






"""simple docstring"""



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    _UpperCamelCase		:  Optional[int]						=		'pt'
elif is_tf_available():
    _UpperCamelCase		:  Any						=		'tf'
else:
    _UpperCamelCase		:  List[str]						=		'jax'

class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Union[str, Any]	     =ByTaTokenizer
    UpperCAmelCase_ :     Any	     =False







    def 			UpperCamelCase_      (			self       ):
      super().setUp()
      lowercase							    =       ByTaTokenizer()
      tokenizer.save_pretrained(self.tmpdirname       )







    @cached_property
    def 			UpperCamelCase_      (			self       ):
      return ByTaTokenizer.from_pretrained('google/byt5-small'       )







    def 			UpperCamelCase_      (			self							,		**_lowerCamelCase       ):
      return self.tokenizer_class.from_pretrained(self.tmpdirname							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=2_0							,		_lowerCamelCase=5       ):
      # XXX The default common tokenizer tests assume that every ID is decodable on its own.
      # This assumption is invalid for ByT5 because single bytes might not be
      # valid utf-8 (byte 128 for instance).
      # Here we're overriding the smallest possible method to provide
      # a clean sequence without making the same assumption.

      lowercase							    =       []
      for i in range(len(_lowerCamelCase       )       ):
        try:
          lowercase							    =       tokenizer.decode([i]							,		clean_up_tokenization_spaces=_lowerCamelCase       )
        except UnicodeDecodeError:
          pass
        toks.append((i, tok)       )

      lowercase							    =       list(filter(lambda _lowerCamelCase       : re.match(R'^[ a-zA-Z]+$'							,		t[1]       )							,		_lowerCamelCase       )       )
      lowercase							    =       list(filter(lambda _lowerCamelCase       : [t[0]] == tokenizer.encode(t[1]							,		add_special_tokens=_lowerCamelCase       )							,		_lowerCamelCase       )       )
      if max_length is not None and len(_lowerCamelCase       ) > max_length:
        lowercase							    =       toks[:max_length]
      if min_length is not None and len(_lowerCamelCase       ) < min_length and len(_lowerCamelCase       ) > 0:
        while len(_lowerCamelCase       ) < min_length:
          lowercase							    =       toks + toks
        # toks_str = [t[1] for t in toks]
      lowercase							    =       [t[0] for t in toks]

      # Ensure consistency
      lowercase							    =       tokenizer.decode(_lowerCamelCase							,		clean_up_tokenization_spaces=_lowerCamelCase       )
      if " " not in output_txt and len(_lowerCamelCase       ) > 1:
        lowercase							    =       (
            tokenizer.decode([toks_ids[0]]							,		clean_up_tokenization_spaces=_lowerCamelCase       )
            + ' '
            + tokenizer.decode(toks_ids[1:]							,		clean_up_tokenization_spaces=_lowerCamelCase       )
        )
      if with_prefix_space:
        lowercase							    =       ' ' + output_txt
      lowercase							    =       tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
      return output_txt, output_ids







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.ta_base_tokenizer
      lowercase							    =       tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']       )
      lowercase							    =       tokenizer(['hi', 'I went to the gym', '']       )
      self.assertListEqual(batch_with_eos_added['input_ids']							,		batch_without_eos_added['input_ids']       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.ta_base_tokenizer
      lowercase							    =       'Unicode €.'
      lowercase							    =       tokenizer(_lowerCamelCase       )
      lowercase							    =       [8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
      self.assertEqual(encoded['input_ids']							,		_lowerCamelCase       )

      # decoding
      lowercase							    =       tokenizer.decode(_lowerCamelCase       )
      self.assertEqual(_lowerCamelCase							,		'Unicode €.</s>'       )

      lowercase							    =       tokenizer('e è é ê ë'       )
      lowercase							    =       [1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
      self.assertEqual(encoded['input_ids']							,		_lowerCamelCase       )
      # decoding
      lowercase							    =       tokenizer.decode(_lowerCamelCase       )
      self.assertEqual(_lowerCamelCase							,		'e è é ê ë</s>'       )

