'''simple docstring'''






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 (       __lowerCAmelCase						,  unittest.TestCase				):



		"""simple docstring"""
		__lowerCAmelCase				      =					XLMTokenizer
		__lowerCAmelCase				      =					False







		def    lowercase_  (							self					):

				'''simple docstring'''







				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""",
				    """w</w>""",
				    """r</w>""",
				    """t</w>""",
				    """lo""",
				    """low""",
				    """er</w>""",
				    """low</w>""",
				    """lowest</w>""",
				    """newer</w>""",
				    """wider</w>""",
				    """<unk>""",
				]
				__UpperCAmelCase:					Any		= dict(zip(snake_case_   ,    range(len(snake_case_					)					)					)					)
				__UpperCAmelCase:					Union[str, Any]		= ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

				__UpperCAmelCase:					Any		= os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["""vocab_file"""]					)
				__UpperCAmelCase:					Optional[Any]		= os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["""merges_file"""]					)
				with open(self.vocab_file   ,    """w"""					) as fp:
						fp.write(json.dumps(snake_case_					)					)
				with open(self.merges_file   ,    """w"""					) as fp:
						fp.write("""\n""".join(snake_case_					)					)







		def    lowercase_  (							self   ,    snake_case_					):

				'''simple docstring'''







				__UpperCAmelCase:					Dict		= """lower newer"""
				__UpperCAmelCase:					Dict		= """lower newer"""
				return input_text, output_text







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					Tuple		= XLMTokenizer(self.vocab_file   ,    self.merges_file					)

				__UpperCAmelCase:					Dict		= """lower"""
				__UpperCAmelCase:					List[Any]		= ["""low""", """er</w>"""]
				__UpperCAmelCase:					List[Any]		= tokenizer.tokenize(snake_case_					)
				self.assertListEqual(snake_case_   ,    snake_case_					)

				__UpperCAmelCase:					int		= tokens + ["""<unk>"""]
				__UpperCAmelCase:					Dict		= [14, 15, 20]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_					)   ,    snake_case_					)







		@slow
		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					List[str]		= XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""					)

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

				__UpperCAmelCase:					Union[str, Any]		= tokenizer.build_inputs_with_special_tokens(snake_case_					)
				__UpperCAmelCase:					Optional[Any]		= tokenizer.build_inputs_with_special_tokens(snake_case_   ,    snake_case_					)

				assert encoded_sentence == [0] + text + [1]
				assert encoded_pair == [0] + text + [1] + text_a + [1]
'''simple docstring'''






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RemBertForCausalLM',
        'RemBertForMaskedLM',
        'RemBertForMultipleChoice',
        'RemBertForQuestionAnswering',
        'RemBertForSequenceClassification',
        'RemBertForTokenClassification',
        'RemBertLayer',
        'RemBertModel',
        'RemBertPreTrainedModel',
        'load_tf_weights_in_rembert',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRemBertForCausalLM',
        'TFRemBertForMaskedLM',
        'TFRemBertForMultipleChoice',
        'TFRemBertForQuestionAnswering',
        'TFRemBertForSequenceClassification',
        'TFRemBertForTokenClassification',
        'TFRemBertLayer',
        'TFRemBertModel',
        'TFRemBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert import RemBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert_fast import RemBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rembert import (
            REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RemBertForCausalLM,
            RemBertForMaskedLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
            RemBertLayer,
            RemBertModel,
            RemBertPreTrainedModel,
            load_tf_weights_in_rembert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)