'''simple docstring'''

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     __UpperCAmelCase       ( __a						,       unittest.TestCase		):
  __A :				Union[str, Any]											= RobertaTokenizer
  __A :				List[str]											= RobertaTokenizerFast
  __A :				str											= True
  __A :				Optional[Any]											= {'cls_token': '<s>'}





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

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

     lowerCAmelCase_			=      os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['''vocab_file'''] )
     lowerCAmelCase_			=      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(_lowerCamelCase ) + '''\n''' )
     with open(self.merges_file    ,     '''w'''    ,     encoding='''utf-8''' ) as fp:
        fp.write('''\n'''.join(_lowerCamelCase ) )





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     kwargs.update(self.special_tokens_map )
     return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     kwargs.update(self.special_tokens_map )
     return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      '''lower newer'''
     lowerCAmelCase_			=      '''lower newer'''
     return input_text, output_text





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.tokenizer_class(self.vocab_file    ,     self.merges_file    ,     **self.special_tokens_map )
     lowerCAmelCase_			=      '''lower newer'''
     lowerCAmelCase_			=      ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
     lowerCAmelCase_			=      tokenizer.tokenize(_lowerCamelCase )  # , add_prefix_space=True)
     self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      tokens + [tokenizer.unk_token]
     lowerCAmelCase_			=      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('''Hello world!'''    ,     add_special_tokens=_lowerCamelCase )    ,     [0, 3_1414, 232, 328, 2] )
     self.assertListEqual(
         tokenizer.encode('''Hello world! cécé herlolip 418'''    ,     add_special_tokens=_lowerCamelCase )    ,     [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]    ,     )





  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.tokenizer_class.from_pretrained('''roberta-base''' )

     lowerCAmelCase_			=      tokenizer.encode('''sequence builders'''    ,     add_special_tokens=_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.encode('''multi-sequence build'''    ,     add_special_tokens=_lowerCamelCase )

     lowerCAmelCase_			=      tokenizer.encode(
         '''sequence builders'''    ,     add_special_tokens=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.encode(
         '''sequence builders'''    ,     '''multi-sequence build'''    ,     add_special_tokens=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase )

     lowerCAmelCase_			=      tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.build_inputs_with_special_tokens(_lowerCamelCase    ,     _lowerCamelCase )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.get_tokenizer()

     lowerCAmelCase_			=      '''Encode this sequence.'''
     lowerCAmelCase_			=      tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]

     # Testing encoder arguments
     lowerCAmelCase_			=      tokenizer.encode(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
     self.assertNotEqual(_lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      tokenizer.encode(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.convert_ids_to_tokens(encoded[0] )[0]
     self.assertEqual(_lowerCamelCase    ,     _lowerCamelCase )

     tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
     lowerCAmelCase_			=      tokenizer.encode(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.convert_ids_to_tokens(encoded[1] )[0]
     self.assertNotEqual(_lowerCamelCase    ,     _lowerCamelCase )

     # Testing spaces after special tokens
     lowerCAmelCase_			=      '''<mask>'''
     tokenizer.add_special_tokens(
         {'''mask_token''': AddedToken(_lowerCamelCase    ,     lstrip=_lowerCamelCase    ,     rstrip=_lowerCamelCase )} )  # mask token has a left space
     lowerCAmelCase_			=      tokenizer.convert_tokens_to_ids(_lowerCamelCase )

     lowerCAmelCase_			=      '''Encode <mask> sequence'''
     lowerCAmelCase_			=      '''Encode <mask>sequence'''

     lowerCAmelCase_			=      tokenizer.encode(_lowerCamelCase )
     lowerCAmelCase_			=      encoded.index(_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
     self.assertEqual(_lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      tokenizer.encode(_lowerCamelCase )
     lowerCAmelCase_			=      encoded.index(_lowerCamelCase )
     lowerCAmelCase_			=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
     self.assertNotEqual(_lowerCamelCase    ,     _lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     pass





  def 						UpperCAmelCase_	(	self ):
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(_lowerCamelCase    ,     **_lowerCamelCase )
           lowerCAmelCase_			=      self.tokenizer_class.from_pretrained(_lowerCamelCase    ,     **_lowerCamelCase )
           lowerCAmelCase_			=      '''A, <mask> AllenNLP sentence.'''
           lowerCAmelCase_			=      tokenizer_r.encode_plus(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     return_token_type_ids=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_p.encode_plus(_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase    ,     return_token_type_ids=_lowerCamelCase )

           # token_type_ids should put 0 everywhere
           self.assertEqual(sum(tokens_r['''token_type_ids'''] )    ,     sum(tokens_p['''token_type_ids'''] ) )

           # attention_mask should put 1 everywhere, so sum over length should be 1
           self.assertEqual(
               sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )    ,     sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )    ,     )

           lowerCAmelCase_			=      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
           lowerCAmelCase_			=      tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )

           # Rust correctly handles the space before the mask while python doesnt
           self.assertSequenceEqual(tokens_p['''input_ids''']    ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
           self.assertSequenceEqual(tokens_r['''input_ids''']    ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )

           self.assertSequenceEqual(
               _lowerCamelCase    ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
           self.assertSequenceEqual(
               _lowerCamelCase    ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )





  def 						UpperCAmelCase_	(	self ):
     for trim_offsets, add_prefix_space in itertools.product([True, False]    ,     repeat=2 ):
        lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
            self.tmpdirname    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )

        lowerCAmelCase_			=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
        lowerCAmelCase_			=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

        self.assertEqual(pre_tokenizer_state['''add_prefix_space''']    ,     _lowerCamelCase )

        self.assertEqual(post_processor_state['''add_prefix_space''']    ,     _lowerCamelCase )
        self.assertEqual(post_processor_state['''trim_offsets''']    ,     _lowerCamelCase )







  def 						UpperCAmelCase_	(	self ):
     # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
     # `trim_offsets`
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
           lowerCAmelCase_			=      '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
           lowerCAmelCase_			=      F'''{text_of_1_token} {text_of_1_token}'''

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (0, len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (len(_lowerCamelCase ) + 1, len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (0, len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (len(_lowerCamelCase ) + 1, len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (0, len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (len(_lowerCamelCase ), len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (0, len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (len(_lowerCamelCase ), len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )

           lowerCAmelCase_			=      F''' {text}'''

           # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
           #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
           # )
           # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
           # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
           # self.assertEqual(
           #     encoding.offset_mapping[1],
           #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
           # )

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (1, 1 + len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (1 + len(_lowerCamelCase ) + 1, 1 + len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (0, 1 + len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (1 + len(_lowerCamelCase ), 1 + len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )

           lowerCAmelCase_			=      self.rust_tokenizer_class.from_pretrained(
               _lowerCamelCase    ,     use_fast=_lowerCamelCase    ,     add_prefix_space=_lowerCamelCase    ,     trim_offsets=_lowerCamelCase )
           lowerCAmelCase_			=      tokenizer_r(_lowerCamelCase    ,     return_offsets_mapping=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase )
           self.assertEqual(encoding.offset_mapping[0]    ,     (0, 1 + len(_lowerCamelCase )) )
           self.assertEqual(
               encoding.offset_mapping[1]    ,     (1 + len(_lowerCamelCase ), 1 + len(_lowerCamelCase ) + 1 + len(_lowerCamelCase ))    ,     )






'''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





