
import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin




@require_jieba
class 				lowerCamelCase__							(       lowerCAmelCase__  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  CpmAntTokenizer
	_A			=  False







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				str:
								super().setUp()

								__UpperCamelCase			:     Dict         =      [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								__UpperCamelCase			:     str         =      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]		)		)







	@tooslow
	def      _lowerCamelCase       (      self	:Any		)	->				Tuple:
								__UpperCamelCase			:     List[Any]         =      CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"		)
								__UpperCamelCase			:     Optional[int]         =      "今天天气真好！"
								__UpperCamelCase			:     Optional[int]         =      ["今天", "天气", "真", "好", "！"]
								__UpperCamelCase			:     str         =      tokenizer.tokenize(_lowerCamelCase		)
								self.assertListEqual(_lowerCamelCase  ,     _lowerCamelCase		)
								__UpperCamelCase			:     str         =      "今天天气真好！"
								__UpperCamelCase			:     str         =      [tokenizer.bos_token] + tokens

								__UpperCamelCase			:     Any         =      [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase		)  ,     _lowerCamelCase		)

								__UpperCamelCase			:     List[Any]         =      tokenizer.decode(_lowerCamelCase		)
								self.assertEqual(_lowerCamelCase  ,     _lowerCamelCase		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase						:      List[Any]									= {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Dict									= [
		    'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SwinForImageClassification',
		    'SwinForMaskedImageModeling',
		    'SwinModel',
		    'SwinPreTrainedModel',
		    'SwinBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Optional[int]									= [
		    'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFSwinForImageClassification',
		    'TFSwinForMaskedImageModeling',
		    'TFSwinModel',
		    'TFSwinPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swin import (
				    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinBackbone,
				    SwinForImageClassification,
				    SwinForMaskedImageModeling,
				    SwinModel,
				    SwinPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				    TFSwinPreTrainedModel,
				)

else:
		import sys

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