

"""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       lowerCamelCase  (_UpperCamelCase	,unittest.TestCase	):
			lowerCamelCase__      :						Tuple    						=							RobertaTokenizer
			lowerCamelCase__      :						List[str]    						=							RobertaTokenizerFast
			lowerCamelCase__      :						int    						=							True
			lowerCamelCase__      :						Optional[Any]    						=							{"""cls_token""": """<s>"""}

			def     SCREAMING_SNAKE_CASE (    self :  str				)      ->			Tuple:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						SCREAMING_SNAKE_CASE__									=					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """\u0120""",
						    """\u0120l""",
						    """\u0120n""",
						    """\u0120lo""",
						    """\u0120low""",
						    """er""",
						    """\u0120lowest""",
						    """\u0120newer""",
						    """\u0120wider""",
						    """<unk>""",
						]
						SCREAMING_SNAKE_CASE__									=					dict(zip(__lowerCamelCase			, range(len(__lowerCamelCase				)				)				)				)
						SCREAMING_SNAKE_CASE__									=					["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
						SCREAMING_SNAKE_CASE__									=					{"""unk_token""": """<unk>"""}

						SCREAMING_SNAKE_CASE__									=					os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES["""vocab_file"""]				)
						SCREAMING_SNAKE_CASE__									=					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     SCREAMING_SNAKE_CASE (    self :  Any			, **__UpperCAmelCase :  str				)      ->			Any:
						kwargs.update(self.special_tokens_map				)
						return self.tokenizer_class.from_pretrained(self.tmpdirname			, **__lowerCamelCase				)

			def     SCREAMING_SNAKE_CASE (    self :  Optional[int]			, **__UpperCAmelCase :  List[Any]				)      ->			List[Any]:
						kwargs.update(self.special_tokens_map				)
						return RobertaTokenizerFast.from_pretrained(self.tmpdirname			, **__lowerCamelCase				)

			def     SCREAMING_SNAKE_CASE (    self :  str			, __UpperCAmelCase :  List[str]				)      ->			Any:
						SCREAMING_SNAKE_CASE__									=					"""lower newer"""
						SCREAMING_SNAKE_CASE__									=					"""lower newer"""
						return input_text, output_text

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

						SCREAMING_SNAKE_CASE__									=					tokens + [tokenizer.unk_token]
						SCREAMING_SNAKE_CASE__									=					[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase				)			, __lowerCamelCase				)

			def     SCREAMING_SNAKE_CASE (    self :  List[str]				)      ->			Tuple:
						SCREAMING_SNAKE_CASE__									=					self.get_tokenizer()

						self.assertListEqual(tokenizer.encode("""Hello world!"""			, add_special_tokens=__lowerCamelCase				)			, [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=__lowerCamelCase				)			, [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     SCREAMING_SNAKE_CASE (    self :  Dict				)      ->			List[Any]:
						SCREAMING_SNAKE_CASE__									=					self.tokenizer_class.from_pretrained("""roberta-base"""				)

						SCREAMING_SNAKE_CASE__									=					tokenizer.encode("""sequence builders"""			, add_special_tokens=__lowerCamelCase				)
						SCREAMING_SNAKE_CASE__									=					tokenizer.encode("""multi-sequence build"""			, add_special_tokens=__lowerCamelCase				)

						SCREAMING_SNAKE_CASE__									=					tokenizer.encode(
						    """sequence builders"""			, add_special_tokens=__lowerCamelCase			, add_prefix_space=__lowerCamelCase				)
						SCREAMING_SNAKE_CASE__									=					tokenizer.encode(
						    """sequence builders"""			, """multi-sequence build"""			, add_special_tokens=__lowerCamelCase			, add_prefix_space=__lowerCamelCase				)

						SCREAMING_SNAKE_CASE__									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase				)
						SCREAMING_SNAKE_CASE__									=					tokenizer.build_inputs_with_special_tokens(__lowerCamelCase			, __lowerCamelCase				)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode

			def     SCREAMING_SNAKE_CASE (    self :  Dict				)      ->			Union[str, Any]:
						SCREAMING_SNAKE_CASE__									=					self.get_tokenizer()

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

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

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

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

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

						SCREAMING_SNAKE_CASE__									=					"""Encode <mask> sequence"""
						SCREAMING_SNAKE_CASE__									=					"""Encode <mask>sequence"""

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

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

			def     SCREAMING_SNAKE_CASE (    self :  int				)      ->			int:
						pass

			def     SCREAMING_SNAKE_CASE (    self :  Optional[Any]				)      ->			Any:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(__lowerCamelCase			, **__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					self.tokenizer_class.from_pretrained(__lowerCamelCase			, **__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					"""A, <mask> AllenNLP sentence."""
												SCREAMING_SNAKE_CASE__									=					tokenizer_r.encode_plus(__lowerCamelCase			, add_special_tokens=__lowerCamelCase			, return_token_type_ids=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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"""]				)			, )

												SCREAMING_SNAKE_CASE__									=					tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
												SCREAMING_SNAKE_CASE__									=					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(
												    __lowerCamelCase			, ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
												self.assertSequenceEqual(
												    __lowerCamelCase			, ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)

			def     SCREAMING_SNAKE_CASE (    self :  Optional[Any]				)      ->			Optional[int]:
						for trim_offsets, add_prefix_space in itertools.product([True, False]			, repeat=2				):
									SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)

									SCREAMING_SNAKE_CASE__									=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
									SCREAMING_SNAKE_CASE__									=					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     SCREAMING_SNAKE_CASE (    self :  List[str]				)      ->			Optional[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})"""				):
												SCREAMING_SNAKE_CASE__									=					"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
												SCREAMING_SNAKE_CASE__									=					F"""{text_of_1_token} {text_of_1_token}"""

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )

												SCREAMING_SNAKE_CASE__									=					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)),
												# )

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )

												SCREAMING_SNAKE_CASE__									=					self.rust_tokenizer_class.from_pretrained(
												    __lowerCamelCase			, use_fast=__lowerCamelCase			, add_prefix_space=__lowerCamelCase			, trim_offsets=__lowerCamelCase				)
												SCREAMING_SNAKE_CASE__									=					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				))			, )








from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






