

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__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						RobertaTokenizer
 _lowercase     :					Optional[Any]      =						RobertaTokenizerFast
 _lowercase     :					Optional[Any]      =						True
 _lowercase     :					Tuple      =						{'''cls_token''': '''<s>'''}

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowerCAmelCase   :					Union[str, Any]        =    [
          "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   :					List[Any]        =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
      _lowerCAmelCase   :					Any        =    {"unk_token": "<unk>"}

      _lowerCAmelCase   :					Optional[Any]        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					Any        =    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    __magic_name__       (		self :	Tuple     ,						**A_ :	int       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	int     ,						**A_ :	Tuple       ):






      '''simple docstring'''




      kwargs.update(self.special_tokens_map       )
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,						**A_       )

 def    __magic_name__       (		self :	str     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    "lower newer"
      _lowerCAmelCase   :					Optional[Any]        =    "lower newer"
      return input_text, output_text

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.tokenizer_class(self.vocab_file     ,						self.merges_file     ,						**self.special_tokens_map       )
      _lowerCAmelCase   :					List[Any]        =    "lower newer"
      _lowerCAmelCase   :					Tuple        =    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
      _lowerCAmelCase   :					str        =    tokenizer.tokenize(A_       )  # , add_prefix_space=True)
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Tuple        =    tokens + [tokenizer.unk_token]
      _lowerCAmelCase   :					Union[str, Any]        =    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )     ,						A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.get_tokenizer()

      self.assertListEqual(tokenizer.encode("Hello world!"     ,						add_special_tokens=A_       )     ,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]       )
      self.assertListEqual(
          tokenizer.encode("Hello world! cécé herlolip 418"     ,						add_special_tokens=A_       )     ,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]     ,						)

 @slow
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.tokenizer_class.from_pretrained("roberta-base"       )

      _lowerCAmelCase   :					Optional[int]        =    tokenizer.encode("sequence builders"     ,						add_special_tokens=A_       )
      _lowerCAmelCase   :					int        =    tokenizer.encode("multi-sequence build"     ,						add_special_tokens=A_       )

      _lowerCAmelCase   :					Tuple        =    tokenizer.encode(
          "sequence builders"     ,						add_special_tokens=A_     ,						add_prefix_space=A_       )
      _lowerCAmelCase   :					Optional[int]        =    tokenizer.encode(
          "sequence builders"     ,						"multi-sequence build"     ,						add_special_tokens=A_     ,						add_prefix_space=A_       )

      _lowerCAmelCase   :					List[str]        =    tokenizer.build_inputs_with_special_tokens(A_       )
      _lowerCAmelCase   :					Optional[int]        =    tokenizer.build_inputs_with_special_tokens(A_     ,						A_       )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.get_tokenizer()

      _lowerCAmelCase   :					Union[str, Any]        =    "Encode this sequence."
      _lowerCAmelCase   :					Optional[int]        =    tokenizer.byte_encoder[" ".encode("utf-8"       )[0]]

      # Testing encoder arguments
      _lowerCAmelCase   :					str        =    tokenizer.encode(A_     ,						add_special_tokens=A_     ,						add_prefix_space=A_       )
      _lowerCAmelCase   :					List[str]        =    tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
      self.assertNotEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Optional[int]        =    tokenizer.encode(A_     ,						add_special_tokens=A_     ,						add_prefix_space=A_       )
      _lowerCAmelCase   :					List[str]        =    tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
      self.assertEqual(A_     ,						A_       )

      tokenizer.add_special_tokens({"bos_token": "<s>"}       )
      _lowerCAmelCase   :					Any        =    tokenizer.encode(A_     ,						add_special_tokens=A_       )
      _lowerCAmelCase   :					Any        =    tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
      self.assertNotEqual(A_     ,						A_       )

      # Testing spaces after special tokens
      _lowerCAmelCase   :					Union[str, Any]        =    "<mask>"
      tokenizer.add_special_tokens(
          {"mask_token": AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       )}       )  # mask token has a left space
      _lowerCAmelCase   :					Union[str, Any]        =    tokenizer.convert_tokens_to_ids(A_       )

