
'''simple docstring'''







import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Tuple       =					TransfoXLTokenizer
		a							:					str       =					False
		a							:					List[Any]       =					False




		def     lowercase       (				self      ):
								super().setUp()

								_SCREAMING_SNAKE_CASE				= [
								    "<unk>",
								    "[CLS]",
								    "[SEP]",
								    "want",
								    "unwanted",
								    "wa",
								    "un",
								    "running",
								    ",",
								    "low",
								    "l",
								]
								_SCREAMING_SNAKE_CASE				= os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file					,						"w"					,						encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )




		def     lowercase       (				self					,						**UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname					,						**UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= "<unk> UNwanted , running"
								_SCREAMING_SNAKE_CASE				= "<unk> unwanted, running"
								return input_text, output_text




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TransfoXLTokenizer(vocab_file=self.vocab_file					,						lower_case=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer.tokenize("<unk> UNwanted , running"      )
								self.assertListEqual(UpperCamelCase					,						["<unk>", "unwanted", ",", "running"]      )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase      )					,						[0, 4, 8, 7]      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TransfoXLTokenizer(lower_case=UpperCamelCase      )

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "      )					,						["hello", "!", "how", "are", "you", "?"]      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TransfoXLTokenizer(lower_case=UpperCamelCase      )

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo ! how  \n Are yoU ?  "      )					,						["HeLLo", "!", "how", "Are", "yoU", "?"]      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TransfoXLTokenizer(lower_case=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= "Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
								_SCREAMING_SNAKE_CASE				= [
								    "Hello",
								    "(",
								    "bracket",
								    ")",
								    "and",
								    "side",
								    "@-@",
								    "scrolled",
								    "[",
								    "and",
								    "]",
								    "Henry",
								    "'s",
								    "$",
								    "5",
								    "@,@",
								    "000",
								    "with",
								    "3",
								    "@.@",
								    "34",
								    "m",
								    ".",
								    "What",
								    "'s",
								    "up",
								    "!",
								    "?",
								]

								self.assertListEqual(tokenizer.tokenize(UpperCamelCase      )					,						UpperCamelCase      )

								self.assertEqual(tokenizer.convert_tokens_to_string(UpperCamelCase      )					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()
								_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )

								tokenizer.add_tokens(["new1", "new2"]      )
								tokenizer.move_added_token("new1"					,						1      )

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(UpperCamelCase      )					,						original_len + 2      )
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode("new1"      )					,						[1]      )
								self.assertEqual(tokenizer.decode([1]      )					,						"new1"      )

'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case				:   int     =				{
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Optional[int]     =				["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)