






"""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        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Dict             =			XLMTokenizer
			_UpperCAmelCase		:Dict             =			False


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase_	:							List[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>",
										]
										lowerCamelCase_	:							Tuple									=dict(zip(snake_case__       ,	range(len(snake_case__ ) ) ) )
										lowerCamelCase_	:							Optional[int]									=["l o 123", "lo w 1456", "e r</w> 1789", ""]

										lowerCamelCase_	:							Union[str, Any]									=os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["vocab_file"] )
										lowerCamelCase_	:							Union[str, 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      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  Union[str, Any] ):
										lowerCamelCase_	:							Optional[Any]									="lower newer"
										lowerCamelCase_	:							Any									="lower newer"
										return input_text, output_text


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							List[Any]									=XLMTokenizer(self.vocab_file       ,	self.merges_file )

										lowerCamelCase_	:							Tuple									="lower"
										lowerCamelCase_	:							List[str]									=["low", "er</w>"]
										lowerCamelCase_	:							Any									=tokenizer.tokenize(snake_case__ )
										self.assertListEqual(snake_case__       ,	snake_case__ )

										lowerCamelCase_	:							Dict									=tokens + ["<unk>"]
										lowerCamelCase_	:							Optional[Any]									=[14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )       ,	snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Any									=XLMTokenizer.from_pretrained("xlm-mlm-en-2048" )

										lowerCamelCase_	:							Optional[int]									=tokenizer.encode("sequence builders"       ,	add_special_tokens=snake_case__ )
										lowerCamelCase_	:							str									=tokenizer.encode("multi-sequence build"       ,	add_special_tokens=snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=tokenizer.build_inputs_with_special_tokens(snake_case__ )
										lowerCamelCase_	:							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_torch_available


A__	:    List[Any]			 = {
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    List[str]			 = [
						    'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'LiltForQuestionAnswering',
						    'LiltForSequenceClassification',
						    'LiltForTokenClassification',
						    'LiltModel',
						    'LiltPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lilt import (
												    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LiltForQuestionAnswering,
												    LiltForSequenceClassification,
												    LiltForTokenClassification,
												    LiltModel,
												    LiltPreTrainedModel,
												)

else:
						import sys

						A__	:    Optional[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



