

import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class     lowercase						(       _UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				GPTNeoXJapaneseTokenizer
 __SCREAMING_SNAKE_CASE												=				False
 __SCREAMING_SNAKE_CASE												=				{"""do_clean_text""": False, """add_prefix_space""": False}




 def   UpperCamelCase__							(self ) ->					Union[str, Any]:






  """simple docstring"""





  super().setUp()

  UpperCAmelCase__							=				[
      'こん',
      'こんに',
      'にちは',
      'ばんは',
      '世界,㔺界',
      '、',
      '。',
      '<BR>',
      '<SP>',
      '<TAB>',
      '<URL>',
      '<EMAIL>',
      '<TEL>',
      '<DATE>',
      '<PRICE>',
      '<BLOCK>',
      '<KIGOU>',
      '<U2000U2BFF>',
      '<|emoji1|>',
      '<unk>',
      '<|startoftext|>',
      '<|endoftext|>',
  ]
  UpperCAmelCase__							=				{'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
  UpperCAmelCase__							=				{'unk_token': '<unk>'}

  UpperCAmelCase__							=				os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['vocab_file'] )
  UpperCAmelCase__							=				os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['emoji_file'] )
  with open(self.vocab_file       ,       'w'       ,       encoding='utf-8' ) as vocab_writer:
   vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
  with open(self.emoji_file       ,       'w' ) as emoji_writer:
   emoji_writer.write(json.dumps(__a ) )




 def   UpperCamelCase__							(self       ,       **__a ) ->					int:






  """simple docstring"""





  kwargs.update(self.special_tokens_map )
  return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname       ,       **__a )




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






  """simple docstring"""





  UpperCAmelCase__							=				'こんにちは、世界。 \nこんばんは、㔺界。😀'
  UpperCAmelCase__							=				'こんにちは、世界。 \nこんばんは、世界。😀'
  return input_text, output_text




 def   UpperCamelCase__							(self       ,       __a ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				self.get_input_output_texts(__a )
  UpperCAmelCase__							=				tokenizer.encode(__a       ,       add_special_tokens=__a )
  UpperCAmelCase__							=				tokenizer.decode(__a       ,       clean_up_tokenization_spaces=__a )
  return text, ids




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  pass  # TODO add if relevant




 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  pass  # TODO add if relevant




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  pass  # TODO add if relevant




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.get_tokenizer()

  # Testing tokenization
  UpperCAmelCase__							=				'こんにちは、世界。　こんばんは、㔺界。'
  UpperCAmelCase__							=				['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
  UpperCAmelCase__							=				tokenizer.tokenize(__a )
  self.assertListEqual(__a       ,       __a )

  # Testing conversion to ids without special tokens
  UpperCAmelCase__							=				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
  UpperCAmelCase__							=				tokenizer.convert_tokens_to_ids(__a )
  self.assertListEqual(__a       ,       __a )

  # Testing conversion to ids with special tokens
  UpperCAmelCase__							=				tokens + [tokenizer.unk_token]
  UpperCAmelCase__							=				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
  UpperCAmelCase__							=				tokenizer.convert_tokens_to_ids(__a )
  self.assertListEqual(__a       ,       __a )




 @slow
 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b' )

  UpperCAmelCase__							=				tokenizer.encode('ありがとう。'       ,       add_special_tokens=__a )
  UpperCAmelCase__							=				tokenizer.encode('どういたしまして。'       ,       add_special_tokens=__a )

  UpperCAmelCase__							=				tokenizer.build_inputs_with_special_tokens(__a )
  UpperCAmelCase__							=				tokenizer.build_inputs_with_special_tokens(__a       ,       __a )

  assert encoded_sentence == ids_a
  assert encoded_pair == ids_a + ids_a




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  pass




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  pass






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


_UpperCamelCase			       =			namedtuple('''ExampleNamedTuple''', '''a b c''')



class     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				torch.randn(5       ,       2 )
  UpperCAmelCase__							=				torch.device('cuda' ) if torch.cuda.is_available() else torch.device('cpu' )

  UpperCAmelCase__							=				send_to_device(__a       ,       __a )
  self.assertTrue(torch.equal(resulta.cpu()       ,       __a ) )

