'''simple docstring'''

import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_     :						List[str]    							='''▁'''

A_     :						int    							=get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				int											= BertGenerationTokenizer
  __A :				Optional[int]											= False
  __A :				Dict											= True





  def 						UpperCAmelCase_	(	self ):
     super().setUp()

     lowerCAmelCase_			=      BertGenerationTokenizer(_lowerCamelCase    ,     keep_accents=_lowerCamelCase )
     tokenizer.save_pretrained(self.tmpdirname )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''<s>'''
     lowerCAmelCase_			=      1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase )    ,     _lowerCamelCase )
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase )    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      list(self.get_tokenizer().get_vocab().keys() )

     self.assertEqual(vocab_keys[0]    ,     '''<unk>''' )
     self.assertEqual(vocab_keys[1]    ,     '''<s>''' )
     self.assertEqual(vocab_keys[-1]    ,     '''<pad>''' )
     self.assertEqual(len(_lowerCamelCase )    ,     1002 )





  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(self.get_tokenizer().vocab_size    ,     1000 )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      BertGenerationTokenizer(_lowerCamelCase    ,     keep_accents=_lowerCamelCase )

     lowerCAmelCase_			=      tokenizer.tokenize('''This is a test''' )
     self.assertListEqual(_lowerCamelCase    ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowerCamelCase )    ,     [285, 46, 10, 170, 382]    ,     )

     lowerCAmelCase_			=      tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
     self.assertListEqual(
         _lowerCamelCase    ,     [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''é''',
             '''.''',
         ]    ,     )
     lowerCAmelCase_			=      tokenizer.convert_tokens_to_ids(_lowerCamelCase )
     self.assertListEqual(
         _lowerCamelCase    ,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]    ,     )

     lowerCAmelCase_			=      tokenizer.convert_ids_to_tokens(_lowerCamelCase )
     self.assertListEqual(
         _lowerCamelCase    ,     [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''<unk>''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''<unk>''',
             '''.''',
         ]    ,     )





  @cached_property
  def 						UpperCAmelCase_	(	self ):
     return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )





  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''Hello World!'''
     lowerCAmelCase_			=      [1_8536, 2260, 101]

     self.assertListEqual(_lowerCamelCase    ,     self.big_tokenizer.encode(_lowerCamelCase ) )





  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      (
         '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
         ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
     )
     lowerCAmelCase_			=      [
         871,
         419,
         358,
         946,
         991,
         2521,
         452,
         358,
         1357,
         387,
         7751,
         3536,
         112,
         985,
         456,
         126,
         865,
         938,
         5400,
         5734,
         458,
         1368,
         467,
         786,
         2462,
         5246,
         1159,
         633,
         865,
         4519,
         457,
         582,
         852,
         2557,
         427,
         916,
         508,
         405,
         3_4324,
         497,
         391,
         408,
         1_1342,
         1244,
         385,
         100,
         938,
         985,
         456,
         574,
         362,
         1_2597,
         3200,
         3129,
         1172,
     ]

     self.assertListEqual(_lowerCamelCase    ,     self.big_tokenizer.encode(_lowerCamelCase ) )





  @require_torch
  @slow
  def 						UpperCAmelCase_	(	self ):
     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     lowerCAmelCase_			=      list(self.big_tokenizer.get_vocab().keys() )[:10]
     lowerCAmelCase_			=      ''' '''.join(_lowerCamelCase )
     lowerCAmelCase_			=      self.big_tokenizer.encode_plus(_lowerCamelCase    ,     return_tensors='''pt'''    ,     return_token_type_ids=_lowerCamelCase )
     lowerCAmelCase_			=      self.big_tokenizer.batch_encode_plus(
         [sequence + ''' ''' + sequence]    ,     return_tensors='''pt'''    ,     return_token_type_ids=_lowerCamelCase )

     lowerCAmelCase_			=      BertGenerationConfig()
     lowerCAmelCase_			=      BertGenerationEncoder(_lowerCamelCase )

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
        model(**_lowerCamelCase )
        model(**_lowerCamelCase )







  @slow
  def 						UpperCAmelCase_	(	self ):
     # fmt: off
     lowerCAmelCase_			=      {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowerCamelCase    ,     model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''    ,     revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''    ,     )






'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects



def snake_case_      (					__snake_case  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]	,   args[1:])])
         raise TypeError(
             F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
             F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''')
      while True:
         if batch_size == 0:
            raise RuntimeError('''No executable batch size found, reached zero.''')
         try:
            return function(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__snake_case):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator





