type_ids': [
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
              ],
              'attention_mask': [
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
              ]
          }
          # fmt: on

          _lowerCAmelCase       :   List[str]  =    [
              'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
              'ALBERT incorporates two parameter reduction techniques',
              'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
              ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
              ' vocabulary embedding.',
          ]

          self.assertDictEqual(encoding.data    ,_A      )

          for expected, decoded in zip(_A    ,_A      ):
              self.assertEqual(_A    ,_A      )








"""simple docstring"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    inspect.getfile(accelerate.test_utils      )
      _lowerCAmelCase       :   Union[str, Any]  =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_script.py']      )
      _lowerCAmelCase       :   int  =    os.path.sep.join(
          mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_distributed_data_loop.py']      )
      _lowerCAmelCase       :   Optional[Any]  =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_ops.py']      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices."""      )
      _lowerCAmelCase       :   int  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices."""      )
      _lowerCAmelCase       :   str  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(F"""Command: {cmd}"""      )
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )







  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
      _lowerCAmelCase       :   Tuple  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1    ,cuda_visible_devices='0,1'      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )


if __name__ == "__main__":
 _lowerCAmelCase						=		Accelerator()
 _lowerCAmelCase						=		(accelerator.state.process_index + 2, 1_0)
 _lowerCAmelCase						=		torch.randint(0, 1_0, shape).to(accelerator.device)

 _lowerCAmelCase						=		""""""

 _lowerCAmelCase						=		accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 _lowerCAmelCase						=		accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 _lowerCAmelCase						=		accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)