      # encode/decode, but with `encode` instead of `__call__`
      self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'       )       )							,		'e è é ê ë</s>'       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.ta_base_tokenizer
      lowercase							    =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      # fmt: off
      lowercase							    =       [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
      # fmt: on
      lowercase							    =       tokenizer(_lowerCamelCase							,		padding=_lowerCamelCase							,		return_tensors=_lowerCamelCase       )
      self.assertIsInstance(_lowerCamelCase							,		_lowerCamelCase       )

      if FRAMEWORK != "jax":
        lowercase							    =       list(batch.input_ids.numpy()[0]       )
      else:
        lowercase							    =       list(batch.input_ids.tolist()[0]       )

      self.assertListEqual(_lowerCamelCase							,		_lowerCamelCase       )

      self.assertEqual((2, 3_7)							,		batch.input_ids.shape       )
      self.assertEqual((2, 3_7)							,		batch.attention_mask.shape       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.ta_base_tokenizer
      lowercase							    =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      lowercase							    =       tokenizer(_lowerCamelCase							,		padding=_lowerCamelCase							,		return_tensors=_lowerCamelCase       )
      # check if input_ids are returned and no decoder_input_ids
      self.assertIn('input_ids'							,		_lowerCamelCase       )
      self.assertIn('attention_mask'							,		_lowerCamelCase       )
      self.assertNotIn('decoder_input_ids'							,		_lowerCamelCase       )
      self.assertNotIn('decoder_attention_mask'							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.ta_base_tokenizer
      lowercase							    =       [
          'Summary of the text.',
          'Another summary.',
      ]
      lowercase							    =       tokenizer(
          text_target=_lowerCamelCase							,		max_length=3_2							,		padding='max_length'							,		truncation=_lowerCamelCase							,		return_tensors=_lowerCamelCase       )
      self.assertEqual(3_2							,		targets['input_ids'].shape[1]       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.ta_base_tokenizer
      lowercase							    =       ['A long paragraph for summarization. </s>']
      lowercase							    =       ['Summary of the text. </s>']
      # fmt: off
      lowercase							    =       [6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
      lowercase							    =       [8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
      # fmt: on

      lowercase							    =       tokenizer(_lowerCamelCase							,		text_target=_lowerCamelCase       )

      self.assertEqual(_lowerCamelCase							,		batch['input_ids'][0]       )
      self.assertEqual(_lowerCamelCase							,		batch['labels'][0]       )







    def 			UpperCamelCase_      (			self       ):
      # safety check on max_len default value so we are sure the test works
      lowercase							    =       self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'       ):
          self.assertNotEqual(tokenizer.model_max_length							,		4_2       )

        # Now let's start the test
      lowercase							    =       self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'       ):
          # Isolate this from the other tests because we save additional tokens/etc
          lowercase							    =       tempfile.mkdtemp()

          lowercase							    =       ' He is very happy, UNwant\u00E9d,running'
          lowercase							    =       tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
          tokenizer.save_pretrained(_lowerCamelCase       )

          lowercase							    =       tokenizer.__class__.from_pretrained(_lowerCamelCase       )
          lowercase							    =       after_tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
          self.assertListEqual(_lowerCamelCase							,		_lowerCamelCase       )

          shutil.rmtree(_lowerCamelCase       )

      lowercase							    =       self.get_tokenizers(model_max_length=4_2       )
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'       ):
          # Isolate this from the other tests because we save additional tokens/etc
          lowercase							    =       tempfile.mkdtemp()

