
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        _UpperCamelCase				(					UpperCamelCase_     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = RobertaTokenizer
			__UpperCamelCase      :						Tuple					      = RobertaTokenizerFast
			__UpperCamelCase      :						Tuple					      = True
			__UpperCamelCase      :						Dict					      = {"""cls_token""": """<s>"""}

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							super().setUp()

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

							UpperCamelCase_:							Tuple	=						os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["""vocab_file"""]      )
							UpperCamelCase_:							Tuple	=						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       lowerCAmelCase__	(       self :       List[str]						,	**snake_case_ :       List[Any]      ):
							kwargs.update(self.special_tokens_map      )
							return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**__a      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	**snake_case_ :       List[Any]      ):
							kwargs.update(self.special_tokens_map      )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname						,	**__a      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						'lower newer'
							UpperCamelCase_:							Optional[int]	=						'lower newer'
							return input_text, output_text

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

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

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()

							self.assertListEqual(tokenizer.encode("""Hello world!"""						,	add_special_tokens=__a      )						,	[0, 3_1414, 232, 328, 2]      )
							self.assertListEqual(
							    tokenizer.encode("""Hello world! cécé herlolip 418"""						,	add_special_tokens=__a      )						,	[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]						,	)

			@slow
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							UpperCamelCase_:							Tuple	=						self.tokenizer_class.from_pretrained("""roberta-base"""      )

							UpperCamelCase_:							int	=						tokenizer.encode("""sequence builders"""						,	add_special_tokens=__a      )
							UpperCamelCase_:							Any	=						tokenizer.encode("""multi-sequence build"""						,	add_special_tokens=__a      )

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

							UpperCamelCase_:							str	=						tokenizer.build_inputs_with_special_tokens(__a      )
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.build_inputs_with_special_tokens(__a						,	__a      )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							str	=						self.get_tokenizer()

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

							# Testing encoder arguments
							UpperCamelCase_:							List[str]	=						tokenizer.encode(__a						,	add_special_tokens=__a						,	add_prefix_space=__a      )
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
							self.assertNotEqual(__a						,	__a      )

							UpperCamelCase_:							Tuple	=						tokenizer.encode(__a						,	add_special_tokens=__a						,	add_prefix_space=__a      )
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
							self.assertEqual(__a						,	__a      )

							tokenizer.add_special_tokens({"""bos_token""": """<s>"""}      )
							UpperCamelCase_:							Union[str, Any]	=						tokenizer.encode(__a						,	add_special_tokens=__a      )
							UpperCamelCase_:							List[str]	=						tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
							self.assertNotEqual(__a						,	__a      )

							# Testing spaces after special tokens
							UpperCamelCase_:							Dict	=						'<mask>'
							tokenizer.add_special_tokens(
							    {"""mask_token""": AddedToken(__a						,	lstrip=__a						,	rstrip=__a      )}      )  # mask token has a left space
							UpperCamelCase_:							List[str]	=						tokenizer.convert_tokens_to_ids(__a      )

							UpperCamelCase_:							int	=						'Encode <mask> sequence'
							UpperCamelCase_:							List[Any]	=						'Encode <mask>sequence'

							UpperCamelCase_:							str	=						tokenizer.encode(__a      )
							UpperCamelCase_:							str	=						encoded.index(__a      )
							UpperCamelCase_:							Optional[Any]	=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
							self.assertEqual(__a						,	__a      )

							UpperCamelCase_:							Dict	=						tokenizer.encode(__a      )
							UpperCamelCase_:							int	=						encoded.index(__a      )
							UpperCamelCase_:							Optional[int]	=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
							self.assertNotEqual(__a						,	__a      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							pass

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






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

															UpperCamelCase_:							List[str]	=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]      )
															UpperCamelCase_:							int	=						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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
															self.assertSequenceEqual(tokens_r["""input_ids"""]						,	[0, 250, 6, 5_0264, 3823, 487, 2_1992, 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       lowerCAmelCase__	(       self :       Tuple      ):






							for trim_offsets, add_prefix_space in itertools.product([True, False]						,	repeat=2      ):
											UpperCamelCase_:							Dict	=						self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )

											UpperCamelCase_:							Union[str, Any]	=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
											UpperCamelCase_:							Dict	=						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       lowerCAmelCase__	(       self :       List[str]      ):






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

															UpperCamelCase_:							Optional[Any]	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							Dict	=						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      ))						,	)

															UpperCamelCase_:							str	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							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      ) + 1, len(__a      ) + 1 + len(__a      ))						,	)

															UpperCamelCase_:							Dict	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							Optional[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      ))						,	)

															UpperCamelCase_:							Optional[Any]	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							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      ))						,	)

															UpperCamelCase_:							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)),
															# )

															UpperCamelCase_:							List[str]	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							List[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      ))						,	)

															UpperCamelCase_:							List[str]	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							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      ))						,	)

															UpperCamelCase_:							Union[str, Any]	=						self.rust_tokenizer_class.from_pretrained(
															    __a						,	use_fast=__a						,	add_prefix_space=__a						,	trim_offsets=__a      )
															UpperCamelCase_:							Dict	=						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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: str         =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Any         =  [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






