






'''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 						SCREAMING_SNAKE_CASE						(   __snake_case     , unittest.TestCase	):
       snake_case__     :  Dict 		=      RobertaTokenizer
       snake_case__     :  Union[str, Any] 		=      RobertaTokenizerFast
       snake_case__     :  List[str] 		=      True
       snake_case__     :  int 		=      {"cls_token": "<s>"}




       def        a_       (       self   :			Optional[int]       ):


            """simple docstring"""







            super().setUp()

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

            __lowerCamelCase    :							Optional[int]					 =					os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]       )
            __lowerCamelCase    :							List[str]					 =					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        a_       (       self   :			int						,     **A__   :			List[str]       ):


            """simple docstring"""







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




       def        a_       (       self   :			int						,     **A__   :			Tuple       ):


            """simple docstring"""







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




       def        a_       (       self   :			Union[str, Any]						,     A__   :			str       ):


            """simple docstring"""







            __lowerCamelCase    :							List[Any]					 =					'''lower newer'''
            __lowerCamelCase    :							str					 =					'''lower newer'''
            return input_text, output_text




       def        a_       (       self   :			int       ):


            """simple docstring"""







            __lowerCamelCase    :							List[Any]					 =					self.tokenizer_class(self.vocab_file						,     self.merges_file						,     **self.special_tokens_map       )
            __lowerCamelCase    :							Any					 =					'''lower newer'''
            __lowerCamelCase    :							Any					 =					['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
            __lowerCamelCase    :							Dict					 =					tokenizer.tokenize(A__       )  # , add_prefix_space=True)
            self.assertListEqual(A__						,     A__       )

            __lowerCamelCase    :							Optional[Any]					 =					tokens + [tokenizer.unk_token]
            __lowerCamelCase    :							Optional[Any]					 =					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A__       )						,     A__       )




       def        a_       (       self   :			List[str]       ):


            """simple docstring"""







            __lowerCamelCase    :							Optional[Any]					 =					self.get_tokenizer()

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




       @slow
       def        a_       (       self   :			Optional[int]       ):


            """simple docstring"""







            __lowerCamelCase    :							str					 =					self.tokenizer_class.from_pretrained("""roberta-base"""       )

            __lowerCamelCase    :							Union[str, Any]					 =					tokenizer.encode("""sequence builders"""						,     add_special_tokens=A__       )
            __lowerCamelCase    :							Dict					 =					tokenizer.encode("""multi-sequence build"""						,     add_special_tokens=A__       )

            __lowerCamelCase    :							List[str]					 =					tokenizer.encode(
                """sequence builders"""						,     add_special_tokens=A__						,     add_prefix_space=A__       )
            __lowerCamelCase    :							Union[str, Any]					 =					tokenizer.encode(
                """sequence builders"""						,     """multi-sequence build"""						,     add_special_tokens=A__						,     add_prefix_space=A__       )

            __lowerCamelCase    :							List[Any]					 =					tokenizer.build_inputs_with_special_tokens(A__       )
            __lowerCamelCase    :							Dict					 =					tokenizer.build_inputs_with_special_tokens(A__						,     A__       )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




       def        a_       (       self   :			str       ):


            """simple docstring"""







            __lowerCamelCase    :							Optional[Any]					 =					self.get_tokenizer()

            __lowerCamelCase    :							List[Any]					 =					'''Encode this sequence.'''
            __lowerCamelCase    :							Any					 =					tokenizer.byte_encoder[''' '''.encode("""utf-8"""       )[0]]

            # Testing encoder arguments
            __lowerCamelCase    :							Optional[int]					 =					tokenizer.encode(A__						,     add_special_tokens=A__						,     add_prefix_space=A__       )
            __lowerCamelCase    :							Optional[Any]					 =					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
            self.assertNotEqual(A__						,     A__       )

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

            tokenizer.add_special_tokens({"""bos_token""": """<s>"""}       )
            __lowerCamelCase    :							Dict					 =					tokenizer.encode(A__						,     add_special_tokens=A__       )
            __lowerCamelCase    :							str					 =					tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
            self.assertNotEqual(A__						,     A__       )

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

            __lowerCamelCase    :							int					 =					'''Encode <mask> sequence'''
            __lowerCamelCase    :							List[str]					 =					'''Encode <mask>sequence'''

            __lowerCamelCase    :							str					 =					tokenizer.encode(A__       )
            __lowerCamelCase    :							str					 =					encoded.index(A__       )
            __lowerCamelCase    :							Dict					 =					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
            self.assertEqual(A__						,     A__       )