          lowercase							    =       ' He is very happy, UNwant\u00E9d,running'
          tokenizer.add_tokens(['bim', 'bambam']       )
          lowercase							    =       tokenizer.additional_special_tokens
          additional_special_tokens.append('new_additional_special_token'       )
          tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}       )
          lowercase							    =       tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
          tokenizer.save_pretrained(_lowerCamelCase       )

          lowercase							    =       tokenizer.__class__.from_pretrained(_lowerCamelCase       )
          lowercase							    =       after_tokenizer.encode(_lowerCamelCase							,		add_special_tokens=_lowerCamelCase       )
          self.assertListEqual(_lowerCamelCase							,		_lowerCamelCase       )
          self.assertIn('new_additional_special_token'							,		after_tokenizer.additional_special_tokens       )
          self.assertEqual(after_tokenizer.model_max_length							,		4_2       )

          lowercase							    =       tokenizer.__class__.from_pretrained(_lowerCamelCase							,		model_max_length=4_3       )
          self.assertEqual(tokenizer.model_max_length							,		4_3       )

          shutil.rmtree(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       []
      if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

      if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

      for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer_utils.save_pretrained(_lowerCamelCase       )

          with open(os.path.join(_lowerCamelCase							,		'special_tokens_map.json'       )							,		encoding='utf-8'       ) as json_file:
            lowercase							    =       json.load(_lowerCamelCase       )

          with open(os.path.join(_lowerCamelCase							,		'tokenizer_config.json'       )							,		encoding='utf-8'       ) as json_file:
            lowercase							    =       json.load(_lowerCamelCase       )

          lowercase							    =       [F'<extra_id_{i}>' for i in range(1_2_5       )]

          lowercase							    =       added_tokens_extra_ids + [
              'an_additional_special_token'
          ]
          lowercase							    =       added_tokens_extra_ids + [
              'an_additional_special_token'
          ]

          with open(os.path.join(_lowerCamelCase							,		'special_tokens_map.json'       )							,		'w'							,		encoding='utf-8'       ) as outfile:
            json.dump(_lowerCamelCase							,		_lowerCamelCase       )
          with open(os.path.join(_lowerCamelCase							,		'tokenizer_config.json'       )							,		'w'							,		encoding='utf-8'       ) as outfile:
            json.dump(_lowerCamelCase							,		_lowerCamelCase       )

          # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
          # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
          # "special_tokens_map.json" files
          lowercase							    =       tokenizer_class.from_pretrained(
              _lowerCamelCase							,		)
          self.assertIn(
              'an_additional_special_token'							,		tokenizer_without_change_in_init.additional_special_tokens       )
          # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
          self.assertEqual(
              ['an_additional_special_token']							,		tokenizer_without_change_in_init.convert_ids_to_tokens(
                  tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']       )       )							,		)

          # Now we test that we can change the value of additional_special_tokens in the from_pretrained
          lowercase							    =       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'							,		lstrip=_lowerCamelCase       )]
          lowercase							    =       tokenizer_class.from_pretrained(
              _lowerCamelCase							,		additional_special_tokens=_lowerCamelCase							,		)

          self.assertIn('a_new_additional_special_token'							,		tokenizer.additional_special_tokens       )
          self.assertEqual(
              ['a_new_additional_special_token']							,		tokenizer.convert_ids_to_tokens(
                  tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']       )       )							,		)







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       []
      if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())       )

      if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())       )

      for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer_utils.save_pretrained(_lowerCamelCase       )

          lowercase							    =       tokenizer_class.from_pretrained(_lowerCamelCase       )

          self.assertTrue(tokenizer.decode([2_5_5]       ) == ''       )