      _lowerCAmelCase   :					int        =    "Encode <mask> sequence"
      _lowerCAmelCase   :					str        =    "Encode <mask>sequence"

      _lowerCAmelCase   :					List[str]        =    tokenizer.encode(A_       )
      _lowerCAmelCase   :					Optional[int]        =    encoded.index(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
      self.assertEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    tokenizer.encode(A_       )
      _lowerCAmelCase   :					List[str]        =    encoded.index(A_       )
      _lowerCAmelCase   :					List[str]        =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
      self.assertNotEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      pass

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                _lowerCAmelCase   :					int        =    self.rust_tokenizer_class.from_pretrained(A_     ,						**A_       )
                _lowerCAmelCase   :					Optional[int]        =    self.tokenizer_class.from_pretrained(A_     ,						**A_       )
                _lowerCAmelCase   :					List[Any]        =    "A, <mask> AllenNLP sentence."
                _lowerCAmelCase   :					Optional[Any]        =    tokenizer_r.encode_plus(A_     ,						add_special_tokens=A_     ,						return_token_type_ids=A_       )
                _lowerCAmelCase   :					Optional[int]        =    tokenizer_p.encode_plus(A_     ,						add_special_tokens=A_     ,						return_token_type_ids=A_       )

                # 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   :					Optional[int]        =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
                _lowerCAmelCase   :					List[str]        =    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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
                self.assertSequenceEqual(tokens_r["input_ids"]     ,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )

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

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      for trim_offsets, add_prefix_space in itertools.product([True, False]     ,						repeat=2       ):
           _lowerCAmelCase   :					Tuple        =    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )

           _lowerCAmelCase   :					Optional[int]        =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
           _lowerCAmelCase   :					List[str]        =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

           self.assertEqual(pre_tokenizer_state["add_prefix_space"]     ,						A_       )

           self.assertEqual(post_processor_state["add_prefix_space"]     ,						A_       )
           self.assertEqual(post_processor_state["trim_offsets"]     ,						A_       )


 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                _lowerCAmelCase   :					Union[str, Any]        =    "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                _lowerCAmelCase   :					str        =    F'''{text_of_1_token} {text_of_1_token}'''

                _lowerCAmelCase   :					List[Any]        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					str        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(0, len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(len(A_       ) + 1, len(A_       ) + 1 + len(A_       ))     ,						)

                _lowerCAmelCase   :					str        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					List[str]        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(0, len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(len(A_       ) + 1, len(A_       ) + 1 + len(A_       ))     ,						)

                _lowerCAmelCase   :					int        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					int        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(0, len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(len(A_       ), len(A_       ) + 1 + len(A_       ))     ,						)

                _lowerCAmelCase   :					List[Any]        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					List[str]        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(0, len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(len(A_       ), len(A_       ) + 1 + len(A_       ))     ,						)

                _lowerCAmelCase   :					str        =    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   :					Any        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					str        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(1, 1 + len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(1 + len(A_       ) + 1, 1 + len(A_       ) + 1 + len(A_       ))     ,						)

                _lowerCAmelCase   :					Optional[int]        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					Optional[Any]        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(0, 1 + len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(1 + len(A_       ), 1 + len(A_       ) + 1 + len(A_       ))     ,						)

                _lowerCAmelCase   :					Any        =    self.rust_tokenizer_class.from_pretrained(
                    A_     ,						use_fast=A_     ,						add_prefix_space=A_     ,						trim_offsets=A_       )
                _lowerCAmelCase   :					Optional[int]        =    tokenizer_r(A_     ,						return_offsets_mapping=A_     ,						add_special_tokens=A_       )
                self.assertEqual(encoding.offset_mapping[0]     ,						(0, 1 + len(A_       ))       )
                self.assertEqual(
                    encoding.offset_mapping[1]     ,						(1 + len(A_       ), 1 + len(A_       ) + 1 + len(A_       ))     ,						)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