            __lowerCamelCase    :							Optional[Any]					 =					tokenizer.encode(A__       )
            __lowerCamelCase    :							str					 =					encoded.index(A__       )
            __lowerCamelCase    :							List[Any]					 =					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
            self.assertNotEqual(A__						,     A__       )




       def        a_       (       self   :			Optional[int]       ):


            """simple docstring"""







            pass




       def        a_       (       self   :			int       ):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                      __lowerCamelCase    :							Optional[int]					 =					self.rust_tokenizer_class.from_pretrained(A__						,     **A__       )
                      __lowerCamelCase    :							str					 =					self.tokenizer_class.from_pretrained(A__						,     **A__       )
                      __lowerCamelCase    :							Dict					 =					'''A, <mask> AllenNLP sentence.'''
                      __lowerCamelCase    :							Optional[Any]					 =					tokenizer_r.encode_plus(A__						,     add_special_tokens=A__						,     return_token_type_ids=A__       )
                      __lowerCamelCase    :							List[Any]					 =					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, 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(
                          A__						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )
                      self.assertSequenceEqual(
                          A__						,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )




       def        a_       (       self   :			Optional[Any]       ):


            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]						,     repeat=2       ):
                 __lowerCamelCase    :							Optional[Any]					 =					self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname						,     use_fast=A__						,     add_prefix_space=A__						,     trim_offsets=A__       )

                 __lowerCamelCase    :							List[str]					 =					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        a_       (       self   :			int       ):


            """simple docstring"""







            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                      __lowerCamelCase    :							Any					 =					'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                      __lowerCamelCase    :							Dict					 =					f"{text_of_1_token} {text_of_1_token}"

                      __lowerCamelCase    :							Dict					 =					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    :							Tuple					 =					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    :							List[str]					 =					self.rust_tokenizer_class.from_pretrained(
                          A__						,     use_fast=A__						,     add_prefix_space=A__						,     trim_offsets=A__       )
                      __lowerCamelCase    :							Any					 =					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    :							Tuple					 =					self.rust_tokenizer_class.from_pretrained(
                          A__						,     use_fast=A__						,     add_prefix_space=A__						,     trim_offsets=A__       )
                      __lowerCamelCase    :							Tuple					 =					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    :							Union[str, Any]					 =					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    :							Union[str, Any]					 =					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]						,     (1, 1 + len(A__       ))       )
                      self.assertEqual(
                          encoding.offset_mapping[1]						,     (1 + len(A__       ) + 1, 1 + len(A__       ) + 1 + len(A__       ))						,     )

                      __lowerCamelCase    :							Dict					 =					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    :							List[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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase			:	Dict								={
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=['AlbertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Optional[int]								=[
	    'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AlbertForMaskedLM',
	    'AlbertForMultipleChoice',
	    'AlbertForPreTraining',
	    'AlbertForQuestionAnswering',
	    'AlbertForSequenceClassification',
	    'AlbertForTokenClassification',
	    'AlbertModel',
	    'AlbertPreTrainedModel',
	    'load_tf_weights_in_albert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	List[Any]								=[
	    'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFAlbertForMaskedLM',
	    'TFAlbertForMultipleChoice',
	    'TFAlbertForPreTraining',
	    'TFAlbertForQuestionAnswering',
	    'TFAlbertForSequenceClassification',
	    'TFAlbertForTokenClassification',
	    'TFAlbertMainLayer',
	    'TFAlbertModel',
	    'TFAlbertPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCamelCase			:	Any								=[
	    'FlaxAlbertForMaskedLM',
	    'FlaxAlbertForMultipleChoice',
	    'FlaxAlbertForPreTraining',
	    'FlaxAlbertForQuestionAnswering',
	    'FlaxAlbertForSequenceClassification',
	    'FlaxAlbertForTokenClassification',
	    'FlaxAlbertModel',
	    'FlaxAlbertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert import AlbertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_albert_fast import AlbertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_albert import (
		    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AlbertForMaskedLM,
		    AlbertForMultipleChoice,
		    AlbertForPreTraining,
		    AlbertForQuestionAnswering,
		    AlbertForSequenceClassification,
		    AlbertForTokenClassification,
		    AlbertModel,
		    AlbertPreTrainedModel,
		    load_tf_weights_in_albert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertMainLayer,
		    TFAlbertModel,
		    TFAlbertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_albert import (
		    FlaxAlbertForMaskedLM,
		    FlaxAlbertForMultipleChoice,
		    FlaxAlbertForPreTraining,
		    FlaxAlbertForQuestionAnswering,
		    FlaxAlbertForSequenceClassification,
		    FlaxAlbertForTokenClassification,
		    FlaxAlbertModel,
		    FlaxAlbertPreTrainedModel,
		)
else:
	import sys

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