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      pass







    def 			UpperCamelCase_      (			self       ):
      # The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
      # and special added tokens as tokens
      lowercase							    =       self.get_tokenizers(fast=_lowerCamelCase							,		do_lower_case=_lowerCamelCase       )
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'       ):
          lowercase							    =       ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
          lowercase							    =       tokenizer.convert_tokens_to_string(_lowerCamelCase       )

          self.assertIsInstance(_lowerCamelCase							,		_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'       ):
          lowercase							    =       [
              'bos_token',
              'eos_token',
              'unk_token',
              'sep_token',
              'pad_token',
              'cls_token',
              'mask_token',
          ]

          lowercase							    =       0
          lowercase							    =       tokenizer.convert_ids_to_tokens(
              _lowerCamelCase							,		skip_special_tokens=_lowerCamelCase       )

          for attr in attributes_list:
            setattr(_lowerCamelCase							,		attr + '_id'							,		_lowerCamelCase       )
            self.assertEqual(getattr(_lowerCamelCase							,		_lowerCamelCase       )							,		_lowerCamelCase       )
            self.assertEqual(getattr(_lowerCamelCase							,		attr + '_id'       )							,		_lowerCamelCase       )

            setattr(_lowerCamelCase							,		attr + '_id'							,		_lowerCamelCase       )
            self.assertEqual(getattr(_lowerCamelCase							,		_lowerCamelCase       )							,		_lowerCamelCase       )
            self.assertEqual(getattr(_lowerCamelCase							,		attr + '_id'       )							,		_lowerCamelCase       )

          setattr(_lowerCamelCase							,		'additional_special_tokens_ids'							,		[]       )
          self.assertListEqual(getattr(_lowerCamelCase							,		'additional_special_tokens'       )							,		[]       )
          self.assertListEqual(getattr(_lowerCamelCase							,		'additional_special_tokens_ids'       )							,		[]       )

          setattr(_lowerCamelCase							,		'additional_special_tokens_ids'							,		[token_id_to_test_setters]       )
          self.assertListEqual(getattr(_lowerCamelCase							,		'additional_special_tokens'       )							,		[token_to_test_setters]       )
          self.assertListEqual(getattr(_lowerCamelCase							,		'additional_special_tokens_ids'       )							,		[token_id_to_test_setters]       )










"""simple docstring"""



from typing import TYPE_CHECKING

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


_UpperCamelCase		:  Dict						=		{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		['RobertaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Tuple						=		[
        'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RobertaForCausalLM',
        'RobertaForMaskedLM',
        'RobertaForMultipleChoice',
        'RobertaForQuestionAnswering',
        'RobertaForSequenceClassification',
        'RobertaForTokenClassification',
        'RobertaModel',
        'RobertaPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  List[Any]						=		[
        'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRobertaForCausalLM',
        'TFRobertaForMaskedLM',
        'TFRobertaForMultipleChoice',
        'TFRobertaForQuestionAnswering',
        'TFRobertaForSequenceClassification',
        'TFRobertaForTokenClassification',
        'TFRobertaMainLayer',
        'TFRobertaModel',
        'TFRobertaPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Optional[int]						=		[
        'FlaxRobertaForCausalLM',
        'FlaxRobertaForMaskedLM',
        'FlaxRobertaForMultipleChoice',
        'FlaxRobertaForQuestionAnswering',
        'FlaxRobertaForSequenceClassification',
        'FlaxRobertaForTokenClassification',
        'FlaxRobertaModel',
        'FlaxRobertaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
    from .tokenization_roberta import RobertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_roberta_fast import RobertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta import (
            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaForCausalLM,
            RobertaForMaskedLM,
            RobertaForMultipleChoice,
            RobertaForQuestionAnswering,
            RobertaForSequenceClassification,
            RobertaForTokenClassification,
            RobertaModel,
            RobertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta import (
            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaForCausalLM,
            TFRobertaForMaskedLM,
            TFRobertaForMultipleChoice,
            TFRobertaForQuestionAnswering,
            TFRobertaForSequenceClassification,
            TFRobertaForTokenClassification,
            TFRobertaMainLayer,
            TFRobertaModel,
            TFRobertaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta import (
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaModel,
            FlaxRobertaPreTrainedModel,
        )

else:
    import sys

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