  UpperCAmelCase__							=				send_to_device((tensor, [tensor, tensor], 1)       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta[0].cpu()       ,       __a ) )
  self.assertIsInstance(resulta[1]       ,       __a )
  self.assertTrue(torch.equal(resulta[1][0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta[1][1].cpu()       ,       __a ) )
  self.assertEqual(resulta[2]       ,       1 )

  UpperCAmelCase__							=				send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta['a'].cpu()       ,       __a ) )
  self.assertIsInstance(resulta['b']       ,       __a )
  self.assertTrue(torch.equal(resulta['b'][0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta['b'][1].cpu()       ,       __a ) )
  self.assertEqual(resulta['c']       ,       1 )

  UpperCAmelCase__							=				send_to_device(ExampleNamedTuple(a=__a       ,       b=[tensor, tensor]       ,       c=1 )       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta.a.cpu()       ,       __a ) )
  self.assertIsInstance(resulta.b       ,       __a )
  self.assertTrue(torch.equal(resulta.b[0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta.b[1].cpu()       ,       __a ) )
  self.assertEqual(resulta.c       ,       1 )

  UpperCAmelCase__							=				send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1} )       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta['a'].cpu()       ,       __a ) )
  self.assertIsInstance(resulta['b']       ,       __a )
  self.assertTrue(torch.equal(resulta['b'][0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta['b'][1].cpu()       ,       __a ) )
  self.assertEqual(resulta['c']       ,       1 )




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  with self.assertRaises(__a ) as cm:
   UpperCAmelCase__							=				recursively_apply(torch.tensor       ,       (torch.tensor(1 ), 1)       ,       error_on_other_type=__a )
  self.assertEqual(
      str(cm.exception )       ,       'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'       ,       )




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				torch.tensor([1, 2, 3, 4, 5] )
  self.assertEqual(listify(__a )       ,       [1, 2, 3, 4, 5] )

  UpperCAmelCase__							=				torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )
  self.assertEqual(listify(__a )       ,       [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )

  UpperCAmelCase__							=				torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )
  self.assertEqual(
      listify(__a )       ,       [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  with patch_environment(aa=1       ,       BB=2 ):
   self.assertEqual(os.environ.get('AA' )       ,       '1' )
   self.assertEqual(os.environ.get('BB' )       ,       '2' )

  self.assertNotIn('AA'       ,       os.environ )
  self.assertNotIn('BB'       ,       os.environ )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				model.forward
  UpperCAmelCase__							=				convert_outputs_to_fpaa(model.forward )
  UpperCAmelCase__							=				extract_model_from_parallel(__a       ,       keep_fpaa_wrapper=__a )
  UpperCAmelCase__							=				pickle.dumps(__a )




 @require_cuda
 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				model.forward
  UpperCAmelCase__							=				torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
  UpperCAmelCase__							=				convert_outputs_to_fpaa(model.forward )
  UpperCAmelCase__							=				extract_model_from_parallel(__a       ,       keep_fpaa_wrapper=__a )
  UpperCAmelCase__							=				pickle.dumps(__a )




 @require_cuda
 @require_torch_min_version(version='2.0' )
 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				model.forward
  UpperCAmelCase__							=				torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
  UpperCAmelCase__							=				convert_outputs_to_fpaa(model.forward )
  UpperCAmelCase__							=				torch.compile(model.forward       ,       backend='inductor' )
  UpperCAmelCase__							=				torch.randn(4       ,       10 ).cuda()
  UpperCAmelCase__							=				model(__a )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  UpperCAmelCase__							=				torch.nn.parallel.DataParallel(__a )
  UpperCAmelCase__							=				extract_model_from_parallel(__a )

  self.assertEqual(__a       ,       __a )




 @require_torch_min_version(version='2.0' )
 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				torch.compile(__a )

  # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  UpperCAmelCase__							=				torch.nn.parallel.DataParallel(__a )
  UpperCAmelCase__							=				torch.compile(__a )
  UpperCAmelCase__							=				extract_model_from_parallel(__a )

  self.assertEqual(compiled_model._orig_mod       ,       compiled_model_unwrapped._orig_mod )




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3] )] )       ,       torch.device('cpu' ) )
  self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3] )} )       ,       torch.device('cpu' ) )
  self.assertIsNone(find_device([1, 'a'] ) )



