






"""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 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				TransfoXLTokenizer
				UpperCAmelCase__        =				False
				UpperCAmelCase__        =				False







				def     _snake_case					(							self							:						Dict	)				->					Union[str, Any]:
										super().setUp()

										__magic_name__            =     [
										    "<unk>",
										    "[CLS]",
										    "[SEP]",
										    "want",
										    "unwanted",
										    "wa",
										    "un",
										    "running",
										    ",",
										    "low",
										    "l",
										]
										__magic_name__            =     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     _snake_case					(							self							:						Optional[Any]						,    **__lowerCamelCase							:						int	)				->					List[Any]:
										__magic_name__            =     True
										return TransfoXLTokenizer.from_pretrained(self.tmpdirname						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						List[Any]	)				->					int:
										__magic_name__            =     "<unk> UNwanted , running"
										__magic_name__            =     "<unk> unwanted, running"
										return input_text, output_text







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[int]:
										__magic_name__            =     TransfoXLTokenizer(vocab_file=self.vocab_file						,    lower_case=__lowerCamelCase	)

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

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







				def     _snake_case					(							self							:						Optional[int]	)				->					int:
										__magic_name__            =     TransfoXLTokenizer(lower_case=__lowerCamelCase	)

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







				def     _snake_case					(							self							:						Optional[int]	)				->					Optional[Any]:
										__magic_name__            =     TransfoXLTokenizer(lower_case=__lowerCamelCase	)

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







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

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

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







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Any:
										__magic_name__            =     self.get_tokenizer()
										__magic_name__            =     len(__lowerCamelCase	)

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

										# Check that moved token is not copied (duplicate)
										self.assertEqual(len(__lowerCamelCase	)						,    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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase    			=       {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

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



