






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     _a							(	UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = RobertaTokenizer
  A_						       = RobertaTokenizerFast
  A_						       = True
  A_						       = {"""cls_token""": """<s>"""}




  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     super().setUp()

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

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




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			str:
     kwargs.update(self.special_tokens_map			)
     return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			int:
     kwargs.update(self.special_tokens_map			)
     return RobertaTokenizerFast.from_pretrained(self.tmpdirname		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[Any]:
     UpperCamelCase_						=  'lower newer'
     UpperCamelCase_						=  'lower newer'
     return input_text, output_text




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  self.tokenizer_class(self.vocab_file		,		self.merges_file		,		**self.special_tokens_map			)
     UpperCamelCase_						=  'lower newer'
     UpperCamelCase_						=  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
     UpperCamelCase_						=  tokenizer.tokenize(_UpperCAmelCase			)  # , add_prefix_space=True)
     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('Hello world!'		,		add_special_tokens=_UpperCAmelCase			)		,		[0, 31414, 232, 328, 2]			)
     self.assertListEqual(
         tokenizer.encode('Hello world! cécé herlolip 418'		,		add_special_tokens=_UpperCAmelCase			)		,		[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,		)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  self.tokenizer_class.from_pretrained('roberta-base'			)

     UpperCamelCase_						=  tokenizer.encode('sequence builders'		,		add_special_tokens=_UpperCAmelCase			)
     UpperCamelCase_						=  tokenizer.encode('multi-sequence build'		,		add_special_tokens=_UpperCAmelCase			)

     UpperCamelCase_						=  tokenizer.encode(
         'sequence builders'		,		add_special_tokens=_UpperCAmelCase		,		add_prefix_space=_UpperCAmelCase			)
     UpperCamelCase_						=  tokenizer.encode(
         'sequence builders'		,		'multi-sequence build'		,		add_special_tokens=_UpperCAmelCase		,		add_prefix_space=_UpperCAmelCase			)

     UpperCamelCase_						=  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			)
     UpperCamelCase_						=  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		,		_UpperCAmelCase			)

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  self.get_tokenizer()

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

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

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

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

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

     UpperCamelCase_						=  'Encode <mask> sequence'
     UpperCamelCase_						=  'Encode <mask>sequence'

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

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




  def  _UpperCAmelCase  (  self			)      ->			str:
     pass




  def  _UpperCAmelCase  (  self			)      ->			int:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
           UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase		,		**_UpperCAmelCase			)
           UpperCamelCase_						=  self.tokenizer_class.from_pretrained(_UpperCAmelCase		,		**_UpperCAmelCase			)
           UpperCamelCase_						=  'A, <mask> AllenNLP sentence.'
           UpperCamelCase_						=  tokenizer_r.encode_plus(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_token_type_ids=_UpperCAmelCase			)
           UpperCamelCase_						=  tokenizer_p.encode_plus(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_token_type_ids=_UpperCAmelCase			)

           # 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']			)		,		)

           UpperCamelCase_						=  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']			)
           UpperCamelCase_						=  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, 50264, 3823, 487, 21992, 3645, 4, 2]			)
           self.assertSequenceEqual(tokens_r['input_ids']		,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

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




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     for trim_offsets, add_prefix_space in itertools.product([True, False]		,		repeat=2			):
        UpperCamelCase_						=  self.rust_tokenizer_class.from_pretrained(
            self.tmpdirname		,		use_fast=_UpperCAmelCase		,		add_prefix_space=_UpperCAmelCase		,		trim_offsets=_UpperCAmelCase			)

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

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

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




  def  _UpperCAmelCase  (  self			)      ->			int:
     # 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})"""			):
           UpperCamelCase_						=  'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
           UpperCamelCase_						=  f"""{text_of_1_token} {text_of_1_token}"""

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

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

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

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

           UpperCamelCase_						=  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)),
           # )

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

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

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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__   :						Optional[int]													=   {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   [
     """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PegasusXForConditionalGeneration""",
     """PegasusXModel""",
     """PegasusXPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 snake_case__   :						Optional[Any]													=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





