

import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  List[str]										=				XLMTokenizer
							_UpperCAmelCase   :  Optional[int]										=				False
							def    lowerCAmelCase					(		self		:      Optional[int]):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								__lowerCamelCase    :    Dict 	=			[
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    'w</w>',
								    'r</w>',
								    't</w>',
								    'lo',
								    'low',
								    'er</w>',
								    'low</w>',
								    'lowest</w>',
								    'newer</w>',
								    'wider</w>',
								    '<unk>',
								]
								__lowerCamelCase    :    Tuple 	=			dict(zip(SCREAMING_SNAKE_CASE__       ,range(len(SCREAMING_SNAKE_CASE__))))
								__lowerCamelCase    :    Optional[int] 	=			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

								__lowerCamelCase    :    Tuple 	=			os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['vocab_file'])
								__lowerCamelCase    :    Dict 	=			os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['merges_file'])
								with open(self.vocab_file       ,'w') as fp:
									fp.write(json.dumps(SCREAMING_SNAKE_CASE__))
								with open(self.merges_file       ,'w') as fp:
									fp.write('\n'.join(SCREAMING_SNAKE_CASE__))
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Tuple 	=			'lower newer'
								__lowerCamelCase    :    List[str] 	=			'lower newer'
								return input_text, output_text
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Any 	=			XLMTokenizer(self.vocab_file       ,self.merges_file)

								__lowerCamelCase    :    Optional[Any] 	=			'lower'
								__lowerCamelCase    :    Union[str, Any] 	=			['low', 'er</w>']
								__lowerCamelCase    :    Union[str, Any] 	=			tokenizer.tokenize(SCREAMING_SNAKE_CASE__)
								self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			tokens + ['<unk>']
								__lowerCamelCase    :    int 	=			[1_4, 1_5, 2_0]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__)


							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			XLMTokenizer.from_pretrained('xlm-mlm-en-2048')

								__lowerCamelCase    :    str 	=			tokenizer.encode('sequence builders'       ,add_special_tokens=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			tokenizer.encode('multi-sequence build'       ,add_special_tokens=SCREAMING_SNAKE_CASE__)

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

								assert encoded_sentence == [0] + text + [1]
								assert encoded_pair == [0] + text + [1] + text_a + [1]







from typing import TYPE_CHECKING

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


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

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """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:
					a		=[
					    """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:
					a		=[
					    """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

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




