





"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:					Dict	      =     {
    '''configuration_xmod''': [
        '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XmodConfig''',
        '''XmodOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					Tuple	      =     [
		    '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XmodForCausalLM''',
		    '''XmodForMaskedLM''',
		    '''XmodForMultipleChoice''',
		    '''XmodForQuestionAnswering''',
		    '''XmodForSequenceClassification''',
		    '''XmodForTokenClassification''',
		    '''XmodModel''',
		    '''XmodPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xmod import (
				    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XmodForCausalLM,
				    XmodForMaskedLM,
				    XmodForMultipleChoice,
				    XmodForQuestionAnswering,
				    XmodForSequenceClassification,
				    XmodForTokenClassification,
				    XmodModel,
				    XmodPreTrainedModel,
				)

else:
		import sys

		a		:					str	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









"""simple docstring"""




import unittest

from transformers import DonutProcessor


a		:					Optional[int]	      =     '''naver-clova-ix/donut-base'''


class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					Dict:
								a :						Optional[Any]      =							DonutProcessor.from_pretrained(lowerCAmelCase__ )






							def        __a  (  self )      ->					List[Any]:
								a :						Optional[Any]      =							{
								    "name": "John Doe",
								    "age": "99",
								    "city": "Atlanta",
								    "state": "GA",
								    "zip": "30301",
								    "phone": "123-4567",
								    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
								}

								a :						Tuple      =							(
								    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
								    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
								    "<s_nicknames><s_nickname>Johnny</s_nickname>"
								    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
								)
								a :						List[Any]      =							self.processor.tokenajson(lowerCAmelCase__ )

								self.assertDictEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )


